diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix
index 4ee0d62cb..49e9b7528 100644
--- a/.devops/nix/package.nix
+++ b/.devops/nix/package.nix
@@ -17,19 +17,18 @@
   rocmPackages,
   vulkan-headers,
   vulkan-loader,
-  clblast,
+  curl,
   useBlas ? builtins.all (x: !x) [
     useCuda
     useMetalKit
-    useOpenCL
     useRocm
     useVulkan
   ] && blas.meta.available,
   useCuda ? config.cudaSupport,
-  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
+  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin,
   useMpi ? false, # Increases the runtime closure size by ~700M
-  useOpenCL ? false,
   useRocm ? config.rocmSupport,
+  enableCurl ? true,
   useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
 
@@ -56,7 +55,6 @@ let
     ++ lib.optionals useCuda [ "CUDA" ]
     ++ lib.optionals useMetalKit [ "MetalKit" ]
     ++ lib.optionals useMpi [ "MPI" ]
-    ++ lib.optionals useOpenCL [ "OpenCL" ]
     ++ lib.optionals useRocm [ "ROCm" ]
     ++ lib.optionals useVulkan [ "Vulkan" ];
 
@@ -198,19 +196,19 @@ effectiveStdenv.mkDerivation (
       optionals effectiveStdenv.isDarwin darwinBuildInputs
       ++ optionals useCuda cudaBuildInputs
       ++ optionals useMpi [ mpi ]
-      ++ optionals useOpenCL [ clblast ]
       ++ optionals useRocm rocmBuildInputs
       ++ optionals useBlas [ blas ]
-      ++ optionals useVulkan vulkanBuildInputs;
+      ++ optionals useVulkan vulkanBuildInputs
+      ++ optionals enableCurl [ curl ];
 
     cmakeFlags =
       [
         (cmakeBool "LLAMA_BUILD_SERVER" true)
         (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
         (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
+        (cmakeBool "LLAMA_CURL" enableCurl)
         (cmakeBool "GGML_NATIVE" false)
         (cmakeBool "GGML_BLAS" useBlas)
-        (cmakeBool "GGML_CLBLAST" useOpenCL)
         (cmakeBool "GGML_CUDA" useCuda)
         (cmakeBool "GGML_HIPBLAS" useRocm)
         (cmakeBool "GGML_METAL" useMetalKit)
@@ -254,7 +252,6 @@ effectiveStdenv.mkDerivation (
         useCuda
         useMetalKit
         useMpi
-        useOpenCL
         useRocm
         useVulkan
         ;
@@ -281,7 +278,7 @@ effectiveStdenv.mkDerivation (
       # Configurations we don't want even the CI to evaluate. Results in the
       # "unsupported platform" messages. This is mostly a no-op, because
       # cudaPackages would've refused to evaluate anyway.
-      badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin;
+      badPlatforms = optionals useCuda lib.platforms.darwin;
 
       # Configurations that are known to result in build failures. Can be
       # overridden by importing Nixpkgs with `allowBroken = true`.
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
index c88134dbb..eb8c4b472 100644
--- a/.github/ISSUE_TEMPLATE/config.yml
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -9,5 +9,3 @@ contact_links:
   - name: Want to contribute?
     url: https://github.com/ggerganov/llama.cpp/wiki/contribute
     about: Head to the contribution guide page of the wiki for areas you can help with
-
-
diff --git a/.gitignore b/.gitignore
index 177e6a8db..6125e350c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -98,13 +98,14 @@ examples/server/*.mjs.hpp
 
 # Python
 
-__pycache__
-.venv
-/Pipfile
-dist
-poetry.lock
+/.venv
+__pycache__/
+*/poetry.lock
 poetry.toml
 
+# Nix
+/result
+
 # Test binaries
 /tests/test-backend-ops
 /tests/test-double-float
diff --git a/CMakeLists.txt b/CMakeLists.txt
index e3a0cc369..4f6cd6872 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -42,6 +42,10 @@ endif()
 
 option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
 
+if (WIN32)
+    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
+endif()
+
 #
 # option list
 #
@@ -152,7 +156,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
         DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama)
 
 install(
-    FILES convert-hf-to-gguf.py
+    FILES convert_hf_to_gguf.py
     PERMISSIONS
         OWNER_READ
         OWNER_WRITE
diff --git a/CMakePresets.json b/CMakePresets.json
index d69bc0344..bdad38952 100644
--- a/CMakePresets.json
+++ b/CMakePresets.json
@@ -19,6 +19,7 @@
         "cacheVariables": {
             "CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
             "CMAKE_CXX_COMPILER": "icx",
+            "CMAKE_C_COMPILER": "cl",
             "GGML_SYCL": "ON",
             "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
         }
diff --git a/Makefile b/Makefile
index 8ae4f1dc4..2730d8b60 100644
--- a/Makefile
+++ b/Makefile
@@ -62,6 +62,11 @@ TEST_TARGETS = \
 	tests/test-tokenizer-1-bpe \
 	tests/test-tokenizer-1-spm
 
+# Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned
+LEGACY_TARGETS = main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
+	simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \
+	retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm
+
 # Deprecation aliases
 ifdef LLAMA_CUBLAS
 $(error LLAMA_CUBLAS is removed. Use GGML_CUDA instead.)
@@ -1086,6 +1091,7 @@ clean:
 	rm -vrf ggml/src/ggml-cuda/template-instances/*.o
 	rm -rvf $(BUILD_TARGETS)
 	rm -rvf $(TEST_TARGETS)
+	rm -rvf $(LEGACY_TARGETS)
 	find examples pocs -type f -name "*.o" -delete
 
 #
diff --git a/README.md b/README.md
index 99b16f6e2..3569b2bbb 100644
--- a/README.md
+++ b/README.md
@@ -108,6 +108,7 @@ Typically finetunes of the base models below are supported as well.
 - [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
+- [X] [BERT](https://github.com/ggerganov/llama.cpp/pull/5423)
 - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
 - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
 - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
@@ -217,6 +218,11 @@ Unless otherwise noted these projects are open-source with permissive licensing:
 **Tools:**
 
 - [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
+- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
+
+**Infrastructure:**
+
+- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
 
 ---
 
diff --git a/ci/run.sh b/ci/run.sh
index e0cedb24f..067ac405b 100755
--- a/ci/run.sh
+++ b/ci/run.sh
@@ -688,7 +688,7 @@ function gg_run_embd_bge_small {
     (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
+    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
 
     model_f16="${path_models}/ggml-model-f16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"
diff --git a/common/common.cpp b/common/common.cpp
index 8b72e8f0e..e6ed77b68 100644
--- a/common/common.cpp
+++ b/common/common.cpp
@@ -757,7 +757,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
         params.cache_type_v = argv[++i];
         return true;
     }
-    if (arg == "--multiline-input") {
+    if (arg == "-mli" || arg == "--multiline-input") {
         params.multiline_input = true;
         return true;
     }
@@ -2070,7 +2070,24 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
     if (params.warmup) {
         LOG("warming up the model with an empty run\n");
 
-        std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
+        std::vector<llama_token> tmp;
+        llama_token bos = llama_token_bos(model);
+        llama_token eos = llama_token_eos(model);
+        // some models (e.g. T5) don't have a BOS token
+        if (bos != -1) {
+            tmp.push_back(bos);
+        }
+        tmp.push_back(eos);
+
+        if (llama_model_has_encoder(model)) {
+            llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
+            llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+            if (decoder_start_token_id == -1) {
+                decoder_start_token_id = bos;
+            }
+            tmp.clear();
+            tmp.push_back(decoder_start_token_id);
+        }
         llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
         llama_past_clear(lctx);
         llama_synchronize(lctx);
diff --git a/common/common.h b/common/common.h
index 627b7ed85..65c0ef81a 100644
--- a/common/common.h
+++ b/common/common.h
@@ -459,4 +459,3 @@ void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const cha
 void yaml_dump_non_result_info(
     FILE * stream, const gpt_params & params, const llama_context * lctx,
     const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
-
diff --git a/convert-hf-to-gguf.py b/convert_hf_to_gguf.py
similarity index 91%
rename from convert-hf-to-gguf.py
rename to convert_hf_to_gguf.py
index 466aac110..dad6f707d 100755
--- a/convert-hf-to-gguf.py
+++ b/convert_hf_to_gguf.py
@@ -13,7 +13,7 @@ import sys
 from enum import IntEnum
 from pathlib import Path
 from hashlib import sha256
-from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast
+from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
 
 import math
 import numpy as np
@@ -490,6 +490,9 @@ class Model:
         if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
             # ref: https://huggingface.co/LumiOpen/Viking-7B
             res = "viking"
+        if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
+            # ref: https://huggingface.co/core42/jais-13b
+            res = "jais"
 
         if res is None:
             logger.warning("\n")
@@ -576,7 +579,19 @@ class Model:
         special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
         special_vocab.add_to_gguf(self.gguf_writer)
 
-    def _set_vocab_sentencepiece(self):
+    def _set_vocab_sentencepiece(self, add_to_gguf=True):
+        tokens, scores, toktypes = self._create_vocab_sentencepiece()
+
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+    def _create_vocab_sentencepiece(self):
         from sentencepiece import SentencePieceProcessor
 
         tokenizer_path = self.dir_model / 'tokenizer.model'
@@ -638,14 +653,7 @@ class Model:
                 scores.append(-1000.0)
                 toktypes.append(SentencePieceTokenTypes.UNUSED)
 
-        self.gguf_writer.add_tokenizer_model("llama")
-        self.gguf_writer.add_tokenizer_pre("default")
-        self.gguf_writer.add_token_list(tokens)
-        self.gguf_writer.add_token_scores(scores)
-        self.gguf_writer.add_token_types(toktypes)
-
-        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
-        special_vocab.add_to_gguf(self.gguf_writer)
+        return tokens, scores, toktypes
 
     def _set_vocab_llama_hf(self):
         vocab = gguf.LlamaHfVocab(self.dir_model)
@@ -669,6 +677,51 @@ class Model:
         special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
         special_vocab.add_to_gguf(self.gguf_writer)
 
+    def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int):
+        tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf"
+        logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
+        vocab_reader = gguf.GGUFReader(tokenizer_path, "r")
+
+        default_pre = "mpt" if model_name == "gpt-neox" else "default"
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL)
+        assert field  # tokenizer model
+        self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8"))
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE)
+        self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre)
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST)
+        assert field  # token list
+        self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
+
+        if model_name == "llama-spm":
+            field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES)
+            assert field  # token scores
+            self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
+        field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
+        assert field  # token types
+        self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
+
+        if model_name != "llama-spm":
+            field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES)
+            assert field  # token merges
+            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
+
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None:
+            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None:
+            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None:
+            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None:
+            self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None:
+            self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0])
+        if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None:
+            self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0])
+
 
 @Model.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(Model):
@@ -1934,7 +1987,7 @@ class Phi3MiniModel(Model):
         if len(rope_scaling_type) == 0:
             raise KeyError('Missing the required key rope_scaling.type')
 
-        if rope_scaling_type == 'su':
+        if rope_scaling_type == 'su' or rope_scaling_type == 'longrope':
             attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0
         elif rope_scaling_type == 'yarn':
             attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0
@@ -2308,6 +2361,8 @@ class GemmaModel(Model):
         special_vocab._set_special_token("eot",    107)
         special_vocab.add_to_gguf(self.gguf_writer)
 
+        self.gguf_writer.add_add_space_prefix(False)
+
     def set_gguf_parameters(self):
         hparams = self.hparams
         block_count = hparams["num_hidden_layers"]
@@ -2345,7 +2400,20 @@ class Gemma2Model(Model):
     model_arch = gguf.MODEL_ARCH.GEMMA2
 
     def set_vocab(self):
-        self._set_vocab_llama_hf()
+        tokens, scores, toktypes = self._create_vocab_sentencepiece()
+        # hack: This is required so that we can properly use start/end-of-turn for chat template
+        for i in range(108):
+            # including <unusedX>, <start_of_turn>, <end_of_turn>
+            toktypes[i] = SentencePieceTokenTypes.CONTROL
+        self.gguf_writer.add_tokenizer_model("llama")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
         self.gguf_writer.add_add_space_prefix(False)
 
     def set_gguf_parameters(self):
@@ -2369,6 +2437,12 @@ class Gemma2Model(Model):
         self.gguf_writer.add_final_logit_softcapping(
             self.hparams["final_logit_softcapping"]
         )
+        self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
+
+        # sanity check
+        attn_scalar = self.hparams["query_pre_attn_scalar"]
+        if attn_scalar != hparams["hidden_size"] / hparams["num_attention_heads"]:
+            raise ValueError("query_pre_attn_scalar must be equal to n_embd / n_head")
 
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unusem
@@ -2410,39 +2484,7 @@ class MambaModel(Model):
             self._set_vocab_sentencepiece()
         else:
             # Use the GPT-NeoX tokenizer when no tokenizer files are present
-            tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
-            logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
-            neox_reader = gguf.GGUFReader(tokenizer_path, "r")
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
-            self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8") if field else "gpt2")
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE)
-            self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else "mpt")
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
-            assert field
-            self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
-            assert field
-            self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
-            assert field
-            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
-            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0] if field else 1)
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
-            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0] if field else 0)
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
-            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0] if field else 0)
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)
-            self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0] if field else 0)
+            self._set_vocab_builtin("gpt-neox", vocab_size)
 
     def set_gguf_parameters(self):
         d_model = self.find_hparam(["hidden_size",       "d_model"])
@@ -2723,6 +2765,82 @@ class JinaBertV2Model(BertModel):
         self.gguf_writer.add_add_eos_token(True)
 
 
+@Model.register("OpenELMForCausalLM")
+class OpenELMModel(Model):
+    model_arch = gguf.MODEL_ARCH.OPENELM
+
+    @staticmethod
+    def _make_divisible(v: float | int, divisor: int) -> int:
+        # ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38
+        new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
+        # Make sure that round down does not go down by more than 10%.
+        if new_v < 0.9 * v:
+            new_v += divisor
+        return new_v
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        ffn_multipliers: list[float] = self.hparams["ffn_multipliers"]
+        ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"]
+        self._n_embd: int = self.hparams["model_dim"]
+        self._num_kv_heads: list[int] = self.hparams["num_kv_heads"]
+        self._num_query_heads: list[int] = self.hparams["num_query_heads"]
+        self._ffn_dims: list[int] = [
+            OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor)
+            for multiplier in ffn_multipliers
+        ]
+        assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
+        assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int)
+
+    # Uses the tokenizer from meta-llama/Llama-2-7b-hf
+    def set_vocab(self):
+        try:
+            self._set_vocab_sentencepiece()
+        except FileNotFoundError:
+            self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"])
+
+    def set_gguf_parameters(self):
+        n_embd = self._n_embd
+        head_dim = self.hparams["head_dim"]
+        rot_pct = 1.0
+        assert self.block_count == len(self._num_kv_heads)
+        assert self.block_count == len(self._num_query_heads)
+        assert self.block_count == len(self._ffn_dims)
+
+        self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
+        self.gguf_writer.add_block_count(self.block_count)
+        self.gguf_writer.add_context_length(self.hparams["max_context_length"])
+        self.gguf_writer.add_embedding_length(n_embd)
+        self.gguf_writer.add_feed_forward_length(self._ffn_dims)
+        self.gguf_writer.add_head_count(self._num_query_heads)
+        self.gguf_writer.add_head_count_kv(self._num_kv_heads)
+        self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"])
+        # https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30
+        self.gguf_writer.add_layer_norm_rms_eps(1e-6)
+        self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim))
+        self.gguf_writer.add_key_length(head_dim)
+        self.gguf_writer.add_value_length(head_dim)
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
+        if "n_layers" in keys:
+            return self.hparams["num_transformer_layers"]
+
+        return super().find_hparam(keys, optional)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+
+        # split ff
+        if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight":
+            ff_dim = self._ffn_dims[bid]
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])
+            return
+
+        yield (self.map_tensor_name(name), data_torch)
+
+
 @Model.register("ArcticForCausalLM")
 class ArcticModel(Model):
     model_arch = gguf.MODEL_ARCH.ARCTIC
@@ -2953,11 +3071,17 @@ class DeepseekV2Model(Model):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
-@Model.register("T5ForConditionalGeneration")
 @Model.register("T5WithLMHeadModel")
+@Model.register("T5ForConditionalGeneration")
+@Model.register("MT5ForConditionalGeneration")
+@Model.register("UMT5ForConditionalGeneration")
 class T5Model(Model):
     model_arch = gguf.MODEL_ARCH.T5
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.shared_token_embeddings_found = False
+
     def set_vocab(self):
         # to avoid TypeError: Descriptors cannot be created directly
         # exception when importing sentencepiece_model_pb2
@@ -2965,17 +3089,29 @@ class T5Model(Model):
         from sentencepiece import SentencePieceProcessor
         from sentencepiece import sentencepiece_model_pb2 as model
 
-        tokenizer_path = self.dir_model / 'spiece.model'
+        tokenizer_path = self.dir_model / 'tokenizer.model'
+
+        # many older models use spiece.model tokenizer model filename
+        if not tokenizer_path.is_file():
+            tokenizer_path = self.dir_model / 'spiece.model'
 
         if not tokenizer_path.is_file():
             raise FileNotFoundError(f"File not found: {tokenizer_path}")
 
         sentencepiece_model = model.ModelProto()
         sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
+
+        # some models like Pile-T5 family use BPE tokenizer instead of Unigram
+        if sentencepiece_model.trainer_spec.model_type == 2: # BPE
+            # assure the tokenizer model file name is correct
+            assert tokenizer_path.name == 'tokenizer.model'
+            return self._set_vocab_sentencepiece()
+        else:
+            assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
+
         add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
         remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
         precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
-        assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
 
         tokenizer = SentencePieceProcessor()
         tokenizer.LoadFromFile(str(tokenizer_path))
@@ -3045,7 +3181,10 @@ class T5Model(Model):
 
     def set_gguf_parameters(self):
         self.gguf_writer.add_name("T5")
-        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
+            logger.warning("Couldn't find context length in config.json, assuming default value of 512")
+            n_ctx = 512
+        self.gguf_writer.add_context_length(n_ctx)
         self.gguf_writer.add_embedding_length(self.hparams["d_model"])
         self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
         self.gguf_writer.add_block_count(self.hparams["num_layers"])
@@ -3061,16 +3200,111 @@ class T5Model(Model):
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         del bid  # unused
 
-        # Sometimes T5 and Flan-T5 based models contain "encoder.embed_tokens.weight" tensor or
-        # "decoder.embed_tokens.weight" tensors that are duplicates of "shared.weight" tensor
-        # To prevent errors caused by an unnecessary unmapped tensor, skip both of them and use only "shared.weight".
-        if name == "decoder.embed_tokens.weight" or name == "encoder.embed_tokens.weight":
-            logger.debug(f"Skipping tensor {name!r} in safetensors so that convert can end normally.")
-            return []
+        # T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
+        # "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
+        # in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
+        # and decoder and ignore the remaining ones.
+        if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
+            if not self.shared_token_embeddings_found:
+                name = "shared.weight"
+                self.shared_token_embeddings_found = True
+            else:
+                logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
+                return []
 
         return [(self.map_tensor_name(name), data_torch)]
 
 
+@Model.register("JAISLMHeadModel")
+class JaisModel(Model):
+    model_arch = gguf.MODEL_ARCH.JAIS
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # SwigLU activation
+        assert self.hparams["activation_function"] == "swiglu"
+        # ALiBi position embedding
+        assert self.hparams["position_embedding_type"] == "alibi"
+
+        # Embeddings scale
+        self.embeddings_scale = 1.0
+        # note: For some JAIS flavors, output is tied to (same as) wte in original model
+        self.output_is_wte = False
+        if 'mup_embeddings_scale' in self.hparams:
+            self.output_is_wte = True   # Hack (?)
+            self.embeddings_scale = self.hparams['mup_embeddings_scale']
+        elif 'embeddings_scale' in self.hparams:
+            self.embeddings_scale = self.hparams['embeddings_scale']
+        else:
+            assert False
+
+        self.width_scale = 1.0
+        if 'mup_output_alpha' in self.hparams:
+            assert 'mup_width_scale' in self.hparams
+            self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
+        elif 'width_scale' in self.hparams:
+            self.width_scale = self.hparams['width_scale']
+        else:
+            assert False
+
+        self.max_alibi_bias = 8.0
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_name(self.dir_model.name)
+        self.gguf_writer.add_block_count(self.hparams["n_layer"])
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
+        self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
+        self.gguf_writer.add_head_count(self.hparams["n_head"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # unused
+
+        tensors: list[tuple[str, Tensor]] = []
+
+        # we don't need these
+        if name.endswith((".attn.bias")):
+            return tensors
+
+        if name.endswith(("relative_pe.slopes")):
+            # Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)
+            # Some other models has max_alibi_bias spelled out explicitly in the hyperparams,
+            # but Jais's PyTorch model simply precalculates the slope values and places them
+            # in relative_pes.slopes
+            n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
+            first_val = float(data_torch._data[0])
+            self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
+
+            return tensors
+
+        if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
+            data_torch = data_torch.transpose(1, 0)
+
+        new_name = self.map_tensor_name(name)
+
+        if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
+            tensors.append((new_name, data_torch * self.embeddings_scale))
+            if self.output_is_wte:
+                tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale))
+        elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
+            assert not self.output_is_wte
+            tensors.append((new_name, data_torch * self.width_scale))
+        else:
+            tensors.append((new_name, data_torch))
+
+        return tensors
+
+    def write_tensors(self):
+        super().write_tensors()
+        self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
+
+
 ###### CONVERSION LOGIC ######
 
 
@@ -3226,7 +3460,8 @@ def main() -> None:
         "auto": gguf.LlamaFileType.GUESSED,
     }
 
-    if args.use_temp_file and (args.split_max_tensors > 0 or args.split_max_size != "0"):
+    is_split = args.split_max_tensors > 0 or args.split_max_size != "0"
+    if args.use_temp_file and is_split:
         logger.error("Error: Cannot use temp file when splitting")
         sys.exit(1)
 
@@ -3263,11 +3498,12 @@ def main() -> None:
         if args.vocab_only:
             logger.info("Exporting model vocab...")
             model_instance.write_vocab()
-            logger.info("Model vocab successfully exported.")
+            logger.info(f"Model vocab successfully exported to {model_instance.fname_out}")
         else:
             logger.info("Exporting model...")
             model_instance.write()
-            logger.info("Model successfully exported.")
+            out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out
+            logger.info(f"Model successfully exported to {out_path}")
 
 
 if __name__ == '__main__':
diff --git a/convert-hf-to-gguf-update.py b/convert_hf_to_gguf_update.py
similarity index 92%
rename from convert-hf-to-gguf-update.py
rename to convert_hf_to_gguf_update.py
index 2758214fa..21a306255 100755
--- a/convert-hf-to-gguf-update.py
+++ b/convert_hf_to_gguf_update.py
@@ -45,6 +45,7 @@ class TOKENIZER_TYPE(IntEnum):
     SPM = auto()
     BPE = auto()
     WPM = auto()
+    UGM = auto()
 
 
 # TODO: this string has to exercise as much pre-tokenizer functionality as possible
@@ -86,6 +87,10 @@ models = [
     {"name": "poro-chat",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
     {"name": "jina-v2-code",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
     {"name": "viking",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
+    {"name": "gemma",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
+    {"name": "gemma-2",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
+    {"name": "jais",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
+    {"name": "t5",             "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
 ]
 
 
@@ -107,9 +112,13 @@ def download_model(model):
     os.makedirs(f"models/tokenizers/{name}", exist_ok=True)
 
     files = ["config.json", "tokenizer.json", "tokenizer_config.json"]
+
     if tokt == TOKENIZER_TYPE.SPM:
         files.append("tokenizer.model")
 
+    if tokt == TOKENIZER_TYPE.UGM:
+        files.append("spiece.model")
+
     for file in files:
         save_path = f"models/tokenizers/{name}/{file}"
         if os.path.isfile(save_path):
@@ -132,7 +141,7 @@ for model in models:
     name = model["name"]
     tokt = model["tokt"]
 
-    if tokt == TOKENIZER_TYPE.SPM:
+    if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
         continue
 
     # Skip if the tokenizer folder does not exist or there are other download issues previously
@@ -142,7 +151,10 @@ for model in models:
 
     # create the tokenizer
     try:
-        tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+        if name == "t5":
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
     except OSError as e:
         logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
         continue  # Skip to the next model if the tokenizer can't be loaded
@@ -263,6 +275,7 @@ tests = [
     "\n =",
     "' era",
     "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
+    "!!!!!!",
     "3",
     "33",
     "333",
@@ -272,7 +285,8 @@ tests = [
     "3333333",
     "33333333",
     "333333333",
-    # "Cửa Việt", # llama-bpe fails on this
+    "Cửa Việt", # llama-bpe fails on this
+    " discards",
     chktxt,
 ]
 
@@ -300,7 +314,10 @@ for model in models:
 
     # create the tokenizer
     try:
-        tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
+        if name == "t5":
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
+        else:
+            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
     except OSError as e:
         logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
         continue  # Skip this model and continue with the next one in the loop
diff --git a/convert-llama-ggml-to-gguf.py b/convert_llama_ggml_to_gguf.py
similarity index 100%
rename from convert-llama-ggml-to-gguf.py
rename to convert_llama_ggml_to_gguf.py
diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp
index 888cf9e8e..3e4ea9e78 100644
--- a/examples/batched/batched.cpp
+++ b/examples/batched/batched.cpp
@@ -93,14 +93,34 @@ int main(int argc, char ** argv) {
 
     // create a llama_batch
     // we use this object to submit token data for decoding
-    llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1);
+    llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel);
+
+    std::vector<llama_seq_id> seq_ids(n_parallel, 0);
+    for (int32_t i = 0; i < n_parallel; ++i) {
+        seq_ids[i] = i;
+    }
 
     // evaluate the initial prompt
     for (size_t i = 0; i < tokens_list.size(); ++i) {
-        llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
+        llama_batch_add(batch, tokens_list[i], i, seq_ids, false);
     }
     GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
 
+    if (llama_model_has_encoder(model)) {
+        if (llama_encode(ctx, batch)) {
+            LOG_TEE("%s : failed to eval\n", __func__);
+            return 1;
+        }
+
+        llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+        if (decoder_start_token_id == -1) {
+            decoder_start_token_id = llama_token_bos(model);
+        }
+
+        llama_batch_clear(batch);
+        llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
+    }
+
     // llama_decode will output logits only for the last token of the prompt
     batch.logits[batch.n_tokens - 1] = true;
 
@@ -109,11 +129,11 @@ int main(int argc, char ** argv) {
         return 1;
     }
 
-    // assign the system KV cache to all parallel sequences
-    // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
-    for (int32_t i = 1; i < n_parallel; ++i) {
-        llama_past_seq_cp(ctx, 0, i, -1, -1);
-    }
+    //// assign the system KV cache to all parallel sequences
+    //// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
+    //for (int32_t i = 1; i < n_parallel; ++i) {
+    //    llama_past_seq_cp(ctx, 0, i, -1, -1);
+    //}
 
     if (n_parallel > 1) {
         LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);
diff --git a/examples/embedding/README.md b/examples/embedding/README.md
index 86df18958..e3705b454 100644
--- a/examples/embedding/README.md
+++ b/examples/embedding/README.md
@@ -58,4 +58,3 @@ The above command will output space-separated float values.
 ```powershell
 embedding.exe -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --embd-separator '<#sep#>' --embd-normalize 2  --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null
 ```
-
diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp
index 9a0ffc83b..d638473e4 100644
--- a/examples/infill/infill.cpp
+++ b/examples/infill/infill.cpp
@@ -659,4 +659,3 @@ int main(int argc, char ** argv) {
 
     return 0;
 }
-
diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt
index 17cb4d5e5..21149d6fe 100644
--- a/examples/llava/requirements.txt
+++ b/examples/llava/requirements.txt
@@ -1,3 +1,3 @@
 -r ../../requirements/requirements-convert-legacy-llama.txt
 pillow~=10.2.0
-torch~=2.1.1
+torch~=2.2.1
diff --git a/examples/lookup/README.md b/examples/lookup/README.md
index 5bfb0de93..71c345c03 100644
--- a/examples/lookup/README.md
+++ b/examples/lookup/README.md
@@ -10,4 +10,3 @@ More info:
 
 https://github.com/ggerganov/llama.cpp/pull/4484
 https://github.com/ggerganov/llama.cpp/issues/4226
-
diff --git a/examples/main-cmake-pkg/.gitignore b/examples/main-cmake-pkg/.gitignore
index e32c11c7f..67c01d64c 100644
--- a/examples/main-cmake-pkg/.gitignore
+++ b/examples/main-cmake-pkg/.gitignore
@@ -48,4 +48,3 @@
 build*/
 out/
 tmp/
-
diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt
index a97ded365..3b38db292 100644
--- a/examples/main-cmake-pkg/CMakeLists.txt
+++ b/examples/main-cmake-pkg/CMakeLists.txt
@@ -30,4 +30,3 @@ target_include_directories(${TARGET} PRIVATE ${_common_path})
 install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
-
diff --git a/examples/main/main.cpp b/examples/main/main.cpp
index 19d6e65c3..fb1963fde 100644
--- a/examples/main/main.cpp
+++ b/examples/main/main.cpp
@@ -255,7 +255,9 @@ int main(int argc, char ** argv) {
     }
 
     const bool add_bos = llama_should_add_bos_token(model);
-    GGML_ASSERT(llama_add_eos_token(model) != 1);
+    if (!llama_model_has_encoder(model)) {
+        GGML_ASSERT(llama_add_eos_token(model) != 1);
+    }
     LOG("add_bos: %d\n", add_bos);
 
     std::vector<llama_token> embd_inp;
@@ -520,6 +522,24 @@ int main(int argc, char ** argv) {
         exit(1);
     }
 
+    if (llama_model_has_encoder(model)) {
+        int enc_input_size = embd_inp.size();
+        llama_token * enc_input_buf = embd_inp.data();
+
+        if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) {
+            LOG_TEE("%s : failed to eval\n", __func__);
+            return 1;
+        }
+
+        llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
+        if (decoder_start_token_id == -1) {
+            decoder_start_token_id = llama_token_bos(model);
+        }
+
+        embd_inp.clear();
+        embd_inp.push_back(decoder_start_token_id);
+    }
+
     while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
         // predict
         if (!embd.empty()) {
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp
index 2d252c593..454296894 100644
--- a/examples/perplexity/perplexity.cpp
+++ b/examples/perplexity/perplexity.cpp
@@ -1991,6 +1991,12 @@ int main(int argc, char ** argv) {
         params.n_batch = std::min(params.n_batch, n_kv);
     } else {
         params.n_batch = std::min(params.n_batch, params.n_ctx);
+        if (params.kl_divergence) {
+            params.n_parallel = 1;
+        } else {
+            // ensure there's at least enough seq_ids for HellaSwag
+            params.n_parallel = std::max(4, params.n_parallel);
+        }
     }
 
     if (params.ppl_stride > 0) {
@@ -2015,9 +2021,6 @@ int main(int argc, char ** argv) {
     llama_model * model;
     llama_context * ctx;
 
-    // ensure there's at least enough seq_ids for HellaSwag
-    params.n_parallel = std::max(4, params.n_parallel);
-
     // load the model and apply lora adapter, if any
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
     if (model == NULL) {
diff --git a/examples/server-embd.py b/examples/server-embd.py
index 118e04271..a9a36a44c 100644
--- a/examples/server-embd.py
+++ b/examples/server-embd.py
@@ -31,4 +31,3 @@ for i in range(n-1):
         embedding2 = np.array(result[j])
         similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
         print(f"Similarity between {i} and {j}: {similarity:.2f}")
-
diff --git a/examples/server/tests/features/passkey.feature b/examples/server/tests/features/passkey.feature
index 1bde7aab8..6a5a84e6a 100644
--- a/examples/server/tests/features/passkey.feature
+++ b/examples/server/tests/features/passkey.feature
@@ -52,4 +52,3 @@ Feature: Passkey / Self-extend with context shift
       #| TheBloke/Llama-2-7B-GGUF        | llama-2-7b.Q2_K.gguf        | 4096        | 3   | 16384 | 512     | 4    | 512    | 500    | 300   | 1234    | 5           | 1234           |
       #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768       | 2   | 16384 | 512     | 4    | 512    | 500    | 100   | 0987    | 5           | 0
       # 987           |
-
diff --git a/examples/server/themes/buttons-top/index.html b/examples/server/themes/buttons-top/index.html
index 6af30d307..8334bcde5 100644
--- a/examples/server/themes/buttons-top/index.html
+++ b/examples/server/themes/buttons-top/index.html
@@ -1054,4 +1054,3 @@
 </body>
 
 </html>
-
diff --git a/examples/server/themes/wild/index.html b/examples/server/themes/wild/index.html
index 772e716cd..8361c5774 100644
--- a/examples/server/themes/wild/index.html
+++ b/examples/server/themes/wild/index.html
@@ -1058,4 +1058,3 @@
 </body>
 
 </html>
-
diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh
index da0e4aaba..111366fb0 100755
--- a/examples/sycl/run-llama2.sh
+++ b/examples/sycl/run-llama2.sh
@@ -34,4 +34,3 @@ fi
 
 #use multiple GPUs with same max compute units
 #ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
-
diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat
index cdae5a528..17dd1ff5c 100644
--- a/examples/sycl/win-build-sycl.bat
+++ b/examples/sycl/win-build-sycl.bat
@@ -31,4 +31,3 @@ exit /B 0
 :ERROR
 echo comomand error: %errorlevel%
 exit /B %errorlevel%
-
diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat
index 1d4d7d2cd..f0385cdf0 100644
--- a/examples/sycl/win-run-llama2.bat
+++ b/examples/sycl/win-run-llama2.bat
@@ -7,5 +7,3 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 
 
 .\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0
-
-
diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp
index 54c9834af..0180c87d8 100644
--- a/examples/tokenize/tokenize.cpp
+++ b/examples/tokenize/tokenize.cpp
@@ -30,6 +30,7 @@ static void print_usage_information(const char * argv0, FILE * stream) {
     fprintf(stream, "    --stdin                              read prompt from standard input.\n");
     fprintf(stream, "    --no-bos                             do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n");
     fprintf(stream, "    --log-disable                        disable logs. Makes stderr quiet when loading the model.\n");
+    fprintf(stream, "    --show-count                         print the total number of tokens.\n");
 }
 
 static void llama_log_callback_null(ggml_log_level level, const char * text, void * user_data) {
@@ -195,6 +196,7 @@ int main(int raw_argc, char ** raw_argv) {
     bool printing_ids = false;
     bool no_bos = false;
     bool disable_logging = false;
+    bool show_token_count = false;
     const char * model_path = NULL;
     const char * prompt_path = NULL;
     const char * prompt_arg = NULL;
@@ -249,6 +251,9 @@ int main(int raw_argc, char ** raw_argv) {
         else if (arg == "--log-disable") {
             disable_logging = true;
         }
+        else if (arg == "--show-count") {
+            show_token_count = true;
+        }
         else {
             fprintf(stderr, "Error: unknown option '%s'\n", argv[iarg].c_str());
             return 1;
@@ -384,6 +389,9 @@ int main(int raw_argc, char ** raw_argv) {
         printf("]\n");
     }
 
+    if (show_token_count) {
+        printf("Total number of tokens: %ld\n", tokens.size());
+    }
     // silence valgrind
     llama_free(ctx);
     llama_free_model(model);
diff --git a/flake.lock b/flake.lock
index 79bb3f63f..973ff4e56 100644
--- a/flake.lock
+++ b/flake.lock
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1718895438,
-        "narHash": "sha256-k3JqJrkdoYwE3fHE6xGDY676AYmyh4U2Zw+0Bwe5DLU=",
+        "lastModified": 1719506693,
+        "narHash": "sha256-C8e9S7RzshSdHB7L+v9I51af1gDM5unhJ2xO1ywxNH8=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "d603719ec6e294f034936c0d0dc06f689d91b6c3",
+        "rev": "b2852eb9365c6de48ffb0dc2c9562591f652242a",
         "type": "github"
       },
       "original": {
diff --git a/ggml/include/ggml-metal.h b/ggml/include/ggml-metal.h
index e7543ae79..6c3226c37 100644
--- a/ggml/include/ggml-metal.h
+++ b/ggml/include/ggml-metal.h
@@ -63,4 +63,3 @@ GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend);
 #ifdef __cplusplus
 }
 #endif
-
diff --git a/ggml/src/CMakeLists.txt b/ggml/src/CMakeLists.txt
index d0f4097d8..08b71d410 100644
--- a/ggml/src/CMakeLists.txt
+++ b/ggml/src/CMakeLists.txt
@@ -486,9 +486,11 @@ if (GGML_SYCL)
     add_compile_options(-I./) #include DPCT
 
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
     if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
         set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
+        add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
+    else()
+        add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
     endif()
 
     file(GLOB   GGML_HEADERS_SYCL "ggml-sycl/*.hpp")
@@ -1166,7 +1168,9 @@ target_link_libraries(ggml PRIVATE Threads::Threads ${GGML_EXTRA_LIBS})
 
 find_library(MATH_LIBRARY m)
 if (MATH_LIBRARY)
-    target_link_libraries(ggml PRIVATE ${MATH_LIBRARY})
+    if (NOT WIN32 OR NOT GGML_SYCL)
+        target_link_libraries(ggml PRIVATE ${MATH_LIBRARY})
+    endif()
 endif()
 
 if (BUILD_SHARED_LIBS)
diff --git a/ggml/src/ggml-common.h b/ggml/src/ggml-common.h
index e8efceb76..c74060cc4 100644
--- a/ggml/src/ggml-common.h
+++ b/ggml/src/ggml-common.h
@@ -106,19 +106,19 @@ typedef sycl::half2 ggml_half2;
 #define QR6_K 2
 
 #define QI2_XXS (QK_K / (4*QR2_XXS))
-#define QR2_XXS 8
+#define QR2_XXS 4
 
 #define QI2_XS (QK_K / (4*QR2_XS))
-#define QR2_XS 8
+#define QR2_XS 4
 
 #define QI2_S (QK_K / (4*QR2_S))
-#define QR2_S 8
+#define QR2_S 4
 
 #define QI3_XXS (QK_K / (4*QR3_XXS))
-#define QR3_XXS 8
+#define QR3_XXS 4
 
 #define QI3_XS (QK_K / (4*QR3_XS))
-#define QR3_XS 8
+#define QR3_XS 4
 
 #define QI1_S (QK_K / (4*QR1_S))
 #define QR1_S 8
@@ -130,10 +130,10 @@ typedef sycl::half2 ggml_half2;
 #define QR4_NL 2
 
 #define QI4_XS (QK_K / (4*QR4_XS))
-#define QR4_XS 8
+#define QR4_XS 2
 
 #define QI3_S (QK_K / (4*QR3_S))
-#define QR3_S 8
+#define QR3_S 4
 
 #endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP
 
diff --git a/ggml/src/ggml-cuda.cu b/ggml/src/ggml-cuda.cu
index 0acfda91d..1c9ccc8a1 100644
--- a/ggml/src/ggml-cuda.cu
+++ b/ggml/src/ggml-cuda.cu
@@ -1882,6 +1882,11 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     bool              use_mul_mat_q =  ggml_is_quantized(src0->type)
         && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
 
+    // if mmvq is available it's a better choice than dmmv:
+#ifndef GGML_CUDA_FORCE_DMMV
+    use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
+#endif // GGML_CUDA_FORCE_DMMV
+
     bool any_gpus_with_slow_fp16 = false;
 
     if (split) {
@@ -1894,22 +1899,15 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
             }
 
             const int cc            = ggml_cuda_info().devices[id].cc;
-            use_mul_mat_vec_q       = use_mul_mat_vec_q       && cc >= MIN_CC_DP4A;
             use_mul_mat_q           = use_mul_mat_q           && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
             any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc);
         }
     } else {
         const int cc            = ggml_cuda_info().devices[ctx.device].cc;
-        use_mul_mat_vec_q       = use_mul_mat_vec_q       && cc >= MIN_CC_DP4A;
         use_mul_mat_q           = use_mul_mat_q           && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
         any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc);
     }
 
-    // if mmvq is available it's a better choice than dmmv:
-#ifndef GGML_CUDA_FORCE_DMMV
-    use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
-#endif // GGML_CUDA_FORCE_DMMV
-
     // debug helpers
     //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
     //printf("      %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
@@ -2713,27 +2711,40 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
         case GGML_OP_MUL_MAT:
         case GGML_OP_MUL_MAT_ID:
             {
-                struct ggml_tensor * a;
-                struct ggml_tensor * b;
+                struct ggml_tensor * a = op->src[0];
                 if (op->op == GGML_OP_MUL_MAT) {
-                    a = op->src[0];
-                    b = op->src[1];
-                } else {
-                    a = op->src[2];
-                    b = op->src[1];
-                }
-                if (a->ne[3] != b->ne[3]) {
-                    return false;
-                }
-                ggml_type a_type = a->type;
-                if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS ||
-                    a_type == GGML_TYPE_IQ1_S   || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S   ||
-                    a_type == GGML_TYPE_IQ1_M   || a_type == GGML_TYPE_IQ2_S  || a_type == GGML_TYPE_IQ4_XS) {
-                    if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
+                    struct ggml_tensor * b = op->src[1];
+                    if (a->ne[3] != b->ne[3]) {
                         return false;
                     }
                 }
-                return true;
+                switch (a->type) {
+                    case GGML_TYPE_F32:
+                    case GGML_TYPE_F16:
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1:
+                    case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q2_K:
+                    case GGML_TYPE_Q3_K:
+                    case GGML_TYPE_Q4_K:
+                    case GGML_TYPE_Q5_K:
+                    case GGML_TYPE_Q6_K:
+                    case GGML_TYPE_Q8_K:
+                    case GGML_TYPE_IQ1_M:
+                    case GGML_TYPE_IQ1_S:
+                    case GGML_TYPE_IQ2_S:
+                    case GGML_TYPE_IQ2_XS:
+                    case GGML_TYPE_IQ2_XXS:
+                    case GGML_TYPE_IQ3_S:
+                    case GGML_TYPE_IQ3_XXS:
+                    case GGML_TYPE_IQ4_NL:
+                    case GGML_TYPE_IQ4_XS:
+                        return true;
+                    default:
+                        return false;
+                }
             } break;
         case GGML_OP_GET_ROWS:
             {
diff --git a/ggml/src/ggml-cuda/common.cuh b/ggml/src/ggml-cuda/common.cuh
index 8d00db6c1..4ff06b871 100644
--- a/ggml/src/ggml-cuda/common.cuh
+++ b/ggml/src/ggml-cuda/common.cuh
@@ -3,6 +3,7 @@
 #include "ggml.h"
 #include "ggml-cuda.h"
 
+#include <cstdint>
 #include <memory>
 
 #if defined(GGML_USE_HIPBLAS)
@@ -226,6 +227,10 @@ typedef float2 dfloat2;
 #define RDNA2
 #endif
 
+#if defined(__gfx1010__) || defined(__gfx1012__)
+#define RDNA1
+#endif
+
 #ifndef __has_builtin
     #define __has_builtin(x) 0
 #endif
@@ -268,30 +273,15 @@ static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigne
     return c;
 }
 
-static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
-#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
-    c = __builtin_amdgcn_sdot4(a, b, c, false);
-#elif defined(RDNA3)
-    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
-#elif defined(__gfx1010__) || defined(__gfx900__)
-    int tmp1;
-    int tmp2;
-    asm("\n \
-        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
-        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
-        v_add3_u32 %0, %1, %2, %0 \n \
-        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
-        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
-        v_add3_u32 %0, %1, %2, %0 \n \
-        "
-        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
-        : "v"(a), "v"(b)
-    );
-#else
-    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
-    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
-    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
-#endif
+static __device__ __forceinline__ unsigned int __vcmpne4(unsigned int a, unsigned int b) {
+    const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
+    const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
+    unsigned int c;
+    uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
+#pragma unroll
+    for (int i = 0; i < 4; ++i) {
+        vc[i] = va[i] == vb[i] ? 0x00 : 0xff;
+    }
     return c;
 }
 
@@ -467,8 +457,48 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half
 }
 #endif // CUDART_VERSION < 12000
 
+static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
+#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
+    c = __builtin_amdgcn_sdot4(a, b, c, false);
+#elif defined(RDNA3)
+    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
+#elif defined(__gfx1010__) || defined(__gfx900__)
+    int tmp1;
+    int tmp2;
+    asm("\n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        "
+        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
+        : "v"(a), "v"(b)
+    );
+#else
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
+#endif
+    return c;
+
+#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+
+#if __CUDA_ARCH__ >= MIN_CC_DP4A
+    return __dp4a(a, b, c);
+#else // __CUDA_ARCH__ >= MIN_CC_DP4A
+    const int8_t * a8 = (const int8_t *) &a;
+    const int8_t * b8 = (const int8_t *) &b;
+    return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3];
+#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
+
+#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+}
+
 // TODO: move to ggml-common.h
-static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
+static constexpr __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
 
 typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);
 
diff --git a/ggml/src/ggml-cuda/cpy.cu b/ggml/src/ggml-cuda/cpy.cu
index 12d741f01..3db57034b 100644
--- a/ggml/src/ggml-cuda/cpy.cu
+++ b/ggml/src/ggml-cuda/cpy.cu
@@ -487,4 +487,3 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
         GGML_ASSERT(false);
     }
 }
-
diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh
index bd7993595..650780fd2 100644
--- a/ggml/src/ggml-cuda/fattn-common.cuh
+++ b/ggml/src/ggml-cuda/fattn-common.cuh
@@ -54,12 +54,11 @@ typedef float (*vec_dot_KQ_f32_t)(
 template<typename T, int D>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
     const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A
 
     const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c;
     GGML_UNUSED(Q_v);
 
-    half sum = 0.0f;
+    T sum = 0.0f;
 
 #pragma unroll
     for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
@@ -72,7 +71,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
         const int v = (get_int_from_uint8(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
         const int u = Q_q8[k_KQ_0/WARP_SIZE];
 
-        const int sumi = __dp4a(v, u, 0);
+        const int sumi = ggml_cuda_dp4a(v, u, 0);
 
 #ifdef FP16_AVAILABLE
         if (std::is_same<T, half>::value) {
@@ -90,19 +89,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
     }
 
     return sum;
-#else
-    GGML_UNUSED(K_c);
-    GGML_UNUSED(Q_v);
-    GGML_UNUSED(Q_q8);
-    GGML_UNUSED(Q_ds_v);
-    NO_DEVICE_CODE;
-#endif  // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 template<typename T, int D>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
     const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A
 
     const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c;
     GGML_UNUSED(Q_v);
@@ -120,7 +111,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
         const int v = (get_int_from_uint8_aligned(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
         const int u = Q_q8[k_KQ_0/WARP_SIZE];
 
-        const int sumi = __dp4a(v, u, 0);
+        const int sumi = ggml_cuda_dp4a(v, u, 0);
 
 #ifdef FP16_AVAILABLE
         if (std::is_same<T, half>::value) {
@@ -142,19 +133,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
     }
 
     return sum;
-#else
-    GGML_UNUSED(K_c);
-    GGML_UNUSED(Q_v);
-    GGML_UNUSED(Q_q8);
-    GGML_UNUSED(Q_ds_v);
-    NO_DEVICE_CODE;
-#endif  // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 template<typename T, int D>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
     const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A
 
     const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c;
     GGML_UNUSED(Q_v);
@@ -179,7 +162,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
 
         const int u = Q_q8[k_KQ_0/WARP_SIZE];
 
-        const int sumi = __dp4a(v, u, 0);
+        const int sumi = ggml_cuda_dp4a(v, u, 0);
 
 #ifdef FP16_AVAILABLE
         if (std::is_same<T, half>::value) {
@@ -197,19 +180,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
     }
 
     return sum;
-#else
-    GGML_UNUSED(K_c);
-    GGML_UNUSED(Q_v);
-    GGML_UNUSED(Q_q8);
-    GGML_UNUSED(Q_ds_v);
-    NO_DEVICE_CODE;
-#endif  // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 template<typename T, int D>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
     const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A
 
     const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c;
     GGML_UNUSED(Q_v);
@@ -234,7 +209,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
 
         const int u = Q_q8[k_KQ_0/WARP_SIZE];
 
-        const int sumi = __dp4a(v, u, 0);
+        const int sumi = ggml_cuda_dp4a(v, u, 0);
 
 #ifdef FP16_AVAILABLE
         if (std::is_same<T, half>::value) {
@@ -256,19 +231,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
     }
 
     return sum;
-#else
-    GGML_UNUSED(K_c);
-    GGML_UNUSED(Q_v);
-    GGML_UNUSED(Q_q8);
-    GGML_UNUSED(Q_ds_v);
-    NO_DEVICE_CODE;
-#endif  // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 template <typename T, int D>
 static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
     const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A
 
     const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c;
     GGML_UNUSED(Q_v);
@@ -297,13 +264,6 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
     }
 
     return sum;
-#else
-    GGML_UNUSED(K_c);
-    GGML_UNUSED(Q_v);
-    GGML_UNUSED(Q_q8);
-    GGML_UNUSED(Q_ds_v);
-    NO_DEVICE_CODE;
-#endif  // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 template <typename T, int D>
diff --git a/ggml/src/ggml-cuda/mmq.cuh b/ggml/src/ggml-cuda/mmq.cuh
index 1396e7a75..deaed066f 100644
--- a/ggml/src/ggml-cuda/mmq.cuh
+++ b/ggml/src/ggml-cuda/mmq.cuh
@@ -60,12 +60,16 @@ static constexpr __device__ int get_mmq_x_max_device() {
 }
 
 static constexpr int get_mmq_y_host(const int cc) {
-    return int8_mma_available(cc) || cc >= CC_VOLTA ? 128 : 64;
+    return cc >= CC_OFFSET_AMD ? (cc == CC_RDNA1 ? 64 : 128) : (cc >= CC_VOLTA ? 128 : 64);
 }
 
 static constexpr __device__ int get_mmq_y_device() {
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
+#if defined(RDNA1)
+    return 64;
+#else
     return 128;
+#endif // defined RDNA1
 #else
 #if __CUDA_ARCH__ >= CC_VOLTA
     return 128;
@@ -2259,9 +2263,9 @@ static __device__ void mul_mat_q_process_tile(
 
 template <ggml_type type, int mmq_x, int nwarps, bool need_check>
 #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
-#if defined(RDNA3) || defined(RDNA2)
+#if defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
     __launch_bounds__(WARP_SIZE*nwarps, 2)
-#endif // defined(RDNA3) || defined(RDNA2)
+#endif // defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
 #else
 #if __CUDA_ARCH__ >= CC_VOLTA
     __launch_bounds__(WARP_SIZE*nwarps, 1)
diff --git a/ggml/src/ggml-cuda/mmvq.cu b/ggml/src/ggml-cuda/mmvq.cu
index e8d157169..e22faf69b 100644
--- a/ggml/src/ggml-cuda/mmvq.cu
+++ b/ggml/src/ggml-cuda/mmvq.cu
@@ -28,16 +28,22 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
 
 static constexpr __device__ int get_vdr_mmvq(ggml_type type) {
     return type == GGML_TYPE_Q4_0 ? VDR_Q4_0_Q8_1_MMVQ :
-        type == GGML_TYPE_Q4_1 ? VDR_Q4_1_Q8_1_MMVQ :
-        type == GGML_TYPE_Q5_0 ? VDR_Q5_0_Q8_1_MMVQ :
-        type == GGML_TYPE_Q5_1 ? VDR_Q5_1_Q8_1_MMVQ :
-        type == GGML_TYPE_Q8_0 ? VDR_Q8_0_Q8_1_MMVQ :
-        type == GGML_TYPE_Q2_K ? VDR_Q2_K_Q8_1_MMVQ :
-        type == GGML_TYPE_Q3_K ? VDR_Q3_K_Q8_1_MMVQ :
-        type == GGML_TYPE_Q4_K ? VDR_Q4_K_Q8_1_MMVQ :
-        type == GGML_TYPE_Q5_K ? VDR_Q5_K_Q8_1_MMVQ :
-        type == GGML_TYPE_Q6_K ? VDR_Q6_K_Q8_1_MMVQ :
-        type == GGML_TYPE_IQ4_NL ? VDR_Q4_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q4_1    ? VDR_Q4_1_Q8_1_MMVQ :
+        type == GGML_TYPE_Q5_0    ? VDR_Q5_0_Q8_1_MMVQ :
+        type == GGML_TYPE_Q5_1    ? VDR_Q5_1_Q8_1_MMVQ :
+        type == GGML_TYPE_Q8_0    ? VDR_Q8_0_Q8_1_MMVQ :
+        type == GGML_TYPE_Q2_K    ? VDR_Q2_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q3_K    ? VDR_Q3_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q4_K    ? VDR_Q4_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q5_K    ? VDR_Q5_K_Q8_1_MMVQ :
+        type == GGML_TYPE_Q6_K    ? VDR_Q6_K_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ2_XXS ? VDR_IQ2_XXS_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ2_XS  ? VDR_IQ2_XS_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ2_S   ? VDR_IQ2_S_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ3_XXS ? VDR_IQ3_XXS_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ3_S   ? VDR_IQ3_S_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ4_NL  ? VDR_IQ4_NL_Q8_1_MMVQ :
+        type == GGML_TYPE_IQ4_XS  ? VDR_IQ4_XS_Q8_1_MMVQ :
         1;
 }
 
diff --git a/ggml/src/ggml-cuda/vecdotq.cuh b/ggml/src/ggml-cuda/vecdotq.cuh
index 3b12d6566..3f3a87c75 100644
--- a/ggml/src/ggml-cuda/vecdotq.cuh
+++ b/ggml/src/ggml-cuda/vecdotq.cuh
@@ -1,4 +1,5 @@
 #include "common.cuh"
+#include <cstdint>
 
 static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
     const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
@@ -28,6 +29,18 @@ static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t *
     return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment
 }
 
+static __device__ __forceinline__ int get_int_b2(const void * x, const int & i32) {
+    const uint16_t * x16 = (const uint16_t *) x;
+
+    int x32  = x16[2*i32 + 0] <<  0;
+    x32     |= x16[2*i32 + 1] << 16;
+
+    return x32;
+}
+
+static __device__ __forceinline__ int get_int_b4(const void * x, const int & i32) {
+    return ((const int *) x)[i32]; // assume at least 4 byte alignment
+}
 
 // VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called
 // MMVQ = mul_mat_vec_q, MMQ = mul_mat_q
@@ -38,7 +51,6 @@ static __device__ __forceinline__ int get_int_from_uint8_aligned(const uint8_t *
 template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_impl(
     const int * v, const int * u, const float & d4, const half2 & ds8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     int sumi = 0;
 
 #pragma unroll
@@ -47,17 +59,14 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_imp
         const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
 
         // SIMD dot product of quantized values
-        sumi = __dp4a(vi0, u[2*i+0], sumi);
-        sumi = __dp4a(vi1, u[2*i+1], sumi);
+        sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi);
+        sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi);
     }
 
     const float2 ds8f = __half22float2(ds8);
 
     // second part effectively subtracts 8 from each quant value
     return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q4_1_Q8_1_MMVQ 2
@@ -66,7 +75,6 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_0_q8_1_imp
 template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_impl(
     const int * v, const int * u, const half2 & dm4, const half2 & ds8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     int sumi = 0;
 
 #pragma unroll
@@ -75,8 +83,8 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp
         const int vi1 = (v[i] >> 4) & 0x0F0F0F0F;
 
         // SIMD dot product of quantized values
-        sumi = __dp4a(vi0, u[2*i+0], sumi);
-        sumi = __dp4a(vi1, u[2*i+1], sumi);
+        sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi);
+        sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi);
     }
 
 #ifdef GGML_CUDA_F16
@@ -92,9 +100,6 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp
 
     // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it
     return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1));
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q5_0_Q8_1_MMVQ 2
@@ -103,7 +108,6 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp
 template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_impl(
     const int * vl, const int * vh, const int * u, const float & d5, const half2 & ds8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     int sumi = 0;
 
 #pragma unroll
@@ -113,23 +117,20 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_imp
         vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
         vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
         vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+        sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
 
         int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
         vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
         vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
         vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
         vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+        sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
     }
 
     const float2 ds8f = __half22float2(ds8);
 
     // second part effectively subtracts 16 from each quant value
     return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q5_1_Q8_1_MMVQ 2
@@ -138,7 +139,6 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_0_q8_1_imp
 template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_impl(
     const int * vl, const int * vh, const int * u, const half2 & dm5, const half2 & ds8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     int sumi = 0;
 
 #pragma unroll
@@ -148,14 +148,14 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp
         vi0    |= (vh[i] << 11) & 0x00001000; // 1 -> 12
         vi0    |= (vh[i] << 18) & 0x00100000; // 2 -> 20
         vi0    |= (vh[i] << 25) & 0x10000000; // 3 -> 28
-        sumi = __dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
+        sumi = ggml_cuda_dp4a(vi0, u[2*i+0], sumi); // SIMD dot product of quantized values
 
         int vi1 = (vl[i] >>  4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits
         vi1    |= (vh[i] >> 12) & 0x00000010; // 16 ->  4
         vi1    |= (vh[i] >>  5) & 0x00001000; // 17 -> 12
         vi1    |= (vh[i] <<  2) & 0x00100000; // 18 -> 20
         vi1    |= (vh[i] <<  9) & 0x10000000; // 19 -> 28
-        sumi = __dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
+        sumi = ggml_cuda_dp4a(vi1, u[2*i+1], sumi); // SIMD dot product of quantized values
     }
 
 #ifdef GGML_CUDA_F16
@@ -171,10 +171,6 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp
 
     // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it
     return sumi*d5d8 + m5s8 / (QI5_1 / vdr);
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q8_0_Q8_1_MMVQ 2
@@ -183,31 +179,26 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp
 template <typename T, int vdr> static __device__ __forceinline__ T vec_dot_q8_0_q8_1_impl(
     const int * v, const int * u, const T & d8_0, const T & d8_1) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     int sumi = 0;
 
 #pragma unroll
     for (int i = 0; i < vdr; ++i) {
         // SIMD dot product of quantized values
-        sumi = __dp4a(v[i], u[i], sumi);
+        sumi = ggml_cuda_dp4a(v[i], u[i], sumi);
     }
 
     return d8_0*d8_1 * ((T) sumi);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_impl(
     const int * v, const int * u, const half2 & dm8, const half2 & ds8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     int sumi = 0;
 
 #pragma unroll
     for (int i = 0; i < vdr; ++i) {
         // SIMD dot product of quantized values
-        sumi = __dp4a(v[i], u[i], sumi);
+        sumi = ggml_cuda_dp4a(v[i], u[i], sumi);
     }
 
 #ifdef GGML_CUDA_F16
@@ -223,9 +214,6 @@ template <int vdr> static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp
 
     // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it
     return sumi*d8d8 + m8s8 / (QI8_1 / vdr);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q2_K_Q8_1_MMVQ 1
@@ -236,7 +224,6 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
     const int & v, const int * __restrict__ u, const uint8_t * __restrict__ scales,
     const half2 & dm2, const float * __restrict__ d8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf_d = 0.0f;
     float sumf_m = 0.0f;
 
@@ -246,28 +233,24 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq(
 
         const int vi = (v >> (2*i)) & 0x03030303;
 
-        sumf_d += d8[i] * (__dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
+        sumf_d += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product
 
         // fill int with 4x m
         int m = sc >> 4;
         m |= m <<  8;
         m |= m << 16;
-        sumf_m += d8[i] * __dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
+        sumf_m += d8[i] * ggml_cuda_dp4a(m, u[i], 0); // multiply constant q2_K part with sum of q8_1 values
     }
 
     const float2 dm2f = __half22float2(dm2);
 
     return dm2f.x*sumf_d - dm2f.y*sumf_m;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 // contiguous u/y values
 static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const half2 * dm2, const float & d8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf_d = 0.0f;
     float sumf_m = 0.0f;
 
@@ -281,8 +264,8 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
 #pragma unroll
         for (int i = i0; i < i0 + QI8_1/2; ++i) {
             const int vi = (vi0 >> (2*(i % (QI8_1/2)))) & 0x03030303;
-            sumi_d = __dp4a(vi,         u[i], sumi_d); // SIMD dot product
-            sumi_m = __dp4a(0x01010101, u[i], sumi_m);
+            sumi_d = ggml_cuda_dp4a(vi,         u[i], sumi_d); // SIMD dot product
+            sumi_m = ggml_cuda_dp4a(0x01010101, u[i], sumi_m);
         }
 
         sumf_d += dm2f.x * sumi_d;
@@ -290,9 +273,6 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq(
     }
 
     return d8*(sumf_d - sumf_m);
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q3_K_Q8_1_MMVQ 1
@@ -303,7 +283,6 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
     const int & vl, const int & vh, const int * __restrict__ u, const uint8_t * __restrict__ scales,
     const int & scale_offset, const float & d3, const float * __restrict__ d8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf = 0.0f;
 
 #pragma unroll
@@ -326,13 +305,10 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq(
 
         const int vi = __vsubss4(vil, vih);
 
-        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+        sumf += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * sc); // SIMD dot product
     }
 
     return d3 * sumf;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 // contiguous u/y values
@@ -340,7 +316,6 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ scales,
     const float & d3, const float & d8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     int sumi = 0;
 
 #pragma unroll
@@ -350,16 +325,13 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq(
 #pragma unroll
         for (int i = i0; i < i0 + QI8_1/2; ++i) {
             const int vi = __vsubss4((v[i/2] >> (4*(i%2))) & 0x0F0F0F0F, 0x04040404);
-            sumi_sc = __dp4a(vi, u[i], sumi_sc); // SIMD dot product
+            sumi_sc = ggml_cuda_dp4a(vi, u[i], sumi_sc); // SIMD dot product
         }
 
         sumi += sumi_sc * scales[i0 / (QI8_1/2)];
     }
 
     return d3*d8 * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q4_K_Q8_1_MMVQ 2
@@ -370,7 +342,6 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
     const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
     const uint8_t * __restrict__ m, const half2 & dm4, const float * __restrict__ d8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf_d = 0.0f;
     float sumf_m = 0.0f;
 
@@ -379,8 +350,8 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
         const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F;
         const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F;
 
-        const int dot1 = __dp4a(v1i, u[2*i+1], __dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, u[2*i+1], __dp4a(0x01010101, u[2*i+0], 0)); // sum of u
+        const int dot1 = ggml_cuda_dp4a(v1i, u[2*i+1], ggml_cuda_dp4a(v0i, u[2*i+0], 0)); // SIMD dot product
+        const int dot2 = ggml_cuda_dp4a(0x01010101, u[2*i+1], ggml_cuda_dp4a(0x01010101, u[2*i+0], 0)); // sum of u
 
         sumf_d += d8[i] * (dot1 * sc[i]);
         sumf_m += d8[i] * (dot2 * m[i]);  // multiply constant part of q4_K with sum of q8_1 values
@@ -389,10 +360,6 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq(
     const float2 dm4f = __half22float2(dm4);
 
     return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 // contiguous u/y values
@@ -400,7 +367,6 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
     const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf_d = 0.0f;
     float sumf_m = 0.0f;
 
@@ -410,7 +376,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
 
 #pragma unroll
         for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = __dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
+            sumi_d = ggml_cuda_dp4a((v[j] >> (4*i)) & 0x0F0F0F0F, u[i*QI8_1 + j], sumi_d); // SIMD dot product
         }
 
         const float2 ds8f = __half22float2(ds8[i]);
@@ -422,10 +388,6 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq(
     const float2 dm4f = __half22float2(dm4);
 
     return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q5_K_Q8_1_MMVQ 2
@@ -436,7 +398,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
     const int * __restrict__ vl, const int * __restrict__ vh, const int * __restrict__ u, const uint8_t * __restrict__ sc,
     const uint8_t * __restrict__ m, const half2 & dm5, const float * __restrict__ d8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf_d = 0.0f;
     float sumf_m = 0.0f;
 
@@ -451,8 +412,8 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
         const int v0i = vl0i | vh0i;
         const int v1i = vl1i | vh1i;
 
-        const int dot1 = __dp4a(v0i, u[2*i+0], __dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
-        const int dot2 = __dp4a(0x01010101, u[2*i+0], __dp4a(0x01010101, u[2*i+1], 0)); // sum of u
+        const int dot1 = ggml_cuda_dp4a(v0i, u[2*i+0], ggml_cuda_dp4a(v1i, u[2*i+1], 0)); // SIMD dot product
+        const int dot2 = ggml_cuda_dp4a(0x01010101, u[2*i+0], ggml_cuda_dp4a(0x01010101, u[2*i+1], 0)); // sum of u
 
         sumf_d += d8[i] * (dot1 * sc[i]);
         sumf_m += d8[i] * (dot2 * m[i]);
@@ -462,10 +423,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq(
     const float2 dm5f = __half22float2(dm5);
 
     return dm5f.x*sumf_d - dm5f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 // contiguous u/y values
@@ -473,7 +430,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ sc,
     const uint8_t * __restrict__ m, const half2 & dm4, const half2 * __restrict__ ds8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf_d = 0.0f;
     float sumf_m = 0.0f;
 
@@ -483,7 +439,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
 
 #pragma unroll
         for (int j = 0; j < QI8_1; ++j) {
-            sumi_d = __dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
+            sumi_d = ggml_cuda_dp4a(v[i*QI8_1 + j], u[i*QI8_1 + j], sumi_d); // SIMD dot product
         }
 
         const float2 ds8f = __half22float2(ds8[i]);
@@ -495,10 +451,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq(
     const float2 dm4f = __half22float2(dm4);
 
     return dm4f.x*sumf_d - dm4f.y*sumf_m;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 #define VDR_Q6_K_Q8_1_MMVQ 1
@@ -509,7 +461,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
     const int & vl, const int & vh, const int * __restrict__ u, const int8_t * __restrict__ scales,
     const float & d, const float * __restrict__ d8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf = 0.0f;
 
 #pragma unroll
@@ -522,13 +473,10 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq(
 
         const int vi = __vsubss4((vil | vih), 0x20202020); // vi = (vil | vih) - 32
 
-        sumf += d8[i] * (__dp4a(vi, u[i], 0) * sc); // SIMD dot product
+        sumf += d8[i] * (ggml_cuda_dp4a(vi, u[i], 0) * sc); // SIMD dot product
     }
 
     return d*sumf;
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 // contiguous u/y values
@@ -536,7 +484,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
     const int * __restrict__ v, const int * __restrict__ u, const int8_t * __restrict__ sc,
     const float & d6, const float * __restrict__ d8) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     float sumf_d = 0.0f;
 
 #pragma unroll
@@ -545,21 +492,17 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq(
 
 #pragma unroll
         for (int i = i0; i < i0 + 2; ++i) {
-            sumi_d.x = __dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
-            sumi_d.x = __dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
+            sumi_d.x = ggml_cuda_dp4a(v[2*i+0], u[2*i+0], sumi_d.x); // SIMD dot product
+            sumi_d.x = ggml_cuda_dp4a(v[2*i+1], u[2*i+1], sumi_d.x); // SIMD dot product
 
-            sumi_d.y = __dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
-            sumi_d.y = __dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
+            sumi_d.y = ggml_cuda_dp4a(v[2*i+4], u[2*i+4], sumi_d.y); // SIMD dot product
+            sumi_d.y = ggml_cuda_dp4a(v[2*i+5], u[2*i+5], sumi_d.y); // SIMD dot product
         }
 
         sumf_d += d8[i0/4] * (sc[i0/2+0]*sumi_d.x + sc[i0/2+1]*sumi_d.y);
     }
 
     return d6 * sumf_d;
-
-#else
-    NO_DEVICE_CODE;
-#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
 }
 
 static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
@@ -572,9 +515,9 @@ static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
 
 #pragma unroll
     for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) {
-        v[i]     = get_int_from_uint8(bq4_0->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
+        v[i]     = get_int_b2(bq4_0->qs, iqs + i);
+        u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI4_0);
     }
 
     return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
@@ -591,9 +534,9 @@ static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
 
 #pragma unroll
     for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
-        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
+        v[i]     = get_int_b4(bq4_1->qs, iqs + i);
+        u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI4_1);
     }
 
     return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
@@ -610,10 +553,10 @@ static __device__ __forceinline__ float vec_dot_q5_0_q8_1(
 
 #pragma unroll
     for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) {
-        vl[i]    = get_int_from_uint8(bq5_0->qs, iqs + i);
-        vh[i]    = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0);
+        vl[i]    = get_int_b2(bq5_0->qs, iqs + i);
+        vh[i]    = get_int_b2(bq5_0->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI5_0);
     }
 
     return vec_dot_q5_0_q8_1_impl<VDR_Q5_0_Q8_1_MMVQ>(vl, vh, u, bq5_0->d, bq8_1->ds);
@@ -630,10 +573,10 @@ static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
 
 #pragma unroll
     for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
-        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
-        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
-        u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
-        u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
+        vl[i]    = get_int_b4(bq5_1->qs, iqs + i);
+        vh[i]    = get_int_b4(bq5_1->qh, 0) >> (4 * (iqs + i));
+        u[2*i+0] = get_int_b4(bq8_1->qs, iqs + i);
+        u[2*i+1] = get_int_b4(bq8_1->qs, iqs + i + QI5_1);
     }
 
     return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
@@ -649,8 +592,8 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
 
 #pragma unroll
     for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) {
-        v[i] = get_int_from_int8(bq8_0->qs, iqs + i);
-        u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
+        v[i] = get_int_b2(bq8_0->qs, iqs + i);
+        u[i] = get_int_b4(bq8_1->qs, iqs + i);
     }
 
     return vec_dot_q8_0_q8_1_impl<float, VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
@@ -666,13 +609,13 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
 
     const uint8_t * scales = bq2_K->scales + scale_offset;
 
-    const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs);
+    const int v = get_int_b4(bq2_K->qs, iqs);
     int    u[QR2_K];
     float d8[QR2_K];
 
 #pragma unroll
     for (int i = 0; i < QR2_K; ++ i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        u[i]  = get_int_b4(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
         d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
     }
 
@@ -689,17 +632,17 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
 
     const float d = bq3_K->d;
 
-    const int vl = get_int_from_uint8(bq3_K->qs, iqs);
+    const int vl = get_int_b2(bq3_K->qs, iqs);
 
     // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted
-    const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
+    const int vh = ~get_int_b2(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset;
 
     int    u[QR3_K];
     float d8[QR3_K];
 
 #pragma unroll
     for (int i = 0; i < QR3_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
+        u[i]  = get_int_b4(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
         d8[i] = __low2float(bq8_1[bq8_offset + i].ds);
     }
 
@@ -807,8 +750,8 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
     const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8);
     const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4));
 
-    const int vl = get_int_from_uint8(bq6_K->ql, iqs);
-    const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
+    const int vl = get_int_b2(bq6_K->ql, iqs);
+    const int vh = get_int_b2(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift;
 
     const int8_t * scales = bq6_K->scales + scale_offset;
 
@@ -817,335 +760,342 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
 
 #pragma unroll
     for (int i = 0; i < QR6_K; ++i) {
-        u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
+        u[i]  = get_int_b4(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
         d8[i] = __low2float(bq8_1[bq8_offset + 2*i].ds);
     }
 
     return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
 }
 
+#define VDR_IQ2_XXS_Q8_1_MMVQ 2
+
 static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
     const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq + kbx;
 
-#if QR2_XXS == 8
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = q2[2] | (q2[3] << 16);
+    const int q2 = get_int_b2(bq2->qs, iqs);
+    const uint8_t * aux8 = (const uint8_t *) &q2;
+    const uint32_t aux32 = get_int_b2(bq2->qs, iqs + 1);
+
     int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(iq2xxs_grid + aux8[l]);
-        const uint8_t  signs = ksigns_iq2xs[aux32 & 127];
-        for (int j = 0; j < 8; ++j) {
-            sumi += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1);
-        }
-        q8 += 8;
-        aux32 >>= 7;
+#pragma unroll
+    for (int k0 = 0; k0 < 8; k0 += 2) {
+        const int * grid_pos = (const int *) (iq2xxs_grid + aux8[k0/2]);
+        const int signs_packed = ksigns_iq2xs[(aux32 >> (7*k0/2)) & 0x7F];
+
+        const int signs0 = __vcmpne4(((signs_packed & 0x03) << 7) | ((signs_packed & 0x0C) << 21), 0x00000000);
+        const int grid0 = __vsub4(grid_pos[0] ^ signs0, signs0);
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, k0 + 0);
+        sumi = ggml_cuda_dp4a(grid0, u0, sumi);
+
+        const int signs1 = __vcmpne4(((signs_packed & 0x30) << 3) | ((signs_packed & 0xC0) << 17), 0x00000000);
+        const int grid1 = __vsub4(grid_pos[1] ^ signs1, signs1);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, k0 + 1);
+        sumi = ggml_cuda_dp4a(grid1, u1, sumi);
     }
-    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f;
+
+    const int ls = aux32 >> 28;
+    sumi = (ls*sumi + sumi/2)/4;
+    const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds);
     return d * sumi;
-#else
-    // iqs is 0...15
-    const int ib32 = iqs/2;
-    const int il = iqs%2;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f;
-    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
-    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
-    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
-    int sumi1 = 0, sumi2 = 0;
-    for (int j = 0; j < 8; ++j) {
-        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
-        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
-    }
-    return d * (sumi1 + sumi2);
-#endif
 }
 
+#define VDR_IQ2_XS_Q8_1_MMVQ 2
+
 static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+
     const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq + kbx;
 
-    const int ib32 = iqs;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
-    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    const int2 q2_packed = make_int2(get_int_b2(bq2->qs, iqs + 0), get_int_b2(bq2->qs, iqs + 1));
+    const uint16_t * q2 = (const uint16_t *) &q2_packed;
+    const int ls0 = bq2->scales[iqs/2] & 0x0F;
+    const int ls1 = bq2->scales[iqs/2] >> 4;
+
+    int sumi0 = 0;
     int sumi1 = 0;
-    for (int l = 0; l < 2; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
-        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
-        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
-        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
-        q8 += 8;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const uint32_t * grid_pos = (const uint32_t *)(iq2xs_grid + (q2[l0/2] & 0x000001FF));
+        const uint32_t * signs    = (const uint32_t *)(ksigns64   + (q2[l0/2] >> 9));
+
+        const int grid_l = __vsub4(grid_pos[0] ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid_pos[1] ^ signs[1], signs[1]);
+
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1);
+
+        if (l0 < 4) {
+            sumi0 = ggml_cuda_dp4a(grid_l, u0, sumi0);
+            sumi0 = ggml_cuda_dp4a(grid_h, u1, sumi0);
+        } else {
+            sumi1 = ggml_cuda_dp4a(grid_l, u0, sumi1);
+            sumi1 = ggml_cuda_dp4a(grid_h, u1, sumi1);
+        }
     }
-    int sumi2 = 0;
-    for (int l = 2; l < 4; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511));
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9));
-        const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]);
-        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
-        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
-        q8 += 8;
-    }
-    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
-    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
-#else
-    GGML_UNUSED(ksigns64);
-    NO_DEVICE_CODE;
-#endif
+    const int sumi = (sumi0*ls0 + sumi1*ls1 + (sumi0 + sumi1)/2)/4;
+    const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds);
+    return d * sumi;
 }
 
-// TODO
+#define VDR_IQ2_S_Q8_1_MMVQ 2
+
 static __device__ __forceinline__ float vec_dot_iq2_s_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
+
     const block_iq2_s * bq2 = (const block_iq2_s *) vbq + kbx;
 
-    const int ib32 = iqs;
-    const int8_t  * q8 = bq8_1[ib32].qs;
-    const uint8_t * signs = bq2->qs + QK_K/8 + 4*ib32;
-    const uint8_t ls1 = bq2->scales[ib32] & 0xf;
-    const uint8_t ls2 = bq2->scales[ib32] >>  4;
+    const int       qs_packed = get_int_b2(bq2->qs, iqs/2);
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    const int qh = bq2->qh[iqs/2];
+
+    const int       signs_packed_32 = get_int_b2(bq2->qs, QK_K/32 + iqs/2);
+    const uint8_t * signs_packed_8  = (const uint8_t *) &signs_packed_32;
+
+    const int ls0 = bq2->scales[iqs/2] & 0x0F;
+    const int ls1 = bq2->scales[iqs/2] >> 4;
+
+    int sumi0 = 0;
     int sumi1 = 0;
-    for (int l = 0; l < 2; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
-        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
-        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
-        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
-        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
-        sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1);
-        sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1);
-        q8 += 8;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int * grid_pos = (const int *)(iq2s_grid + (qs[l0/2] | ((qh << (8-l0)) & 0x300)));
+
+        const int signs0 = __vcmpne4(((signs_packed_8[l0/2] & 0x03) << 7) | ((signs_packed_8[l0/2] & 0x0C) << 21), 0x00000000);
+        const int signs1 = __vcmpne4(((signs_packed_8[l0/2] & 0x30) << 3) | ((signs_packed_8[l0/2] & 0xC0) << 17), 0x00000000);
+
+        const int grid_l = __vsub4(grid_pos[0] ^ signs0, signs0);
+        const int grid_h = __vsub4(grid_pos[1] ^ signs1, signs1);
+
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1);
+
+        if (l0 < 4) {
+            sumi0 = ggml_cuda_dp4a(grid_l, u0, sumi0);
+            sumi0 = ggml_cuda_dp4a(grid_h, u1, sumi0);
+        } else {
+            sumi1 = ggml_cuda_dp4a(grid_l, u0, sumi1);
+            sumi1 = ggml_cuda_dp4a(grid_h, u1, sumi1);
+        }
     }
-    int sumi2 = 0;
-    for (int l = 2; l < 4; ++l) {
-        const uint32_t * grid = (const uint32_t *)(iq2s_grid + (bq2->qs[4*ib32+l] | ((bq2->qh[ib32] << (8-2*l)) & 0x300)));
-        const uint32_t signs0 = __vcmpeq4(((signs[l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
-        const uint32_t signs1 = __vcmpeq4(((signs[l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
-        const int grid_l = __vsub4(grid[0] ^ signs0, signs0);
-        const int grid_h = __vsub4(grid[1] ^ signs1, signs1);
-        sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2);
-        sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2);
-        q8 += 8;
-    }
-    const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f;
-    return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2);
-#else
-    GGML_UNUSED(ksigns64);
-    NO_DEVICE_CODE;
-#endif
+    const int sumi = (sumi0*ls0 + sumi1*ls1 + (sumi0 + sumi1)/2)/4;
+
+    const float d = __half2float(bq2->d) * __low2float(bq8_1[iqs/2].ds);
+    return d * sumi;
 }
 
+#define VDR_IQ3_XXS_Q8_1_MMVQ 2
+
 static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq + kbx;
 
-    const int ib32 = iqs;
-    const uint8_t  * q3 = bq2->qs + 8*ib32;
-    const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
-    uint32_t aux32 = gas[0] | (gas[1] << 16);
+    const block_iq3_xxs * bq3 = (const block_iq3_xxs *) vbq + kbx;
+
+    const int2 q3_packed = make_int2(get_int_b2(bq3->qs, iqs), get_int_b2(bq3->qs, iqs+1));
+    const uint8_t * q3 = (const uint8_t *) &q3_packed;
+    const uint32_t aux32 = get_int_b2(bq3->qs, QK_K/16 + iqs/2);
+
     int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0];
-        const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1];
-        const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127));
-        const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]);
-        const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]);
-        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
-        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
-        q8 += 8;
-        aux32 >>= 7;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int2 grid_pos = make_int2(iq3xxs_grid[q3[l0 + 0]], iq3xxs_grid[q3[l0 + 1]]);
+
+        const int * signs = (const int *)(ksigns64 + ((aux32 >> (7*l0/2)) & 0x7F));
+
+        const int grid_l = __vsub4(grid_pos.x ^ signs[0], signs[0]);
+        const int grid_h = __vsub4(grid_pos.y ^ signs[1], signs[1]);
+
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1);
+
+        sumi = ggml_cuda_dp4a(grid_l, u0, sumi);
+        sumi = ggml_cuda_dp4a(grid_h, u1, sumi);
     }
-    const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f;
+
+    const int ls = aux32 >> 28;
+    sumi = (ls*sumi + sumi/2)/2;
+    const float d = __half2float(bq3->d) * __low2float(bq8_1[iqs/2].ds);
     return d * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif
 }
 
+#define VDR_IQ3_S_Q8_1_MMVQ 2
+
 // TODO: don't use lookup table for signs
 static __device__ __forceinline__ float vec_dot_iq3_s_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const block_iq3_s * bq2 = (const block_iq3_s *) vbq + kbx;
 
-    const int ib32 = iqs;
-    const uint8_t  * qs = bq2->qs + 8*ib32;
-    const int8_t   * q8 = bq8_1[ib32].qs;
+    const block_iq3_s * bq3 = (const block_iq3_s *) vbq + kbx;
+
+    const int2      qs_packed = make_int2(get_int_b2(bq3->qs, iqs + 0), get_int_b2(bq3->qs, iqs + 1));
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    const int qh = bq3->qh[iqs/2];
+
+    const int       signs_packed_32 = get_int_b2(bq3->signs, iqs/2);
+    const uint8_t * signs_packed_8  = (const uint8_t *) &signs_packed_32;
+
     int sumi = 0;
-    for (int l = 0; l < 4; ++l) {
-        const uint32_t * grid1 = iq3s_grid + (qs[2*l+0] | ((bq2->qh[ib32] << (8 - 2*l)) & 256));
-        const uint32_t * grid2 = iq3s_grid + (qs[2*l+1] | ((bq2->qh[ib32] << (7 - 2*l)) & 256));
-        uint32_t signs0 = __vcmpeq4(((bq2->signs[4*ib32+l] & 0xf) * 0x01010101) & 0x08040201, 0x08040201);
-        uint32_t signs1 = __vcmpeq4(((bq2->signs[4*ib32+l] >>  4) * 0x01010101) & 0x08040201, 0x08040201);
-        const int grid_l = __vsub4(grid1[0] ^ signs0, signs0);
-        const int grid_h = __vsub4(grid2[0] ^ signs1, signs1);
-        sumi = __dp4a(grid_l, *((int *)q8+0), sumi);
-        sumi = __dp4a(grid_h, *((int *)q8+1), sumi);
-        q8 += 8;
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int2 grid_pos = make_int2(
+            iq3s_grid[qs[l0 + 0] | ((qh << (8 - l0)) & 0x100)],
+            iq3s_grid[qs[l0 + 1] | ((qh << (7 - l0)) & 0x100)]);
+
+        const int signs0 = __vcmpne4(((signs_packed_8[l0/2] & 0x03) << 7) | ((signs_packed_8[l0/2] & 0x0C) << 21), 0x00000000);
+        const int signs1 = __vcmpne4(((signs_packed_8[l0/2] & 0x30) << 3) | ((signs_packed_8[l0/2] & 0xC0) << 17), 0x00000000);
+
+        const int grid_l = __vsub4(grid_pos.x ^ signs0, signs0);
+        const int grid_h = __vsub4(grid_pos.y ^ signs1, signs1);
+
+        const int u0 = get_int_b4(bq8_1[iqs/2].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/2].qs, l0 + 1);
+
+        sumi = ggml_cuda_dp4a(grid_l, u0, sumi);
+        sumi = ggml_cuda_dp4a(grid_h, u1, sumi);
     }
-    const float d = (float)bq2->d * (1 + 2*((bq2->scales[ib32/2] >> 4*(ib32%2)) & 0xf)) * __low2float(bq8_1[ib32].ds);
+
+    sumi *= 1 + 2*((bq3->scales[iqs/4] >> ((iqs << 1) & 0x04)) & 0x0F);
+
+    const float d = __half2float(bq3->d) * __low2float(bq8_1[iqs/2].ds);
     return d * sumi;
-#else
-    NO_DEVICE_CODE;
-#endif
 }
 
 static __device__ __forceinline__ float vec_dot_iq1_s_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
     const block_iq1_s * bq1 = (const block_iq1_s *) vbq + kbx;
 
-    const int ib32 = iqs;
+    const int       qs_packed = get_int_b2(bq1->qs, iqs);
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    const int qh = bq1->qh[iqs];
+
     int sumi = 0;
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const int * q8 = (const int *)bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
-        int grid0 = grid[0] & 0x0f0f0f0f;
-        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
-        sumi = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi));
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int grid = iq1s_grid_gpu[qs[l0/2] | (((qh >> 3*(l0/2)) & 0x07) << 8)];
+
+        const int grid0 = (grid >> 0) & 0x0F0F0F0F;
+        const int grid1 = (grid >> 4) & 0x0F0F0F0F;
+
+        const int u0 = get_int_b4(bq8_1[iqs].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs].qs, l0 + 1);
+
+        sumi = ggml_cuda_dp4a(grid0, u0, sumi);
+        sumi = ggml_cuda_dp4a(grid1, u1, sumi);
     }
-#else
-    const int8_t * q8 = bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
-        for (int j = 0; j < 4; ++j) {
-            sumi += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
-        }
-        q8 += 8;
-    }
-#endif
-    const float delta = bq1->qh[ib32] & 0x8000 ? -1-IQ1S_DELTA : -1+IQ1S_DELTA;
-    const float d1q = (float)bq1->d * (2*((bq1->qh[ib32] >> 12) & 7) + 1);
-    const float d = d1q * __low2float (bq8_1[ib32].ds);
-    const float m = d1q * __high2float(bq8_1[ib32].ds);
-    return d * sumi + m * delta;
+
+    const float  d1q   = __half2float(bq1->d) * (((qh >> 11) & 0x0E) + 1);
+    const float  delta = -1.0f + IQ1S_DELTA - (qh & 0x8000) * (2.0f*IQ1S_DELTA/0x8000);
+    const float2 ds    = __half22float2(bq8_1[iqs].ds);
+    return d1q * (ds.x*sumi + ds.y*delta);
 }
 
 static __device__ __forceinline__ float vec_dot_iq1_m_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
     const block_iq1_m * bq1 = (const block_iq1_m *) vbq + kbx;
 
-    const int ib32 = iqs;
-    int   sumi[2] = {0, 0};
-    float sumf[2] = {0.f, 0.f};
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const int * q8 = (const int *)bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const int * grid = (const int *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 7) << 8)));
-        int grid0 = grid[0] & 0x0f0f0f0f;
-        int grid1 = (grid[0] >> 4) & 0x0f0f0f0f;
-        sumi[l/2] = __dp4a(q8[2*l+1], grid1, __dp4a(q8[2*l+0], grid0, sumi[l/2]));
-        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
-        const int sumy = __dp4a(q8[2*l+1], 0x01010101, __dp4a(q8[2*l+0], 0x01010101, 0));
-        sumf[l/2] += delta*sumy;
-    }
-#else
-    const int8_t * q8 = bq8_1[ib32].qs;
-    for (int l = 0; l < 4; ++l) {
-        const uint8_t * grid = (const uint8_t *)(iq1s_grid_gpu + (bq1->qs[4*ib32+l] | (((bq1->qh[ib32] >> 3*l) & 7) << 8)));
+    const int       qs_packed = get_int_b4(bq1->qs, iqs);
+    const uint8_t * qs        = (const uint8_t *) &qs_packed;
+
+    int   sumi[2] = {0};
+    float sumf[2] = {0.0f};
+#pragma unroll
+    for (int l0 = 0; l0 < 8; l0 += 2) {
+        const int qhl = bq1->qh[2*iqs + l0/4] >> (4 * ((l0/2) % 2));
+
+        const int grid = iq1s_grid_gpu[qs[l0/2] | ((qhl & 0x07) << 8)];
+
+        const int grid0 = (grid >> 0) & 0x0F0F0F0F;
+        const int grid1 = (grid >> 4) & 0x0F0F0F0F;
+
+        const int u0 = get_int_b4(bq8_1[iqs].qs, l0 + 0);
+        const int u1 = get_int_b4(bq8_1[iqs].qs, l0 + 1);
+
+        sumi[l0/4] = ggml_cuda_dp4a(grid0, u0, sumi[l0/4]);
+        sumi[l0/4] = ggml_cuda_dp4a(grid1, u1, sumi[l0/4]);
+
+        const float delta = -1.0f + IQ1M_DELTA - (qhl & 0x08) * (2.0f*IQ1M_DELTA/0x08);
         int sumy = 0;
-        for (int j = 0; j < 4; ++j) {
-            sumi[l/2] += q8[j] * (grid[j] & 0xf) + q8[j+4] * (grid[j] >> 4);
-            sumy += q8[j] + q8[j+4];
-        }
-        const float delta = (bq1->qh[2*ib32+l/2] >> 4*(l%2)) & 0x08 ? -1-IQ1M_DELTA : -1+IQ1M_DELTA;
-        sumf[l/2] += delta*sumy;
-        q8 += 8;
+        sumy = ggml_cuda_dp4a(u0, 0x01010101, sumy);
+        sumy = ggml_cuda_dp4a(u1, 0x01010101, sumy);
+        sumf[l0/4] += delta*sumy;
     }
-#endif
+
+    const uint16_t * sc = (const uint16_t *) bq1->scales;
+
     iq1m_scale_t scale;
-    const uint16_t * sc = (const uint16_t *)bq1->scales;
-    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000);
-    const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds);
-    return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1));
+    scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00F0) | ((sc[2] >> 4) & 0x0F00) | (sc[3] & 0xF000);
+    const float d = __half2float(scale.f16) * __low2float(bq8_1[iqs].ds);
+
+    const int tmp = sc[iqs/2] >> (6*(iqs%2));
+    const int sc0 = 2*((tmp >> 0) & 0x07) + 1;
+    const int sc1 = 2*((tmp >> 3) & 0x07) + 1;
+    return d * ((sumi[0] + sumf[0]) * sc0 + (sumi[1] + sumf[1]) * sc1);
 }
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4, const uint8_t * values,
-        int & val1, int & val2) {
+static __device__ __forceinline__ int2 get_int_from_table_16(const int & q4) {
+    const int      q0_32  = (q4 >> 0) & 0x0F0F0F0F;
+    const int8_t * q0_8   = (const int8_t *) &q0_32;
+    const char4    val0_8 = make_char4(
+        kvalues_iq4nl[q0_8[0]], kvalues_iq4nl[q0_8[1]], kvalues_iq4nl[q0_8[2]], kvalues_iq4nl[q0_8[3]]);
 
-    uint32_t aux32; const uint8_t * q8 = (const uint8_t *)&aux32;
-    aux32 = q4 & 0x0f0f0f0f;
-    uint16_t v1 = values[q8[0]] | (values[q8[1]] << 8);
-    uint16_t v2 = values[q8[2]] | (values[q8[3]] << 8);
-    val1 = v1 | (v2 << 16);
-    aux32 = (q4 >> 4) & 0x0f0f0f0f;
-    v1 = values[q8[0]] | (values[q8[1]] << 8);
-    v2 = values[q8[2]] | (values[q8[3]] << 8);
-    val2 = v1 | (v2 << 16);
+    const int      q1_32  = (q4 >> 4) & 0x0F0F0F0F;
+    const int8_t * q1_8   = (const int8_t *) &q1_32;
+    const char4    val1_8 = make_char4(
+        kvalues_iq4nl[q1_8[0]], kvalues_iq4nl[q1_8[1]], kvalues_iq4nl[q1_8[2]], kvalues_iq4nl[q1_8[3]]);
+
+    return make_int2(*((const int *) &val0_8), *((const int *) &val1_8));
 }
-#endif
+
+#define VDR_IQ4_NL_Q8_1_MMVQ 2
 
 static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
 
-    const block_iq4_nl * bq = (const block_iq4_nl *) vbq + kbx;
+    const block_iq4_nl * bq4 = (const block_iq4_nl *) vbq + kbx;
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
-    const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs;
-    const int32_t  * q8 = (const int32_t  *)bq8_1->qs + iqs;
+    const int * q8 = (const int *) bq8_1->qs + iqs;
 
-    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
-
-    int v1, v2;
-    int sumi1 = 0, sumi2 = 0;
+    int sumi = 0;
+#pragma unroll
     for (int l = 0; l < VDR_Q4_0_Q8_1_MMVQ; ++l) {
-        const uint32_t aux = q4[2*l] | (q4[2*l+1] << 16);
-        get_int_from_table_16(aux, values, v1, v2);
-        sumi1 = __dp4a(v1, q8[l+0], sumi1);
-        sumi2 = __dp4a(v2, q8[l+4], sumi2);
+        const int aux_q4 = get_int_b2(bq4->qs, iqs + l);
+        const int2 v = get_int_from_table_16(aux_q4);
+
+        sumi = ggml_cuda_dp4a(v.x, q8[l + 0], sumi);
+        sumi = ggml_cuda_dp4a(v.y, q8[l + 4], sumi);
     }
 
-#else
-    const uint8_t * q4 = bq->qs + 4*iqs;
-    const int8_t  * q8 = bq8_1->qs + 4*iqs;
-
-    int sumi1 = 0, sumi2 = 0;
-    for (int l = 0; l < 4*VDR_Q4_0_Q8_1_MMVQ; ++l) {
-        sumi1 += q8[l+ 0] * kvalues_iq4nl[q4[l] & 0xf];
-        sumi2 += q8[l+16] * kvalues_iq4nl[q4[l] >>  4];
-    }
-#endif
-    const float d = (float)bq->d * __low2float(bq8_1->ds);
-    return d * (sumi1 + sumi2);
+    const float d = __half2float(bq4->d) * __low2float(bq8_1->ds);
+    return d * sumi;
 }
 
+#define VDR_IQ4_XS_Q8_1_MMVQ 4
+
 static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1(
     const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
 
-#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics
     const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq + kbx;
-    const uint8_t * values = (const uint8_t *)kvalues_iq4nl;
 
-    // iqs is 0...7
-    const int ib32 = iqs;
-    const int32_t  * q8 = (const int *)bq8_1[ib32].qs;
-    const uint32_t * q4 = (const uint32_t *)bq4->qs + 4*ib32;
-    const int8_t ls = ((bq4->scales_l[ib32/2] >> 4*(ib32%2)) & 0xf) | (((bq4->scales_h >> 2*ib32) & 3) << 4);
-    const float d = (float)bq4->d * (ls - 32) * __low2float(bq8_1[ib32].ds);
-    int v1, v2;
-    int sumi1 = 0, sumi2 = 0;
+    int sumi = 0;
+#pragma unroll
     for (int j = 0; j < 4; ++j) {
-        get_int_from_table_16(q4[j], values, v1, v2);
-        sumi1 = __dp4a(v1, q8[j+0], sumi1);
-        sumi2 = __dp4a(v2, q8[j+4], sumi2);
+        const int aux_q4 = get_int_b4(bq4->qs, iqs + j);
+        const int2 v = get_int_from_table_16(aux_q4);
+
+        const int u0 = get_int_b4(bq8_1[iqs/4].qs, j + 0);
+        const int u1 = get_int_b4(bq8_1[iqs/4].qs, j + 4);
+
+        sumi = ggml_cuda_dp4a(v.x, u0, sumi);
+        sumi = ggml_cuda_dp4a(v.y, u1, sumi);
     }
-    return d * (sumi1 + sumi2);
-#else
-    return vec_dot_iq4_xs_q8_1(vbq, bq8_1, kbx, iqs);
-#endif
+
+    const int ls = ((bq4->scales_l[iqs/8] >> (iqs & 0x04)) & 0x0F) | (((bq4->scales_h >> (iqs/2)) & 0x03) << 4);
+    sumi *= ls - 32;
+
+    const float d = __half2float(bq4->d) * __low2float(bq8_1[iqs/4].ds);
+    return d * sumi;
 }
diff --git a/ggml/src/ggml-metal.metal b/ggml/src/ggml-metal.metal
index e2796fd60..c3503479b 100644
--- a/ggml/src/ggml-metal.metal
+++ b/ggml/src/ggml-metal.metal
@@ -6537,4 +6537,3 @@ template [[host_name("kernel_mul_mv_id_iq3_s_f32")]]   kernel kernel_mul_mv_id_t
 template [[host_name("kernel_mul_mv_id_iq2_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_s_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_nl_f32_impl>>;
 template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_xs_f32_impl>>;
-
diff --git a/ggml/src/ggml-quants.h b/ggml/src/ggml-quants.h
index 4d436a8f0..30983b872 100644
--- a/ggml/src/ggml-quants.h
+++ b/ggml/src/ggml-quants.h
@@ -130,4 +130,3 @@ void iq3xs_free_impl(int grid_size);
 #ifdef __cplusplus
 }
 #endif
-
diff --git a/ggml/src/ggml-sycl.cpp b/ggml/src/ggml-sycl.cpp
index 4a668a2c3..76bad57e2 100644
--- a/ggml/src/ggml-sycl.cpp
+++ b/ggml/src/ggml-sycl.cpp
@@ -74,51 +74,6 @@ typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const gg
                                        const float *src1_dd, float *dst_dd,
                                        const queue_ptr &main_stream);
 
-static __dpct_inline__ float warp_reduce_sum(float x,
-                                             const sycl::nd_item<3> &item_ct1) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        /*
-        DPCT1096:98: The right-most dimension of the work-group used in the SYCL
-        kernel that calls this function may be less than "32". The function
-        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
-        CPU device. Modify the size of the work-group to ensure that the value
-        of the right-most dimension is a multiple of "32".
-        */
-        x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask);
-    }
-    return x;
-}
-
-static __dpct_inline__ sycl::float2
-warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3> &item_ct1) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(),
-                                                mask);
-        a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(),
-                                                mask);
-    }
-    return a;
-}
-
-static __dpct_inline__ float warp_reduce_max(float x,
-                                             const sycl::nd_item<3> &item_ct1) {
-#pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        /*
-        DPCT1096:97: The right-most dimension of the work-group used in the SYCL
-        kernel that calls this function may be less than "32". The function
-        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
-        CPU device. Modify the size of the work-group to ensure that the value
-        of the right-most dimension is a multiple of "32".
-        */
-        x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
-                              item_ct1.get_sub_group(), x, mask));
-    }
-    return x;
-}
-
 static __dpct_inline__ float op_repeat(const float a, const float b) {
     return b;
     GGML_UNUSED(a);
@@ -336,47 +291,6 @@ static void sqr_f32(const float * x, float * dst, const int k,
     dst[i] = x[i] * x[i];
 }
 
-static void norm_f32(const float * x, float * dst, const int ncols, const float eps,
-                     const sycl::nd_item<3> &item_ct1, sycl::float2 *s_sum, int block_size) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-    const int tid = item_ct1.get_local_id(2);
-
-    sycl::float2 mean_var = sycl::float2(0.f, 0.f);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
-        mean_var.x() += xi;
-        mean_var.y() += xi * xi;
-    }
-
-    // sum up partial sums
-    mean_var = warp_reduce_sum(mean_var, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = mean_var;
-        }
-        /*
-        DPCT1118:0: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-        mean_var = s_sum[lane_id];
-        mean_var = warp_reduce_sum(mean_var, item_ct1);
-    }
-
-    const float mean = mean_var.x() / ncols;
-    const float var = mean_var.y() / ncols - mean * mean;
-    const float inv_std = sycl::rsqrt(var + eps);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
-    }
-}
-
 static void concat_f32(const float  *x,const float  *y, float *dst, const int ne0, const int ne02,
                        const sycl::nd_item<3> &item_ct1) {
     int nidx = item_ct1.get_local_id(2) +
@@ -444,126 +358,11 @@ static void pad_f32(const float  *x, float *dst, const int ne0, const int ne00,
     }
 }
 
-static void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps,
-                           const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) {
-    int start = item_ct1.get_group(2) * group_size;
-    int end = start + group_size;
-
-    start += item_ct1.get_local_id(2);
-
-    if (end >= ne_elements) {
-        end = ne_elements;
-    }
-
-    float tmp = 0.0f; // partial sum for thread in warp
-
-    for (int j = start; j < end; j += block_size) {
-        tmp += x[j];
-    }
-
-    tmp = warp_reduce_sum(tmp, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        /*
-        DPCT1118:1: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        /*
-        DPCT1065:54: Consider replacing sycl::nd_item::barrier() with
-        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-        better performance if there is no access to global memory.
-        */
-        item_ct1.barrier();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp, item_ct1);
-    }
-
-    float mean = tmp / group_size;
-    tmp = 0.0f;
-
-    for (int j = start; j < end; j += block_size) {
-        float xi = x[j] - mean;
-        dst[j] = xi;
-        tmp += xi * xi;
-    }
-
-    tmp = warp_reduce_sum(tmp, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        /*
-        DPCT1118:2: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        /*
-        DPCT1065:55: Consider replacing sycl::nd_item::barrier() with
-        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
-        better performance if there is no access to global memory.
-        */
-        item_ct1.barrier();
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp, item_ct1);
-    }
-
-    float variance = tmp / group_size;
-    float scale = sycl::rsqrt(variance + eps);
-    for (int j = start; j < end; j += block_size) {
-        dst[j] *= scale;
-    }
-}
-
-static void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps,
-                         const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) {
-    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
-                    item_ct1.get_local_id(1);
-    const int tid = item_ct1.get_local_id(2);
-
-    float tmp = 0.0f; // partial sum for thread in warp
-
-    for (int col = tid; col < ncols; col += block_size) {
-        const float xi = x[row*ncols + col];
-        tmp += xi * xi;
-    }
-
-    // sum up partial sums
-    tmp = warp_reduce_sum(tmp, item_ct1);
-    if (block_size > WARP_SIZE) {
-
-        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
-        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
-        if (lane_id == 0) {
-            s_sum[warp_id] = tmp;
-        }
-        /*
-        DPCT1118:3: SYCL group functions and algorithms must be encountered in
-        converged control flow. You may need to adjust the code.
-        */
-        item_ct1.barrier(sycl::access::fence_space::local_space);
-        tmp = s_sum[lane_id];
-        tmp = warp_reduce_sum(tmp, item_ct1);
-    }
-
-    const float mean = tmp / ncols;
-    const float scale = sycl::rsqrt(mean + eps);
-
-    for (int col = tid; col < ncols; col += block_size) {
-        dst[row*ncols + col] = scale * x[row*ncols + col];
-    }
-}
-
+template<int QUANT_BLOCK_TILE>
 static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded,
                           const sycl::nd_item<3> &item_ct1) {
-    const int ix = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                   item_ct1.get_local_id(2);
+    const int ix = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2)) * QUANT_BLOCK_TILE;
 
     if (ix >= kx_padded) {
         return;
@@ -578,23 +377,39 @@ static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy,
 
     const int ib = i_padded / QK8_1; // block index
     const int iqs = i_padded % QK8_1; // quant index
-
-    const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
-    float amax = sycl::fabs((float)xi);
-    float sum = xi;
-
+    typedef  sycl::vec<float, QUANT_BLOCK_TILE> TC;
+    typedef  sycl::vec<int8_t, QUANT_BLOCK_TILE> TQ;
+    TC zeros;
+    TQ qzeros;
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
-        amax = sycl::fmax(amax, dpct::permute_sub_group_by_xor(
-                                    item_ct1.get_sub_group(), amax, mask));
-        sum +=
-            dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), sum, mask);
+    for (int i = 0; i < QUANT_BLOCK_TILE; i++)
+    {
+        zeros[i] = 0.f;
+        qzeros[i] = 0;
     }
+    const TC xi = ix < kx ? *(TC *)&x[iy * kx + ix] : zeros;
+    float sum = xi[0];
+    float amax = sycl::fabs(xi[0]);
+#pragma unroll
+    for (int i = 1; i < QUANT_BLOCK_TILE; i++)
+    {
+        sum += xi[i];
+        amax = sycl::fmax(sycl::fabs(xi[i]), amax);
+    }
+    sum = warp_reduce_sum(sum, item_ct1);
+    amax = warp_reduce_max(amax, item_ct1);
 
     const float d = amax / 127;
-    const int8_t q = amax == 0.0f ? 0 : sycl::round(xi / d);
+    TQ q = qzeros;
+    if (amax != 0.0f)
+    {
+#pragma unroll
+        for (int i = 0; i < QUANT_BLOCK_TILE; i++) {
+            q[i] = sycl::round(xi[i] / d);
+        }
+    }
 
-    y[ib].qs[iqs] = q;
+    *(TQ *)&y[ib].qs[iqs] = q;
 
     if (iqs > 0) {
         return;
@@ -728,7 +543,7 @@ static void mul_mat_p021_f16_f32(
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -781,7 +596,7 @@ static void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -978,114 +793,6 @@ static void cpy_f32_q(const char * cx, char * cdst, const int ne,
     cpy_blck(cx + x_offset, cdst + dst_offset);
 }
 
-static float rope_yarn_ramp(const float low, const float high, const int i0) {
-    const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low);
-    return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y));
-}
-
-struct rope_corr_dims {
-    float v[4];
-};
-
-// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
-// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
-static void rope_yarn(
-    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
-    float * cos_theta, float * sin_theta
-) {
-    // Get n-d rotational scaling corrected for extrapolation
-    float theta_interp = freq_scale * theta_extrap;
-    float theta = theta_interp;
-    if (ext_factor != 0.0f) {
-        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
-        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
-
-        // Get n-d magnitude scaling corrected for interpolation
-        mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale);
-    }
-    *cos_theta = sycl::cos(theta) * mscale;
-    *sin_theta = sycl::sin(theta) * mscale;
-}
-
-// rope == RoPE == rotary positional embedding
-template<typename T, bool has_pos>
-static void rope(
-    const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims
-,
-    const sycl::nd_item<3> &item_ct1) {
-    const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                         item_ct1.get_local_id(1));
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                    item_ct1.get_local_id(2);
-    const int i = row*ncols + col;
-    const int i2 = row/p_delta_rows;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float theta_base = p * dpct::pow(freq_base, -float(col) / ncols);
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + 1];
-
-    dst[i + 0] = x0*cos_theta - x1*sin_theta;
-    dst[i + 1] = x0*sin_theta + x1*cos_theta;
-}
-
-template<typename T, bool has_pos, bool has_freq_facs>
-static void rope_neox(
-    const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
-    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims,
-    const float * freq_factors, const sycl::nd_item<3> &item_ct1) {
-    const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
-                         item_ct1.get_local_id(1));
-
-    if (col >= ncols) {
-        return;
-    }
-
-    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
-                    item_ct1.get_local_id(2);
-    const int ib = col / n_dims;
-    const int ic = col % n_dims;
-
-    if (ib > 0) {
-        const int i = row*ncols + ib*n_dims + ic;
-
-        dst[i + 0] = x[i + 0];
-        dst[i + 1] = x[i + 1];
-
-        return;
-    }
-
-    const int i  = row*ncols + ib*n_dims + ic/2;
-    const int i2 = row/p_delta_rows;
-
-    float cur_rot = inv_ndims * ic - ib;
-
-    const int p = has_pos ? pos[i2] : 0;
-    const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f;
-
-    const float theta_base =
-        p * freq_scale * dpct::pow(theta_scale, col / 2.0f)/freq_factor;
-
-    float cos_theta, sin_theta;
-    rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
-
-    const float x0 = x[i + 0];
-    const float x1 = x[i + n_dims/2];
-
-    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
-    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
-}
-
 static void k_sum_rows_f32(const float * x, float * dst, const int ncols,
                            const sycl::nd_item<3> &item_ct1) {
     const int row = item_ct1.get_group(1);
@@ -1751,99 +1458,6 @@ static void sqr_f32_sycl(const float *x, float *dst, const int k,
         });
 }
 
-static void norm_f32_sycl(const float *x, float *dst, const int ncols,
-                          const int nrows, const float eps,
-                          queue_ptr stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
-    if (ncols < 1024) {
-        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
-                sycl::range<1>(32), cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        norm_f32(x, dst, ncols, eps, item_ct1,
-                                            s_sum_acc_ct1.get_pointer(), WARP_SIZE);
-                    });
-        });
-    } else {
-        const int work_group_size = get_work_group_size(stream->get_device());
-        const sycl::range<3> block_dims(1, 1, work_group_size);
-        /*
-        DPCT1049:17: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
-                sycl::range<1>(32), cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        norm_f32(x, dst, ncols, eps, item_ct1,
-                                       s_sum_acc_ct1.get_pointer(), work_group_size);
-                    });
-        });
-    }
-}
-
-static void group_norm_f32_sycl(const float *x, float *dst,
-                                const int num_groups, const int group_size,
-                                const int ne_elements, queue_ptr stream) {
-    static const float eps = 1e-6f;
-    if (group_size < 1024) {
-        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
-                                                         cgh);
-
-            const float eps_ct4 = eps;
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        group_norm_f32(
-                            x, dst, group_size, ne_elements, eps_ct4, item_ct1,
-                            s_sum_acc_ct1.get_pointer(), WARP_SIZE);
-                    });
-        });
-    } else {
-        const int work_group_size = get_work_group_size(stream->get_device());
-        const sycl::range<3> block_dims(1, 1, work_group_size);
-        /*
-        DPCT1049:18: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
-                                                         cgh);
-
-            const float eps_ct4 = eps;
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        group_norm_f32(x, dst, group_size, ne_elements,
-                                             eps_ct4, item_ct1,
-                                             s_sum_acc_ct1.get_pointer(), work_group_size);
-                    });
-        });
-    }
-}
-
 static void concat_f32_sycl(const float *x, const float *y, float *dst,
                             const int ne0, int ne1, int ne2, int ne02,
                             queue_ptr stream) {
@@ -1885,64 +1499,22 @@ static void pad_f32_sycl(const float *x, float *dst, const int ne00,
         });
 }
 
-static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols,
-                              const int nrows, const float eps,
-                              queue_ptr stream) {
-    GGML_ASSERT(ncols % WARP_SIZE == 0);
-    // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
-    if (ncols < 1024) {
-        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
-                                                         cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        rms_norm_f32(x, dst, ncols, eps, item_ct1,
-                                                s_sum_acc_ct1.get_pointer(), WARP_SIZE);
-                    });
-        });
-    } else {
-        const int work_group_size = get_work_group_size(stream->get_device());
-        const sycl::range<3> block_dims(1, 1, work_group_size);
-        /*
-        DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        stream->submit([&](sycl::handler &cgh) {
-            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
-                                                         cgh);
-
-            cgh.parallel_for(
-                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
-                                  block_dims),
-                [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        rms_norm_f32(x, dst, ncols, eps, item_ct1,
-                                           s_sum_acc_ct1.get_pointer(), work_group_size);
-                    });
-        });
-    }
-}
-
 static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx,
                                    const int ky, const int kx_padded,
                                    queue_ptr stream) {
     const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE;
     const sycl::range<3> num_blocks(1, ky, block_num_x);
-    const sycl::range<3> block_size(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE);
+    int constexpr QUANT_BLOCK_TILE = QK8_1 / WARP_SIZE;
+    static_assert(QK8_1 % WARP_SIZE == 0);
+    const sycl::range<3> block_size(1, 1, SYCL_QUANTIZE_BLOCK_SIZE / QUANT_BLOCK_TILE);
     {
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
         stream->parallel_for(
             sycl::nd_range<3>(num_blocks * block_size, block_size),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
-                quantize_q8_1(x, vy, kx, kx_padded, item_ct1);
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                quantize_q8_1<QUANT_BLOCK_TILE>(x, vy, kx, kx_padded, item_ct1);
             });
     }
 }
@@ -1962,7 +1534,7 @@ static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y,
 
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 mul_mat_p021_f16_f32(vx, y, dst, ncols_x, nrows_x, nchannels_x,
                                      nchannels_y, item_ct1);
             });
@@ -1982,7 +1554,7 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl(
 
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 mul_mat_vec_nc_f16_f32(vx, y, dst, ncols_x, nrows_x,
                                        row_stride_x, channel_stride_x,
                                        nchannels_y / nchannels_x, item_ct1);
@@ -2241,117 +1813,13 @@ static void clamp_f32_sycl(const float *x, float *dst, const float min,
         });
 }
 
-template <typename T>
-static void rope_sycl(const T *x, T *dst, int ncols, int nrows,
-                      const int32_t *pos, float freq_scale, int p_delta_rows,
-                      float freq_base, float ext_factor, float attn_factor,
-                      rope_corr_dims corr_dims, queue_ptr stream) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
-    const sycl::range<3> block_nums(1, num_blocks_x, nrows);
-    if (pos == nullptr) {
-        /*
-        DPCT1049:40: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                rope<T, false>(x, dst, ncols, pos, freq_scale, p_delta_rows,
-                               freq_base, ext_factor, attn_factor, corr_dims,
-                               item_ct1);
-            });
-    } else {
-        /*
-        DPCT1049:41: The work-group size passed to the SYCL kernel may exceed
-        the limit. To get the device limit, query
-        info::device::max_work_group_size. Adjust the work-group size if needed.
-        */
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        stream->parallel_for(
-            sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) {
-                rope<T, true>(x, dst, ncols, pos, freq_scale, p_delta_rows,
-                              freq_base, ext_factor, attn_factor, corr_dims,
-                              item_ct1);
-            });
-    }
-}
-
-template <typename T>
-static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
-                           const int32_t *pos, float freq_scale,
-                           int p_delta_rows, float freq_base, float ext_factor,
-                           float attn_factor, rope_corr_dims corr_dims,
-                           const float * freq_factors, queue_ptr stream) {
-    GGML_ASSERT(ncols % 2 == 0);
-    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
-    const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
-    const sycl::range<3> block_nums(1, num_blocks_x, nrows);
-
-    const float theta_scale = powf(freq_base, -2.0f/n_dims);
-    const float inv_ndims = -1.0f / n_dims;
-
-    if (pos == nullptr) {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-        if (freq_factors == nullptr) {
-            stream->parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    rope_neox<T, false, false>(x, dst, ncols, n_dims, pos, freq_scale,
-                                        p_delta_rows, ext_factor, attn_factor,
-                                        corr_dims, theta_scale, inv_ndims, freq_factors,
-                                        item_ct1);
-                });
-        } else {
-            stream->parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    rope_neox<T, false, true>(x, dst, ncols, n_dims, pos, freq_scale,
-                                        p_delta_rows, ext_factor, attn_factor,
-                                        corr_dims, theta_scale, inv_ndims, freq_factors,
-                                        item_ct1);
-                });
-        }
-    } else {
-        dpct::has_capability_or_fail(stream->get_device(),
-                                     {sycl::aspect::fp16});
-
-        if (freq_factors == nullptr) {
-            stream->parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    rope_neox<T, true, false>(x, dst, ncols, n_dims, pos, freq_scale,
-                                       p_delta_rows, ext_factor, attn_factor,
-                                       corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
-                });
-        } else {
-            stream->parallel_for(
-                sycl::nd_range<3>(block_nums * block_dims, block_dims),
-                [=](sycl::nd_item<3> item_ct1) {
-                    rope_neox<T, true, true>(x, dst, ncols, n_dims, pos, freq_scale,
-                                       p_delta_rows, ext_factor, attn_factor,
-                                       corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
-                });
-        }
-    }
-}
-
 static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols,
                               const int nrows, queue_ptr stream) {
     const sycl::range<3> block_dims(1, 1, WARP_SIZE);
     const sycl::range<3> block_nums(1, nrows, 1);
     stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
                          [=](sycl::nd_item<3> item_ct1)
-                             [[intel::reqd_sub_group_size(32)]] {
+                             [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                                  k_sum_rows_f32(x, dst, ncols, item_ct1);
                              });
 }
@@ -2432,7 +1900,7 @@ static void soft_max_f32_submitter(const float * x, const float * mask, float *
 
         cgh.parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
                                                                              nrows_y, scale, max_bias, m0,
                                                                              m1, n_head_log2, item_ct1,
@@ -2612,12 +2080,6 @@ static inline int get_sycl_env(const char *env_name, int default_val) {
     return user_number;
 }
 
-static inline int get_work_group_size(const sycl::device& device) {
-    dpct::device_info prop;
-    dpct::get_device_info(prop, device);
-    return prop.get_max_work_group_size();
-}
-
 static void ggml_check_sycl() try {
     static bool initialized = false;
 
@@ -3176,45 +2638,6 @@ inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor
     (void) src1_dd;
 }
 
-inline void ggml_sycl_op_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
-                              ggml_tensor *dst, const float *src0_dd,
-                              const float *src1_dd, float *dst_dd,
-                              const queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
-    norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
-inline void ggml_sycl_op_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-                                    const ggml_tensor *src1, ggml_tensor *dst,
-                                    const float *src0_dd, const float *src1_dd,
-                                    float *dst_dd,
-                                    const queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    int num_groups = dst->op_params[0];
-    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
-    group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
 inline void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
                                 const ggml_tensor *src1, ggml_tensor *dst,
                                 const float *src0_dd, const float *src1_dd,
@@ -3278,28 +2701,6 @@ inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor
     (void) src1_dd;
 }
 
-inline void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
-                                  const ggml_tensor *src1, ggml_tensor *dst,
-                                  const float *src0_dd, const float *src1_dd,
-                                  float *dst_dd,
-                                  const queue_ptr &main_stream) {
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t nrows = ggml_nrows(src0);
-
-    float eps;
-    memcpy(&eps, dst->op_params, sizeof(float));
-
-    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
 static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split) {
     int64_t min_compute_capability = INT_MAX;
     int64_t max_compute_capability = INT_MIN;
@@ -3461,97 +2862,6 @@ catch (sycl::exception const &exc) {
   std::exit(1);
 }
 
-inline void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
-                              ggml_tensor *dst, const float *src0_dd,
-                              const float *src1_dd, float *dst_dd,
-                              const queue_ptr &main_stream) {
-    const ggml_tensor * src2 = dst->src[2];
-
-    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
-    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
-    GGML_ASSERT(src0->type == dst->type);
-
-    const int64_t ne00 = src0->ne[0];
-    const int64_t ne01 = src0->ne[1];
-    const int64_t ne2 = dst->ne[2];
-    const int64_t nrows = ggml_nrows(src0);
-
-    //const int n_past      = ((int32_t *) dst->op_params)[0];
-    const int n_dims      = ((int32_t *) dst->op_params)[1];
-    const int mode        = ((int32_t *) dst->op_params)[2];
-    //const int n_ctx       = ((int32_t *) dst->op_params)[3];
-    const int n_ctx_orig  = ((int32_t *) dst->op_params)[4];
-
-    // RoPE alteration for extended context
-    float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
-    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
-    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
-    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
-    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
-    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
-    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
-
-    const float * freq_factors = nullptr;
-    const int32_t * pos = nullptr;
-    if ((mode & 1) == 0) {
-        GGML_ASSERT(src1->type == GGML_TYPE_I32);
-        GGML_ASSERT(src1->ne[0] == ne2);
-        pos = (const int32_t *) src1_dd;
-    }
-
-    const bool is_neox = mode & 2;
-
-#pragma message("TODO: update rope NORM mode to match NEOX mode")
-#pragma message("      https://github.com/ggerganov/llama.cpp/pull/7634")
-
-    if (is_neox) {
-        pos = (const int32_t *) src1_dd;
-
-        if (src2 != nullptr) {
-            freq_factors = (const float *) src2->data;
-        }
-    } else {
-        GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox");
-    }
-
-    rope_corr_dims corr_dims;
-    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v);
-
-    // compute
-    if (is_neox) {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_neox_sycl(
-                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, freq_factors, main_stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd,
-                           ne00, n_dims, nrows, pos, freq_scale, ne01,
-                           freq_base, ext_factor, attn_factor, corr_dims,
-                           freq_factors, main_stream);
-        } else {
-            GGML_ASSERT(false);
-        }
-    } else {
-        if (src0->type == GGML_TYPE_F32) {
-            rope_sycl(
-                (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                attn_factor, corr_dims, main_stream
-            );
-        } else if (src0->type == GGML_TYPE_F16) {
-            rope_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00,
-                      nrows, pos, freq_scale, ne01, freq_base, ext_factor,
-                      attn_factor, corr_dims, main_stream);
-        } else {
-            GGML_ASSERT(false);
-        }
-    }
-
-    (void) src1;
-    (void) dst;
-    (void) src1_dd;
-}
-
 static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
                                 const ggml_tensor *src1, ggml_tensor *dst,
                                 const float *src0_dd, const float *src1_dd,
@@ -4576,7 +3886,6 @@ bool ggml_sycl_supports_dmmv(enum ggml_type type) {
 
 static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer);
-
     int64_t min_compute_capability = INT_MAX;
 
     if (split) {
@@ -6241,7 +5550,9 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
         case GGML_OP_CONT:
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_SOFT_MAX:
+            return true;
         case GGML_OP_ROPE:
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_IM2COL:
         case GGML_OP_POOL_2D:
         case GGML_OP_SUM_ROWS:
diff --git a/ggml/src/ggml-sycl/backend.hpp b/ggml/src/ggml-sycl/backend.hpp
index 2d37e271f..3afa33919 100644
--- a/ggml/src/ggml-sycl/backend.hpp
+++ b/ggml/src/ggml-sycl/backend.hpp
@@ -19,5 +19,7 @@
 #include "dmmv.hpp"
 #include "mmq.hpp"
 #include "mmvq.hpp"
+#include "rope.hpp"
+#include "norm.hpp"
 
 #endif // GGML_SYCL_BACKEND_HPP
diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp
index e01f91633..476d847ca 100644
--- a/ggml/src/ggml-sycl/common.hpp
+++ b/ggml/src/ggml-sycl/common.hpp
@@ -295,5 +295,66 @@ struct ggml_backend_sycl_context {
     }
 };
 
+// common host functions
+
+static inline int get_work_group_size(const sycl::device& device) {
+    dpct::device_info prop;
+    dpct::get_device_info(prop, device);
+    return prop.get_max_work_group_size();
+}
+
+
+// common device functions
+
+static __dpct_inline__ float warp_reduce_sum(float x,
+    const sycl::nd_item<3>& item_ct1) {
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        /*
+        DPCT1096:98: The right-most dimension of the work-group used in the SYCL
+        kernel that calls this function may be less than "32". The function
+        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
+        CPU device. Modify the size of the work-group to ensure that the value
+        of the right-most dimension is a multiple of "32".
+        */
+        x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask);
+    }
+    return x;
+}
+
+static __dpct_inline__ sycl::float2
+warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3>& item_ct1) {
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(),
+            mask);
+        a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(),
+            mask);
+    }
+    return a;
+}
+
+static __dpct_inline__ float warp_reduce_max(float x,
+    const sycl::nd_item<3>& item_ct1) {
+#pragma unroll
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
+        /*
+        DPCT1096:97: The right-most dimension of the work-group used in the SYCL
+        kernel that calls this function may be less than "32". The function
+        "dpct::permute_sub_group_by_xor" may return an unexpected result on the
+        CPU device. Modify the size of the work-group to ensure that the value
+        of the right-most dimension is a multiple of "32".
+        */
+        x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
+            item_ct1.get_sub_group(), x, mask));
+    }
+    return x;
+}
+
+// Helper for vec loading aligned data
+template <typename Tp, int n>
+inline sycl::vec<Tp, n> vec_aligned_load(const Tp* aligned_ptr) {
+    return *reinterpret_cast<const sycl::vec<Tp, n>*>(aligned_ptr);
+}
 
 #endif // GGML_SYCL_COMMON_HPP
diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp
index ce9de2b42..a15271b51 100644
--- a/ggml/src/ggml-sycl/convert.cpp
+++ b/ggml/src/ggml-sycl/convert.cpp
@@ -152,12 +152,15 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k,
         dpct::has_capability_or_fail(stream->get_device(),
                                      {sycl::aspect::fp16});
 
-        stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
+        stream->submit([&](sycl::handler &cgh) {
+            sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(12), cgh);
+            cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
                                                    sycl::range<3>(1, 1, 32),
                                                sycl::range<3>(1, 1, 32)),
                              [=](sycl::nd_item<3> item_ct1) {
-                                 dequantize_block_q4_K(vx, y, item_ct1);
+                                 dequantize_block_q4_K(vx, y, scale_local_acc.get_pointer(), item_ct1);
                              });
+        });
     }
 }
 
diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp
index b6080d83a..ed8ad098b 100644
--- a/ggml/src/ggml-sycl/dequantize.hpp
+++ b/ggml/src/ggml-sycl/dequantize.hpp
@@ -293,7 +293,8 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri
 #if QK_K == 256
 static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
     if (j < 4) {
-        d = q[j] & 63; m = q[j + 4] & 63;
+        d = q[j] & 63;
+        m = q[j + 4] & 63;
     } else {
         d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
         m = (q[j+4] >>  4) | ((q[j-0] >> 6) << 4);
@@ -303,7 +304,7 @@ static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8
 
 template<typename dst_t>
 static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
-                                  const sycl::nd_item<3> &item_ct1) {
+                                  uint8_t* scales_local, const sycl::nd_item<3> &item_ct1) {
     const block_q4_K * x = (const block_q4_K *) vx;
 
     const int i = item_ct1.get_group(2);
@@ -318,19 +319,26 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri
 
     dst_t * y = yy + i*QK_K + 64*il + n*ir;
 
-    const float dall = x[i].dm[0];
-    const float dmin = x[i].dm[1];
+    const sycl::half2 dm = x[i].dm;
+    const float dall = dm[0];
+    const float dmin = dm[1];
 
-    const uint8_t * q = x[i].qs + 32*il + n*ir;
+    if (tid < 12)
+        scales_local[tid] = x[i].scales[tid];
+    item_ct1.barrier(sycl::access::fence_space::local_space);
 
     uint8_t sc, m;
-    get_scale_min_k4(is + 0, x[i].scales, sc, m);
-    const float d1 = dall * sc; const float m1 = dmin * m;
-    get_scale_min_k4(is + 1, x[i].scales, sc, m);
-    const float d2 = dall * sc; const float m2 = dmin * m;
+    get_scale_min_k4(is + 0, scales_local, sc, m);
+    const float d1 = dall * sc;
+    const float m1 = dmin * m;
+    get_scale_min_k4(is + 1, scales_local, sc, m);
+    const float d2 = dall * sc;
+    const float m2 = dmin * m;
+
+    sycl::vec<uint8_t, n> q_vec = vec_aligned_load<uint8_t, n>(x[i].qs + 32*il + n*ir);
     for (int l = 0; l < n; ++l) {
-        y[l + 0] = d1 * (q[l] & 0xF) - m1;
-        y[l +32] = d2 * (q[l] >>  4) - m2;
+        y[l + 0] = d1 * (q_vec[l] & 0xF) - m1;
+        y[l +32] = d2 * (q_vec[l] >>  4) - m2;
     }
 #else
     const int tid = item_ct1.get_local_id(2);
diff --git a/ggml/src/ggml-sycl/dmmv.cpp b/ggml/src/ggml-sycl/dmmv.cpp
index 3a87d3ef8..927819281 100644
--- a/ggml/src/ggml-sycl/dmmv.cpp
+++ b/ggml/src/ggml-sycl/dmmv.cpp
@@ -76,7 +76,7 @@ static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat *
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -104,7 +104,7 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
 
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols,
                                                           nrows, item_ct1);
             });
@@ -227,7 +227,7 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -346,7 +346,7 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -499,7 +499,7 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -633,7 +633,7 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -748,7 +748,7 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -774,7 +774,7 @@ static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
 
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(
                     vx, y, dst, ncols, nrows, item_ct1);
             });
@@ -795,7 +795,7 @@ static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
 
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(
                     vx, y, dst, ncols, nrows, item_ct1);
             });
@@ -816,7 +816,7 @@ static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y,
 
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(
                     vx, y, dst, ncols, nrows, item_ct1);
             });
@@ -837,7 +837,7 @@ static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y,
 
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(
                     vx, y, dst, ncols, nrows, item_ct1);
             });
@@ -858,7 +858,7 @@ static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y,
 
         stream->parallel_for(
             sycl::nd_range<3>(block_nums * block_dims, block_dims),
-            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+            [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                 dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(
                     vx, y, dst, ncols, nrows, item_ct1);
             });
@@ -873,10 +873,10 @@ static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y,
     const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
     const int block_num_y = (nrows + ny - 1) / ny;
     const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, ny, 32);
+    const sycl::range<3> block_dims(1, ny, WARP_SIZE);
     stream->parallel_for(
         sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
             dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
         });
 }
@@ -889,10 +889,10 @@ static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
     const int ny = 2 / K_QUANTS_PER_ITERATION;
     const int block_num_y = (nrows + ny - 1) / ny;
     const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, ny, 32);
+    const sycl::range<3> block_dims(1, ny, WARP_SIZE);
     stream->parallel_for(
         sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
             dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
         });
 }
@@ -905,10 +905,10 @@ static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
     const int ny = 2 / K_QUANTS_PER_ITERATION;
     const int block_num_y = (nrows + ny - 1) / ny;
     const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, ny, 32);
+    const sycl::range<3> block_dims(1, ny, WARP_SIZE);
     stream->parallel_for(
         sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
             dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
         });
 }
@@ -918,10 +918,10 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
                                              const int nrows,
                                              dpct::queue_ptr stream) {
     GGML_ASSERT(ncols % QK_K == 0);
-    const sycl::range<3> block_dims(1, 1, 32);
+    const sycl::range<3> block_dims(1, 1, WARP_SIZE);
     stream->parallel_for(
         sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
             dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1);
         });
 }
@@ -934,10 +934,10 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
     const int ny = 2 / K_QUANTS_PER_ITERATION;
     const int block_num_y = (nrows + ny - 1) / ny;
     const sycl::range<3> block_nums(1, 1, block_num_y);
-    const sycl::range<3> block_dims(1, ny, 32);
+    const sycl::range<3> block_dims(1, ny, WARP_SIZE);
     stream->parallel_for(
         sycl::nd_range<3>(block_nums * block_dims, block_dims),
-        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
+        [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
             dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
         });
 }
diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp
index 1ff297218..5e98660dc 100644
--- a/ggml/src/ggml-sycl/dpct/helper.hpp
+++ b/ggml/src/ggml-sycl/dpct/helper.hpp
@@ -255,7 +255,7 @@ namespace dpct
         void set_pitch(size_t pitch) { _pitch = pitch; }
 
         size_t get_x() { return _x; }
-        void set_x(size_t x) { _x = x; };
+        void set_x(size_t x) { _x = x; }
 
         size_t get_y() { return _y; }
         void set_y(size_t y) { _y = y; }
@@ -1056,7 +1056,7 @@ namespace dpct
 #error "Only support Windows and Linux."
 #endif
                 next_free = mapped_address_space;
-            };
+            }
 
         public:
             using buffer_id_t = int;
@@ -1077,7 +1077,7 @@ namespace dpct
 #else
 #error "Only support Windows and Linux."
 #endif
-            };
+            }
 
             mem_mgr(const mem_mgr &) = delete;
             mem_mgr &operator=(const mem_mgr &) = delete;
diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp
index 23227649e..3fbc4dd60 100644
--- a/ggml/src/ggml-sycl/mmvq.cpp
+++ b/ggml/src/ggml-sycl/mmvq.cpp
@@ -37,7 +37,7 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -85,7 +85,7 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -133,7 +133,7 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -181,7 +181,7 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -229,7 +229,7 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -277,7 +277,7 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -325,7 +325,7 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -373,7 +373,7 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -421,7 +421,7 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -470,7 +470,7 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx,
 
     // sum up partial sums and write back result
 #pragma unroll
-    for (int mask = 16; mask > 0; mask >>= 1) {
+    for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
         tmp +=
             dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
     }
@@ -495,7 +495,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK4_0, QI4_0, block_q4_0,
                                       VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -519,7 +519,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
                                       VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -543,7 +543,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
                                       VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -567,7 +567,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
                                       VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -591,7 +591,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
                                       VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -615,7 +615,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
                                       VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -639,7 +639,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
                                       VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -663,7 +663,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
                                       VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -687,7 +687,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
                                       VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -711,7 +711,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
                                       VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
@@ -734,8 +734,8 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS, block_iq2_xxs, 1>(
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS/2, block_iq2_xxs, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
         });
@@ -759,8 +759,8 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS, block_iq2_xs, 1>(
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS/2, block_iq2_xs, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
         });
@@ -784,8 +784,8 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S, block_iq2_s, 1>(
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S/2, block_iq2_s, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
         });
@@ -809,8 +809,8 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS, block_iq3_xxs, 1>(
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS/2, block_iq3_xxs, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
         });
@@ -833,8 +833,8 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_XS, block_iq3_s, 1>(
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S/2, block_iq3_s, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
         });
@@ -858,7 +858,7 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
@@ -879,7 +879,7 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
@@ -901,7 +901,7 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
                         mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
@@ -923,8 +923,8 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
             cgh.parallel_for(
                 sycl::nd_range<3>(block_nums * block_dims, block_dims),
                 [=](sycl::nd_item<3> item_ct1)
-                    [[intel::reqd_sub_group_size(32)]] {
-                        mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS, block_iq4_xs, 1>(
+                    [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                        mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS/4, block_iq4_xs, 1>(
                             vx, vy, dst, ncols, nrows, item_ct1);
                     });
         });
@@ -936,7 +936,7 @@ void ggml_sycl_op_mul_mat_vec_q(
     const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
     const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
     float *dst_dd_i, const int64_t row_low, const int64_t row_high,
-    const int64_t src1_ncols, const int64_t src1_padded_row_size,
+    const int64_t src1_ncols, const int64_t src1_padded_col_size,
     const dpct::queue_ptr &stream) {
 
     const int64_t ne10 = src1->ne[0];
@@ -948,77 +948,80 @@ void ggml_sycl_op_mul_mat_vec_q(
     int id;
     SYCL_CHECK(
         CHECK_TRY_ERROR(id = get_current_device_id()));
-
+    const size_t q8_1_ts = sizeof(block_q8_1);
+    const size_t q8_1_bs = QK8_1;
     // the main device has a larger memory buffer to hold the results from all GPUs
     // nrows_dst == nrows of the matrix that the kernel writes into
     const int64_t nrows_dst = id == ctx.device ? ne00 : row_diff;
-
-    switch (src0->type) {
+    for (int i = 0; i < src1_ncols; i++)
+    {
+        const size_t src1_ddq_i_offset = i * src1_padded_col_size * q8_1_ts / q8_1_bs;
+        const char* src1_ddq_i_bs = src1_ddq_i + src1_ddq_i_offset;
+        float* dst_dd_i_bs = dst_dd_i + i * dst->ne[0];
+        switch (src0->type) {
         case GGML_TYPE_Q4_0:
-            mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q4_1:
-            mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q5_0:
-            mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q5_1:
-            mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q8_0:
-            mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q2_K:
-            mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q3_K:
-            mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q4_K:
-            mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q5_K:
-            mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_Q6_K:
-            mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ1_S:
-            mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ1_M:
-            mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ2_XXS:
-            mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ2_XS:
-            mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ2_S:
-            mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ3_XXS:
-            mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ3_S:
-            mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ4_NL:
-            mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         case GGML_TYPE_IQ4_XS:
-            mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
+            mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
             break;
         default:
             GGML_ASSERT(false);
             break;
+        }
     }
-
     (void) src1;
     (void) dst;
     (void) src1_ddf_i;
-    (void) src1_ncols;
-    (void) src1_padded_row_size;
 }
diff --git a/ggml/src/ggml-sycl/norm.cpp b/ggml/src/ggml-sycl/norm.cpp
new file mode 100644
index 000000000..a77f7852c
--- /dev/null
+++ b/ggml/src/ggml-sycl/norm.cpp
@@ -0,0 +1,370 @@
+#include "norm.hpp"
+
+static void norm_f32(const float* x, float* dst, const int ncols, const float eps,
+    const sycl::nd_item<3>& item_ct1, sycl::float2* s_sum, int block_size) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+        item_ct1.get_local_id(1);
+    const int tid = item_ct1.get_local_id(2);
+
+    const int nthreads = item_ct1.get_local_range(2);
+    const int nwarps = nthreads / WARP_SIZE;
+    assert(nwarps % WARP_SIZE == 0);
+    sycl::float2 mean_var = sycl::float2(0.f, 0.f);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row * ncols + col];
+        mean_var.x() += xi;
+        mean_var.y() += xi * xi;
+    }
+
+    // sum up partial sums
+    mean_var = warp_reduce_sum(mean_var, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = mean_var;
+        }
+        /*
+        DPCT1118:0: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+        mean_var = 0.f;
+        int nreduce = nwarps / WARP_SIZE;
+        for (size_t i = 0; i < nreduce; i += 1)
+        {
+            mean_var += s_sum[lane_id + i * WARP_SIZE];
+        }
+        mean_var = warp_reduce_sum(mean_var, item_ct1);
+    }
+
+    const float mean = mean_var.x() / ncols;
+    const float var = mean_var.y() / ncols - mean * mean;
+    const float inv_std = sycl::rsqrt(var + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row * ncols + col] = (x[row * ncols + col] - mean) * inv_std;
+    }
+}
+
+static void group_norm_f32(const float* x, float* dst, const int group_size, const int ne_elements, const float eps,
+    const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
+    int start = item_ct1.get_group(2) * group_size;
+    int end = start + group_size;
+    const int nthreads = item_ct1.get_local_range(2);
+    const int nwarps = nthreads / WARP_SIZE;
+    assert(nwarps % WARP_SIZE == 0);
+    start += item_ct1.get_local_id(2);
+
+    if (end >= ne_elements) {
+        end = ne_elements;
+    }
+
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int j = start; j < end; j += block_size) {
+        tmp += x[j];
+    }
+
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:1: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        /*
+        DPCT1065:54: Consider replacing sycl::nd_item::barrier() with
+        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+        better performance if there is no access to global memory.
+        */
+        item_ct1.barrier();
+        tmp = 0.f;
+        int nreduce = nwarps / WARP_SIZE;
+        for (size_t i = 0; i < nreduce; i += 1)
+        {
+            tmp += s_sum[lane_id + i * WARP_SIZE];
+        }
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    float mean = tmp / group_size;
+    tmp = 0.0f;
+
+    for (int j = start; j < end; j += block_size) {
+        float xi = x[j] - mean;
+        dst[j] = xi;
+        tmp += xi * xi;
+    }
+
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:2: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        /*
+        DPCT1065:55: Consider replacing sycl::nd_item::barrier() with
+        sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
+        better performance if there is no access to global memory.
+        */
+        item_ct1.barrier();
+        tmp = s_sum[lane_id];
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    float variance = tmp / group_size;
+    float scale = sycl::rsqrt(variance + eps);
+    for (int j = start; j < end; j += block_size) {
+        dst[j] *= scale;
+    }
+}
+
+static void rms_norm_f32(const float* x, float* dst, const int ncols, const float eps,
+    const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
+    const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
+        item_ct1.get_local_id(1);
+    const int tid = item_ct1.get_local_id(2);
+    const int nthreads = item_ct1.get_local_range(2);
+    const int nwarps = nthreads / WARP_SIZE;
+    assert(nwarps % WARP_SIZE == 0);
+    float tmp = 0.0f; // partial sum for thread in warp
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[row * ncols + col];
+        tmp += xi * xi;
+    }
+
+    // sum up partial sums
+    tmp = warp_reduce_sum(tmp, item_ct1);
+    if (block_size > WARP_SIZE) {
+
+        int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
+        int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
+        if (lane_id == 0) {
+            s_sum[warp_id] = tmp;
+        }
+        /*
+        DPCT1118:3: SYCL group functions and algorithms must be encountered in
+        converged control flow. You may need to adjust the code.
+        */
+        item_ct1.barrier(sycl::access::fence_space::local_space);
+        int nreduce = nwarps / WARP_SIZE;
+        tmp = 0.f;
+        for (size_t i = 0; i < nreduce; i += 1)
+        {
+            tmp += s_sum[lane_id + i * WARP_SIZE];
+        }
+        tmp = warp_reduce_sum(tmp, item_ct1);
+    }
+
+    const float mean = tmp / ncols;
+    const float scale = sycl::rsqrt(mean + eps);
+
+    for (int col = tid; col < ncols; col += block_size) {
+        dst[row * ncols + col] = scale * x[row * ncols + col];
+    }
+}
+
+static void norm_f32_sycl(const float* x, float* dst, const int ncols,
+    const int nrows, const float eps,
+    queue_ptr stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    if (ncols < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler& cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    norm_f32(x, dst, ncols, eps, item_ct1,
+                        nullptr, WARP_SIZE);
+                });
+            });
+    }
+    else {
+        const int work_group_size = get_work_group_size(stream->get_device());
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:17: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->submit([&](sycl::handler& cgh) {
+            sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
+                sycl::range<1>(work_group_size / WARP_SIZE), cgh);
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    norm_f32(x, dst, ncols, eps, item_ct1,
+                        s_sum_acc_ct1.get_pointer(), work_group_size);
+                });
+            });
+    }
+}
+
+static void group_norm_f32_sycl(const float* x, float* dst,
+    const int num_groups, const int group_size,
+    const int ne_elements, queue_ptr stream) {
+    static const float eps = 1e-6f;
+    if (group_size < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler& cgh) {
+            const float eps_ct4 = eps;
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    group_norm_f32(
+                        x, dst, group_size, ne_elements, eps_ct4, item_ct1,
+                        nullptr, WARP_SIZE);
+                });
+            });
+    }
+    else {
+        const int work_group_size = get_work_group_size(stream->get_device());
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:18: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+
+        stream->submit([&](sycl::handler& cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
+                cgh);
+
+            const float eps_ct4 = eps;
+
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    group_norm_f32(x, dst, group_size, ne_elements,
+                        eps_ct4, item_ct1,
+                        s_sum_acc_ct1.get_pointer(), work_group_size);
+                });
+            });
+    }
+}
+
+static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols,
+    const int nrows, const float eps,
+    queue_ptr stream) {
+    GGML_ASSERT(ncols % WARP_SIZE == 0);
+    // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
+    if (ncols < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler& cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    rms_norm_f32(x, dst, ncols, eps, item_ct1,
+                        nullptr, WARP_SIZE);
+                });
+            });
+    }
+    else {
+        const int work_group_size = get_work_group_size(stream->get_device());
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        /*
+        DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->submit([&](sycl::handler& cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
+                cgh);
+            cgh.parallel_for(
+                sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
+                    block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[intel::reqd_sub_group_size(WARP_SIZE)]] {
+                    rms_norm_f32(x, dst, ncols, eps, item_ct1,
+                        s_sum_acc_ct1.get_pointer(), work_group_size);
+                });
+            });
+    }
+}
+
+void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1,
+    ggml_tensor* dst, const float* src0_dd,
+    const float* src1_dd, float* dst_dd,
+    const queue_ptr& main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
+
+    (void)src1;
+    (void)dst;
+    (void)src1_dd;
+}
+
+void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
+    const ggml_tensor* src1, ggml_tensor* dst,
+    const float* src0_dd, const float* src1_dd,
+    float* dst_dd,
+    const queue_ptr& main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    int num_groups = dst->op_params[0];
+    int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
+    group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
+
+    (void)src1;
+    (void)dst;
+    (void)src1_dd;
+}
+
+void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
+    const ggml_tensor* src1, ggml_tensor* dst,
+    const float* src0_dd, const float* src1_dd,
+    float* dst_dd,
+    const queue_ptr& main_stream) {
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t nrows = ggml_nrows(src0);
+
+    float eps;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
+
+    (void)src1;
+    (void)dst;
+    (void)src1_dd;
+}
diff --git a/ggml/src/ggml-sycl/norm.hpp b/ggml/src/ggml-sycl/norm.hpp
new file mode 100644
index 000000000..a9ad9156f
--- /dev/null
+++ b/ggml/src/ggml-sycl/norm.hpp
@@ -0,0 +1,35 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_NORM_HPP
+#define GGML_SYCL_NORM_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1,
+    ggml_tensor* dst, const float* src0_dd,
+    const float* src1_dd, float* dst_dd,
+    const queue_ptr& main_stream);
+
+void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
+    const ggml_tensor* src1, ggml_tensor* dst,
+    const float* src0_dd, const float* src1_dd,
+    float* dst_dd,
+    const queue_ptr& main_stream);
+
+void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
+    const ggml_tensor* src1, ggml_tensor* dst,
+    const float* src0_dd, const float* src1_dd,
+    float* dst_dd,
+    const queue_ptr& main_stream);
+
+#endif // GGML_SYCL_NORM_HPP
diff --git a/ggml/src/ggml-sycl/presets.hpp b/ggml/src/ggml-sycl/presets.hpp
index fe9d41770..c09c75dc7 100644
--- a/ggml/src/ggml-sycl/presets.hpp
+++ b/ggml/src/ggml-sycl/presets.hpp
@@ -16,7 +16,7 @@
 #define GGML_SYCL_MAX_STREAMS       8
 #define GGML_SYCL_MAX_BUFFERS       256
 
-#define WARP_SIZE 32
+#define WARP_SIZE GGML_SYCL_WARP_SIZE
 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
 
 #define SYCL_GELU_BLOCK_SIZE 256
diff --git a/ggml/src/ggml-sycl/rope.cpp b/ggml/src/ggml-sycl/rope.cpp
new file mode 100644
index 000000000..eabf1693e
--- /dev/null
+++ b/ggml/src/ggml-sycl/rope.cpp
@@ -0,0 +1,275 @@
+#include "rope.hpp"
+
+struct rope_corr_dims {
+    float v[2];
+};
+
+static float rope_yarn_ramp(const float low, const float high, const int i0) {
+    const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low);
+    return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y));
+}
+
+// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
+// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
+static void rope_yarn(
+    float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
+    float * cos_theta, float * sin_theta) {
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale);
+    }
+    *cos_theta = sycl::cos(theta) * mscale;
+    *sin_theta = sycl::sin(theta) * mscale;
+}
+
+template<typename T, bool has_ff>
+static void rope_norm(
+    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors,
+    const sycl::nd_item<3> &item_ct1) {
+    const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                         item_ct1.get_local_id(1));
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i = row*ne0 + i0;
+    const int i2 = row/p_delta_rows;
+
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+    float cos_theta;
+    float sin_theta;
+
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + 1];
+
+    dst[i + 0] = x0*cos_theta - x1*sin_theta;
+    dst[i + 1] = x0*sin_theta + x1*cos_theta;
+}
+
+template<typename T, bool has_ff>
+static void rope_neox(
+    const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
+    float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors,
+    const sycl::nd_item<3> &item_ct1) {
+    const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
+                         item_ct1.get_local_id(1));
+
+    if (i0 >= ne0) {
+        return;
+    }
+
+    const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
+                    item_ct1.get_local_id(2);
+
+    if (i0 >= n_dims) {
+        const int i = row*ne0 + i0;
+
+        dst[i + 0] = x[i + 0];
+        dst[i + 1] = x[i + 1];
+
+        return;
+    }
+
+    const int i  = row*ne0 + i0/2;
+    const int i2 = row/p_delta_rows;
+
+    const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
+
+    const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+    float cos_theta;
+    float sin_theta;
+
+    rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + n_dims/2];
+
+    dst[i + 0]        = x0*cos_theta - x1*sin_theta;
+    dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
+}
+
+template <typename T>
+static void rope_norm_sycl(
+    const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, num_blocks_x, nr);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    dpct::has_capability_or_fail(stream->get_device(),
+                                     {sycl::aspect::fp16});
+
+    if (freq_factors == nullptr) {
+        /*
+        DPCT1049:40: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_norm<T, false>(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows,
+                               ext_factor, attn_factor, corr_dims, theta_scale, freq_factors,
+                               item_ct1);
+            });
+    } else {
+        /*
+        DPCT1049:41: The work-group size passed to the SYCL kernel may exceed
+        the limit. To get the device limit, query
+        info::device::max_work_group_size. Adjust the work-group size if needed.
+        */
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_norm<T, true>(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows,
+                              ext_factor, attn_factor, corr_dims, theta_scale, freq_factors,
+                              item_ct1);
+            });
+    }
+}
+
+template <typename T>
+static void rope_neox_sycl(
+    const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows,
+    float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) {
+    GGML_ASSERT(ne0 % 2 == 0);
+    const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
+    const sycl::range<3> block_nums(1, num_blocks_x, nr);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    dpct::has_capability_or_fail(stream->get_device(),
+                                    {sycl::aspect::fp16});
+
+    if (freq_factors == nullptr) {
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_neox<T, false>(x, dst, ne0, n_dims, pos, freq_scale,
+                                    p_delta_rows, ext_factor, attn_factor,
+                                    corr_dims, theta_scale, freq_factors,
+                                    item_ct1);
+            });
+    } else {
+        stream->parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) {
+                rope_neox<T, true>(x, dst, ne0, n_dims, pos, freq_scale,
+                                    p_delta_rows, ext_factor, attn_factor,
+                                    corr_dims, theta_scale, freq_factors,
+                                    item_ct1);
+            });
+    }
+}
+
+void ggml_sycl_op_rope(
+    ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const float *src0_dd, const float *src1_dd, float *dst_dd, const queue_ptr &main_stream) {
+    const ggml_tensor * src2 = dst->src[2];
+
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
+    GGML_ASSERT( dst->type == GGML_TYPE_F32 ||  dst->type == GGML_TYPE_F16);
+    GGML_ASSERT(src0->type == dst->type);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t nr = ggml_nrows(src0);
+
+    //const int n_past      = ((int32_t *) dst->op_params)[0];
+    const int n_dims      = ((int32_t *) dst->op_params)[1];
+    const int mode        = ((int32_t *) dst->op_params)[2];
+    //const int n_ctx       = ((int32_t *) dst->op_params)[3];
+    const int n_ctx_orig  = ((int32_t *) dst->op_params)[4];
+
+    // RoPE alteration for extended context
+    float freq_base;
+    float freq_scale;
+    float ext_factor;
+    float attn_factor;
+    float beta_fast;
+    float beta_slow;
+
+    memcpy(&freq_base,   (int32_t *) dst->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (int32_t *) dst->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (int32_t *) dst->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (int32_t *) dst->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (int32_t *) dst->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (int32_t *) dst->op_params + 10, sizeof(float));
+
+    const bool is_neox = mode & 2;
+
+    const int32_t * pos = (const int32_t *) src1_dd;
+
+    const float * freq_factors = nullptr;
+    if (src2 != nullptr) {
+        freq_factors = (const float *) src2->data;
+    }
+
+    rope_corr_dims corr_dims;
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v);
+
+    // compute
+    if (is_neox) {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_neox_sycl(
+                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_neox_sycl(
+                (const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
+            );
+        } else {
+            GGML_ASSERT(false);
+        }
+    } else {
+        if (src0->type == GGML_TYPE_F32) {
+            rope_norm_sycl(
+                (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
+            );
+        } else if (src0->type == GGML_TYPE_F16) {
+            rope_norm_sycl(
+                (const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
+                attn_factor, corr_dims, freq_factors, main_stream
+            );
+        } else {
+            GGML_ASSERT(false);
+        }
+    }
+
+    (void) src1;
+    (void) dst;
+    (void) src1_dd;
+}
diff --git a/ggml/src/ggml-sycl/rope.hpp b/ggml/src/ggml-sycl/rope.hpp
new file mode 100644
index 000000000..00354c313
--- /dev/null
+++ b/ggml/src/ggml-sycl/rope.hpp
@@ -0,0 +1,22 @@
+//
+// MIT license
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: MIT
+//
+
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+
+#ifndef GGML_SYCL_ROPE_HPP
+#define GGML_SYCL_ROPE_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_rope(
+    ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
+    const float *src0_dd, const float *src1_dd, float *dst_dd, const queue_ptr &main_stream);
+
+#endif // GGML_SYCL_ROPE_HPP
diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp
index 5e2e82546..d2dccade2 100644
--- a/ggml/src/ggml-sycl/vecdotq.hpp
+++ b/ggml/src/ggml-sycl/vecdotq.hpp
@@ -820,7 +820,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq,
 #if QK_K == 256
     const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
 
-#if QR2_XXS == 8
     const int ib32 = iqs;
     const uint16_t * q2 = bq2->qs + 4*ib32;
     const uint8_t  * aux8 = (const uint8_t *)q2;
@@ -838,26 +837,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq,
     }
     const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f;
     return d * sumi;
-#else
-    // iqs is 0...15
-    const int ib32 = iqs/2;
-    const int il = iqs%2;
-    const uint16_t * q2 = bq2->qs + 4*ib32;
-    const uint8_t  * aux8 = (const uint8_t *)q2;
-    const uint8_t  * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
-    const uint8_t  * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
-    const uint32_t aux32 = q2[2] | (q2[3] << 16);
-    const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * bq8_1[ib32].ds[0] * 0.25f;
-    const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
-    const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
-    const int8_t * q8 = bq8_1[ib32].qs + 16*il;
-    int sumi1 = 0, sumi2 = 0;
-    for (int j = 0; j < 8; ++j) {
-        sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
-        sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
-    }
-    return d * (sumi1 + sumi2);
-#endif
 #else
     assert(false);
     return 0.f;
diff --git a/ggml/src/ggml-vulkan-shaders.hpp b/ggml/src/ggml-vulkan-shaders.hpp
index 01ff66f71..f0c4c6baf 100644
--- a/ggml/src/ggml-vulkan-shaders.hpp
+++ b/ggml/src/ggml-vulkan-shaders.hpp
@@ -144954,4 +144954,3 @@ unsigned char sum_rows_f32_data[] = {
 
 };
 const uint64_t sum_rows_f32_len = 2112;
-
diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c
index 06906134e..88e85def4 100644
--- a/ggml/src/ggml.c
+++ b/ggml/src/ggml.c
@@ -5312,7 +5312,7 @@ void ggml_mul_mat_set_prec(
     as  -> [cols, rows, n_expert]
     ids -> [n_experts_used, n_tokens] (i32)
     b   -> [cols, n_expert_used, n_tokens]
-    c   -> [cols, n_expert_used, n_tokens]
+    c   -> [rows, n_expert_used, n_tokens]
 
     in b, n_experts_used can be broadcasted to match the n_expert_used of ids
 
diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py
index 646dd85b5..4121fc086 100644
--- a/gguf-py/gguf/constants.py
+++ b/gguf-py/gguf/constants.py
@@ -66,6 +66,7 @@ class Keys:
         Q_LORA_RANK       = "{arch}.attention.q_lora_rank"
         KV_LORA_RANK      = "{arch}.attention.kv_lora_rank"
         REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
+        SLIDING_WINDOW    = "{arch}.attention.sliding_window"
 
     class Rope:
         DIMENSION_COUNT         = "{arch}.rope.dimension_count"
@@ -160,10 +161,12 @@ class MODEL_ARCH(IntEnum):
     COMMAND_R    = auto()
     DBRX         = auto()
     OLMO         = auto()
+    OPENELM      = auto()
     ARCTIC       = auto()
     DEEPSEEK2    = auto()
     BITNET       = auto()
     T5           = auto()
+    JAIS         = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -288,10 +291,12 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.COMMAND_R:      "command-r",
     MODEL_ARCH.DBRX:           "dbrx",
     MODEL_ARCH.OLMO:           "olmo",
+    MODEL_ARCH.OPENELM:        "openelm",
     MODEL_ARCH.ARCTIC:         "arctic",
     MODEL_ARCH.DEEPSEEK2:      "deepseek2",
     MODEL_ARCH.BITNET:         "bitnet",
     MODEL_ARCH.T5:             "t5",
+    MODEL_ARCH.JAIS:           "jais",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -894,6 +899,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.FFN_DOWN,
         MODEL_TENSOR.FFN_UP,
     ],
+    MODEL_ARCH.OPENELM: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_Q_NORM,
+        MODEL_TENSOR.ATTN_K_NORM,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_UP,
+    ],
     MODEL_ARCH.ARCTIC: [
         MODEL_TENSOR.TOKEN_EMBD,
         MODEL_TENSOR.OUTPUT_NORM,
@@ -989,6 +1007,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ENC_FFN_UP,
         MODEL_TENSOR.ENC_OUTPUT_NORM,
     ],
+    MODEL_ARCH.JAIS: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.FFN_NORM,
+        MODEL_TENSOR.FFN_DOWN,
+        MODEL_TENSOR.FFN_GATE,
+        MODEL_TENSOR.FFN_UP,
+    ],
     # TODO
 }
 
diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py
index 3e8d0283e..cf9554162 100644
--- a/gguf-py/gguf/gguf_writer.py
+++ b/gguf-py/gguf/gguf_writer.py
@@ -480,8 +480,11 @@ class GGUFWriter:
     def add_leading_dense_block_count(self, length: int) -> None:
         self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length)
 
-    def add_feed_forward_length(self, length: int) -> None:
-        self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+    def add_feed_forward_length(self, length: int | Sequence[int]) -> None:
+        if isinstance(length, int):
+            self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+        else:
+            self.add_array(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
 
     def add_expert_feed_forward_length(self, length: int) -> None:
         self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
@@ -495,8 +498,11 @@ class GGUFWriter:
     def add_decoder_start_token_id(self, id: int) -> None:
         self.add_uint32(Keys.LLM.DECODER_START_TOKEN_ID.format(arch=self.arch), id)
 
-    def add_head_count(self, count: int) -> None:
-        self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
+    def add_head_count(self, count: int | Sequence[int]) -> None:
+        if isinstance(count, int):
+            self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
+        else:
+            self.add_array(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
 
     def add_head_count_kv(self, count: int | Sequence[int]) -> None:
         if isinstance(count, int):
@@ -555,6 +561,9 @@ class GGUFWriter:
     def add_relative_attn_buckets_count(self, value: int) -> None:
         self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value)
 
+    def add_sliding_window(self, value: int) -> None:
+        self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value)
+
     def add_pooling_type(self, value: PoolingType) -> None:
         self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
 
diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py
index 0aee22de0..aa4fb5f6a 100644
--- a/gguf-py/gguf/tensor_mapping.py
+++ b/gguf-py/gguf/tensor_mapping.py
@@ -10,7 +10,7 @@ class TensorNameMap:
         # Token embeddings
         MODEL_TENSOR.TOKEN_EMBD: (
             "gpt_neox.embed_in",                         # gptneox
-            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen dbrx
+            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen dbrx jais
             "transformer.word_embeddings",               # falcon
             "word_embeddings",                           # bloom
             "model.embed_tokens",                        # llama-hf
@@ -24,6 +24,7 @@ class TensorNameMap:
             "backbone.embedding",                        # mamba
             "backbone.embeddings",                       # mamba-hf
             "transformer.in_out_embed",                  # Grok
+            "transformer.token_embeddings",              # openelm
             "shared",                                    # t5
         ),
 
@@ -37,6 +38,7 @@ class TensorNameMap:
             "word_embeddings_layernorm",  # bloom
             "embeddings.LayerNorm",       # bert
             "emb_ln",                     # nomic-bert
+            "transformer.norm",           # openelm
         ),
 
         # Position embeddings
@@ -49,7 +51,7 @@ class TensorNameMap:
         # Output
         MODEL_TENSOR.OUTPUT: (
             "embed_out",                 # gptneox
-            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
+            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais
             "output",                    # llama-pth bloom internlm2
             "word_embeddings_for_head",  # persimmon
             "lm_head.linear",            # phi2
@@ -58,7 +60,7 @@ class TensorNameMap:
         # Output norm
         MODEL_TENSOR.OUTPUT_NORM: (
             "gpt_neox.final_layer_norm",               # gptneox
-            "transformer.ln_f",                        # gpt2 gpt-j falcon
+            "transformer.ln_f",                        # gpt2 gpt-j falcon jais
             "model.norm",                              # llama-hf baichuan internlm2
             "norm",                                    # llama-pth
             "transformer.norm_f",                      # mpt dbrx
@@ -69,6 +71,7 @@ class TensorNameMap:
             "model.norm_f",                            # mamba-qbert
             "backbone.norm_f",                         # mamba
             "transformer.rms_norm",                    # Grok
+            "transformer.norm",                        # openelm
         ),
 
         # Rope frequencies
@@ -81,7 +84,7 @@ class TensorNameMap:
         # Attention norm
         MODEL_TENSOR.ATTN_NORM: (
             "gpt_neox.layers.{bid}.input_layernorm",                # gptneox
-            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact qwen
+            "transformer.h.{bid}.ln_1",                             # gpt2 gpt-j refact qwen jais
             "transformer.blocks.{bid}.norm_1",                      # mpt
             "transformer.h.{bid}.input_layernorm",                  # falcon7b
             "h.{bid}.input_layernorm",                              # bloom
@@ -98,6 +101,7 @@ class TensorNameMap:
             "backbone.layers.{bid}.norm",                           # mamba
             "transformer.decoder_layer.{bid}.rms_norm",             # Grok
             "transformer.blocks.{bid}.norm_attn_norm.norm_1",       # dbrx
+            "transformer.layers.{bid}.attn_norm",                   # openelm
         ),
 
         # Attention norm 2
@@ -109,7 +113,7 @@ class TensorNameMap:
         # Attention query-key-value
         MODEL_TENSOR.ATTN_QKV: (
             "gpt_neox.layers.{bid}.attention.query_key_value",                     # gptneox
-            "transformer.h.{bid}.attn.c_attn",                                     # gpt2 qwen
+            "transformer.h.{bid}.attn.c_attn",                                     # gpt2 qwen jais
             "transformer.blocks.{bid}.attn.Wqkv",                                  # mpt
             "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv",                   # dbrx
             "transformer.h.{bid}.self_attention.query_key_value",                  # falcon
@@ -119,7 +123,8 @@ class TensorNameMap:
             "h.{bid}.attn.c_attn",                                                 # gpt2
             "transformer.h.{bid}.mixer.Wqkv",                                      # phi2
             "encoder.layers.{bid}.attn.Wqkv",                                      # nomic-bert
-            "model.layers.{bid}.self_attn.qkv_proj"                                # phi3
+            "model.layers.{bid}.self_attn.qkv_proj",                               # phi3
+            "transformer.layers.{bid}.attn.qkv_proj",                              # openelm
         ),
 
         # Attention query
@@ -160,7 +165,7 @@ class TensorNameMap:
         # Attention output
         MODEL_TENSOR.ATTN_OUT: (
             "gpt_neox.layers.{bid}.attention.dense",                        # gptneox
-            "transformer.h.{bid}.attn.c_proj",                              # gpt2 refact qwen
+            "transformer.h.{bid}.attn.c_proj",                              # gpt2 refact qwen jais
             "transformer.blocks.{bid}.attn.out_proj",                       # mpt
             "transformer.h.{bid}.self_attention.dense",                     # falcon
             "h.{bid}.self_attention.dense",                                 # bloom
@@ -177,6 +182,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.attn.out_proj",                           # nomic-bert
             "transformer.decoder_layer.{bid}.multi_head_attention.linear",  # Grok
             "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj",        # dbrx
+            "transformer.layers.{bid}.attn.out_proj",                       # openelm
         ),
 
         # Attention output norm
@@ -202,7 +208,7 @@ class TensorNameMap:
         # Feed-forward norm
         MODEL_TENSOR.FFN_NORM: (
             "gpt_neox.layers.{bid}.post_attention_layernorm",                # gptneox
-            "transformer.h.{bid}.ln_2",                                      # gpt2 refact qwen
+            "transformer.h.{bid}.ln_2",                                      # gpt2 refact qwen jais
             "h.{bid}.post_attention_layernorm",                              # bloom
             "transformer.blocks.{bid}.norm_2",                               # mpt
             "model.layers.{bid}.post_attention_layernorm",                   # llama-hf
@@ -212,6 +218,7 @@ class TensorNameMap:
             "h.{bid}.ln_2",                                                  # gpt2
             "model.layers.{bid}.ffn_norm",                                   # internlm2
             "transformer.decoder_layer.{bid}.rms_norm_2",                    # Grok
+            "transformer.layers.{bid}.ffn_norm",                             # openelm
             "model.layers.{bid}.pre_ff_layernorm",                           # jamba
             "model.layers.{bid}.pre_moe_layernorm",                          # mini-jamba
         ),
@@ -242,7 +249,7 @@ class TensorNameMap:
         # Feed-forward up
         MODEL_TENSOR.FFN_UP: (
             "gpt_neox.layers.{bid}.mlp.dense_h_to_4h",                # gptneox
-            "transformer.h.{bid}.mlp.c_fc",                           # gpt2
+            "transformer.h.{bid}.mlp.c_fc",                           # gpt2 jais
             "transformer.blocks.{bid}.ffn.up_proj",                   # mpt
             "transformer.h.{bid}.mlp.dense_h_to_4h",                  # falcon
             "h.{bid}.mlp.dense_h_to_4h",                              # bloom
@@ -289,6 +296,7 @@ class TensorNameMap:
             "model.layers.{bid}.mlp.gate_proj",           # llama-hf refact
             "layers.{bid}.feed_forward.w1",               # llama-pth
             "transformer.h.{bid}.mlp.w2",                 # qwen
+            "transformer.h.{bid}.mlp.c_fc2",              # jais
             "model.layers.layers.{bid}.mlp.gate_proj",    # plamo
             "model.layers.{bid}.feed_forward.w1",         # internlm2
             "encoder.layers.{bid}.mlp.fc12",              # nomic-bert
@@ -313,7 +321,7 @@ class TensorNameMap:
         # Feed-forward down
         MODEL_TENSOR.FFN_DOWN: (
             "gpt_neox.layers.{bid}.mlp.dense_4h_to_h",                # gptneox
-            "transformer.h.{bid}.mlp.c_proj",                         # gpt2 refact qwen
+            "transformer.h.{bid}.mlp.c_proj",                         # gpt2 refact qwen jais
             "transformer.blocks.{bid}.ffn.down_proj",                 # mpt
             "transformer.h.{bid}.mlp.dense_4h_to_h",                  # falcon
             "h.{bid}.mlp.dense_4h_to_h",                              # bloom
@@ -331,6 +339,7 @@ class TensorNameMap:
             "encoder.layers.{bid}.mlp.fc2",                           # nomic-bert
             "model.layers.{bid}.mlp.c_proj",                          # starcoder2
             "encoder.layer.{bid}.mlp.wo",                             # jina-bert-v2
+            "transformer.layers.{bid}.ffn.proj_2",                    # openelm
             "model.layers.{bid}.residual_mlp.w2",                     # arctic
             "encoder.layer.{bid}.mlp.down_layer",                     # jina-bert-v2
             "model.layers.{bid}.feed_forward.down_proj",              # jamba
@@ -353,7 +362,8 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.q_layernorm",                       # persimmon
             "model.layers.{bid}.self_attn.q_norm",                            # cohere
             "transformer.blocks.{bid}.attn.q_ln",                             # sea-lion
-            "encoder.layer.{bid}.attention.self.layer_norm_q"                 # jina-bert-v2
+            "encoder.layer.{bid}.attention.self.layer_norm_q",                # jina-bert-v2
+            "transformer.layers.{bid}.attn.q_norm",                           # openelm
         ),
 
         MODEL_TENSOR.ATTN_K_NORM: (
@@ -361,7 +371,8 @@ class TensorNameMap:
             "model.layers.{bid}.self_attn.k_layernorm",                       # persimmon
             "model.layers.{bid}.self_attn.k_norm",                            # cohere
             "transformer.blocks.{bid}.attn.k_ln",                             # sea-lion
-            "encoder.layer.{bid}.attention.self.layer_norm_k"                 # jina-bert-v2
+            "encoder.layer.{bid}.attention.self.layer_norm_k",                # jina-bert-v2
+            "transformer.layers.{bid}.attn.k_norm",                           # openelm
         ),
 
         MODEL_TENSOR.ROPE_FREQS: (
diff --git a/include/llama.h b/include/llama.h
index 4418d6c74..d789b501a 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -89,6 +89,7 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_SMAUG          = 14,
         LLAMA_VOCAB_PRE_TYPE_PORO           = 15,
         LLAMA_VOCAB_PRE_TYPE_VIKING         = 16,
+        LLAMA_VOCAB_PRE_TYPE_JAIS           = 17,
     };
 
     // note: these values should be synchronized with ggml_rope
@@ -484,6 +485,13 @@ extern "C" {
     // Get a llama model tensor
     LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
 
+    // Returns true if the model contains an encoder that requires llama_encode() call
+    LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
+
+    // For encoder-decoder models, this function returns id of the token that must be provided
+    // to the decoder to start generating output sequence. For other models, it returns -1.
+    LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);
+
     // Returns 0 on success
     LLAMA_API uint32_t llama_model_quantize(
             const char * fname_inp,
@@ -819,6 +827,14 @@ extern "C" {
     // Frees a batch of tokens allocated with llama_batch_init()
     LLAMA_API void llama_batch_free(struct llama_batch batch);
 
+    // Processes a batch of tokens with the ecoder part of the encoder-decoder model.
+    // Stores the encoder output internally for later use by the decoder cross-attention layers.
+    //   0 - success
+    // < 0 - error
+    LLAMA_API int32_t llama_encode(
+            struct llama_context * ctx,
+              struct llama_batch   batch);
+
     // Positive return values does not mean a fatal error, but rather a warning.
     //   0 - success
     //   1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)
diff --git a/models/ggml-vocab-bert-bge.gguf.inp b/models/ggml-vocab-bert-bge.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-bert-bge.gguf.inp
+++ b/models/ggml-vocab-bert-bge.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-bert-bge.gguf.out b/models/ggml-vocab-bert-bge.gguf.out
index e4a76cdb0..a62566ce7 100644
--- a/models/ggml-vocab-bert-bge.gguf.out
+++ b/models/ggml-vocab-bert-bge.gguf.out
@@ -31,6 +31,7 @@
  1027
  1005 3690
  7592 1010 1061 1005 2035 999 2129 2024 2017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995
+ 999 999 999 999 999 999
  1017
  3943
  21211
@@ -40,4 +41,6 @@
  21211 22394 22394
  21211 22394 22394 2509
  21211 22394 22394 22394
+ 12731 2050 19710
+ 5860 18117
  100 1006 3671 1007 100 1006 3674 7861 29147 2483 9530 16280 23854 1007 100 100 1017 3943 21211 21211 2509 21211 22394 21211 22394 2509 21211 22394 22394 21211 22394 22394 2509 1017 1012 1017 1017 1012 1012 1017 1017 1012 1012 1012 1017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 1011 1011 1011 1011 1011 1011 1027 1027 1027 1027 1027 1027 1027 1192 15290 29754 14150 1192 10260 1181 29755 29436 29741 10260 16856 29747 23925 10325 1005 1005 1005 1005 1005 1005 1036 1036 1036 1036 1036 1036 1036 1000 1000 1000 1000 1012 1012 1012 1012 1012 1012 999 999 999 999 999 999 1029 1029 1029 1029 1029 1029 1045 1005 2310 2042 1005 2409 2002 1005 1055 2045 1010 1005 2128 2017 2469 1029 1005 1049 2025 2469 1045 1005 2222 2191 2009 1010 1005 1040 2017 2066 2070 5572 1029 2057 1005 2310 1037 1005 2222
diff --git a/models/ggml-vocab-command-r.gguf.inp b/models/ggml-vocab-command-r.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-command-r.gguf.inp
+++ b/models/ggml-vocab-command-r.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-command-r.gguf.out b/models/ggml-vocab-command-r.gguf.out
index cc4277daa..3f6b41888 100644
--- a/models/ggml-vocab-command-r.gguf.out
+++ b/models/ggml-vocab-command-r.gguf.out
@@ -31,6 +31,7 @@
  206 1857
  14 4515
  28339 19 1770 14 1954 8 4070 1955 1933 80503 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372
+ 57178 10251
  26
  26 26
  26 26 26
@@ -40,4 +41,6 @@
  26 26 26 26 26 26 26
  26 26 26 26 26 26 26 26
  26 26 26 26 26 26 26 26 26
+ 42 30719 12584
+ 3642 4388
  127731 51628 205 57788 18494 97469 126134 206 2226 256 230 1737 18258 16 80503 122 35927 2226 242 112 57462 1737 54457 223165 106230 2096 16 48389 11254 107 255 2226 107 255 228 26 228 26 26 228 26 26 26 228 26 26 26 26 228 26 26 26 26 26 228 26 26 26 26 26 26 228 26 26 26 26 26 26 26 228 26 26 26 26 26 26 26 26 228 26 21 26 228 26 2271 26 228 26 3834 26 182018 230 174833 38111 249 86325 241 38111 245 86325 232 38111 252 38111 123 38111 261 165 24629 38111 261 38111 103 174833 38111 235 188568 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 8391 158343 3512 40071 2196 3236 8750 1764 37097 41168 29721 32797 25646 3802 4975 4975 116167 57178 10251 154048 27292 1767 5125 2632 2155 91 2378 1919 1914 2782 19 2155 3354 1933 5470 38 2155 52 2068 5470 1767 4961 3059 1894 19 2155 43 1933 3026 2725 23186 38 2930 14 20676 1671 14 83 51
diff --git a/models/ggml-vocab-deepseek-coder.gguf.inp b/models/ggml-vocab-deepseek-coder.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-deepseek-coder.gguf.inp
+++ b/models/ggml-vocab-deepseek-coder.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-deepseek-coder.gguf.out b/models/ggml-vocab-deepseek-coder.gguf.out
index 9ccc560d6..52c4111a1 100644
--- a/models/ggml-vocab-deepseek-coder.gguf.out
+++ b/models/ggml-vocab-deepseek-coder.gguf.out
@@ -31,6 +31,7 @@
  185 405
  6 2895
  17535 11 320 6 435 0 1717 417 340 12394 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239
+ 15330 3023
  18
  18 18
  18 18 18
@@ -40,4 +41,6 @@
  18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18 18
+ 34 155 119 242 64 24297 155 119 216 83
+ 1607 2539
  185 207 185 185 207 185 185 185 207 12405 459 22758 185 243 185 315 185 251 185 730 185 10047 235 209 334 8760 8 12394 233 114 350 222 10047 221 104 169 116 224 334 4684 3909 992 24330 262 29651 612 8 207 156 237 214 12394 99 234 10047 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 524 18 207 18 1202 18 207 155 239 209 155 239 114 155 239 228 155 240 220 155 239 224 155 240 211 155 239 231 155 239 115 155 239 240 155 240 210 155 239 240 155 239 95 155 239 114 155 239 214 10047 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239 18155 374 17194 28 2861 6478 616 2251 14994 31269 4191 6 4686 4686 10252 3358 3358 3409 524 15330 3023 15031 5668 303 6 312 798 651 83 839 362 6 82 741 11 651 1369 340 2037 30 651 44 441 2037 303 6 642 1098 359 11 651 35 340 833 738 10860 30 998 6 10709 245 6 75 43
diff --git a/models/ggml-vocab-deepseek-llm.gguf.inp b/models/ggml-vocab-deepseek-llm.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-deepseek-llm.gguf.inp
+++ b/models/ggml-vocab-deepseek-llm.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-deepseek-llm.gguf.out b/models/ggml-vocab-deepseek-llm.gguf.out
index fd94b896d..0191b7a11 100644
--- a/models/ggml-vocab-deepseek-llm.gguf.out
+++ b/models/ggml-vocab-deepseek-llm.gguf.out
@@ -31,6 +31,7 @@
  185 403
  6 2906
  17464 11 320 6 436 0 1724 418 340 33701 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239
+ 15278 3033
  18
  18 18
  18 18 18
@@ -40,4 +41,6 @@
  18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18 18
+ 34 32555 242 64 23708 32555 216 83
+ 1763 2550
  185 207 185 185 207 185 185 185 207 11969 486 22504 185 243 185 300 185 251 185 663 185 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 12356 99 234 10044 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 526 18 207 18 1204 18 207 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71899 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 78827 55170 76659 620 91754 31116 36804 4885 4885 10897 4390 4390 41047 15278 3033 14986 5675 304 6 313 803 655 33326 362 6 82 745 11 655 1374 340 2049 30 655 44 441 2049 304 6 647 1099 359 11 655 35 340 837 742 10842 30 1003 6 10699 245 6 75 43
diff --git a/models/ggml-vocab-falcon.gguf.inp b/models/ggml-vocab-falcon.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-falcon.gguf.inp
+++ b/models/ggml-vocab-falcon.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-falcon.gguf.out b/models/ggml-vocab-falcon.gguf.out
index 209b04cda..64a48d97f 100644
--- a/models/ggml-vocab-falcon.gguf.out
+++ b/models/ggml-vocab-falcon.gguf.out
@@ -31,6 +31,7 @@
  1212 40
  18 4932
  9856 23 291 18 436 12 1265 362 299 8196 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236
+ 51520
  30
  3138
  22287
@@ -40,4 +41,6 @@
  22287 22287 30
  22287 22287 3138
  22287 22287 22287
+ 46 19768 239 76 9634 19768 213 95
+ 1080 1502
  1212 4824 1001 1212 192 204 663 49453 2069 742 561 1501 193 2571 232 206 204 19 11003 20 8196 126 283 219 48778 116 13392 204 19 51831 732 63209 1741 7955 522 20 22438 211 3346 111 231 2571 111 231 204 30 204 3138 204 22287 204 22287 30 204 22287 3138 204 22287 22287 204 22287 22287 30 204 22287 22287 3138 204 30 25 30 204 30 513 30 204 30 951 30 27171 236 206 38154 126 38154 225 167 237 217 38154 221 167 237 208 38154 228 38154 127 38154 237 167 237 207 38154 237 38154 107 38154 126 38154 211 20589 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 204 37057 2228 10666 5052 133 6207 151 215 150 134 5052 133 6279 5052 223 151 216 49679 123 53110 47043 7795 204 7544 7544 7544 8543 8543 17593 3513 3513 12844 51520 17664 4247 295 18 298 650 204 18 95 693 332 18 94 629 23 204 18 1553 299 1310 42 204 18 56 416 1310 295 18 567 717 334 23 204 18 47 299 606 596 6696 42 703 18 16139 241 18 87 55
diff --git a/models/ggml-vocab-gpt-2.gguf.inp b/models/ggml-vocab-gpt-2.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-gpt-2.gguf.inp
+++ b/models/ggml-vocab-gpt-2.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-gpt-2.gguf.out b/models/ggml-vocab-gpt-2.gguf.out
index 78430f0d3..17a13bdfc 100644
--- a/models/ggml-vocab-gpt-2.gguf.out
+++ b/models/ggml-vocab-gpt-2.gguf.out
@@ -31,6 +31,7 @@
  198 796
  6 6980
  15496 11 331 6 439 0 1374 389 345 30325 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252
+ 13896 3228
  18
  2091
  20370
@@ -40,4 +41,6 @@
  24840 20370
  24840 24840
  24840 2091 20370
+ 34 157 119 255 64 16049 157 119 229 83
+ 1221 1371
  198 220 628 220 628 198 220 197 220 197 197 220 197 198 220 220 198 220 220 220 198 220 220 220 220 198 220 220 220 220 220 198 8582 248 222 357 11265 8 30325 114 447 235 8582 234 104 37929 357 48101 795 13210 271 1673 36686 515 8 14519 227 12520 99 247 8582 99 247 513 4747 23460 513 20370 23460 2091 23460 20370 23460 24840 23460 2091 20370 513 13 18 513 492 18 513 986 18 28053 252 222 157 252 114 157 252 241 157 253 233 157 252 237 157 253 224 157 252 244 157 252 115 157 252 253 157 253 223 157 252 253 157 252 95 157 252 114 157 252 227 47249 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 40103 1421 18604 12466 121 16843 141 231 15166 12466 121 16142 12466 239 141 232 30143 140 111 16142 21169 21727 31583 18849 705 39115 6 33153 15506 63 15931 15931 16317 13896 3228 9805 3548 314 1053 587 705 44040 339 338 612 11 705 2200 345 1654 30 705 44 407 1654 314 1183 787 340 11 705 35 345 588 617 8887 30 775 6 26979 257 6 75 43
diff --git a/models/ggml-vocab-llama-bpe.gguf.inp b/models/ggml-vocab-llama-bpe.gguf.inp
index 9380bf355..9baf7d77a 100644
--- a/models/ggml-vocab-llama-bpe.gguf.inp
+++ b/models/ggml-vocab-llama-bpe.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
@@ -104,5 +110,3 @@ __ggml_vocab_test__
      
 🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
 __ggml_vocab_test__
- Việt
-__ggml_vocab_test__
diff --git a/models/ggml-vocab-llama-bpe.gguf.out b/models/ggml-vocab-llama-bpe.gguf.out
index 1f3607fb6..4b35cf93f 100644
--- a/models/ggml-vocab-llama-bpe.gguf.out
+++ b/models/ggml-vocab-llama-bpe.gguf.out
@@ -31,6 +31,7 @@
  198 284
  6 11639
  9906 11 379 65948 0 2650 527 499 27623 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909
+ 17523 3001
  18
  1644
  8765
@@ -40,5 +41,6 @@
  8765 8765 18
  8765 8765 1644
  8765 8765 8765
+ 34 91163 101798
+ 2624 2402
  198 4815 15073 66597 8004 1602 2355 79772 11187 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 11410 99 247 9468 99 247 220 18 220 1644 220 8765 220 8765 18 220 8765 1644 220 8765 8765 220 8765 8765 18 220 8765 8765 1644 220 18 13 18 220 18 497 18 220 18 1131 18 220 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 76460 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 56560 54337 19175 102118 13373 64571 34694 3114 112203 80112 3436 106451 14196 14196 74694 3089 3089 29249 17523 3001 27708 7801 358 3077 1027 364 83 820 568 596 1070 11 364 793 499 2771 30 364 44 539 2771 358 3358 1304 433 11 364 35 499 1093 1063 15600 30 1226 6 43712 264 64966 43
- 101798
diff --git a/models/ggml-vocab-llama-spm.gguf.inp b/models/ggml-vocab-llama-spm.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-llama-spm.gguf.inp
+++ b/models/ggml-vocab-llama-spm.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-llama-spm.gguf.out b/models/ggml-vocab-llama-spm.gguf.out
index 9c3327cb5..93aacf8ba 100644
--- a/models/ggml-vocab-llama-spm.gguf.out
+++ b/models/ggml-vocab-llama-spm.gguf.out
@@ -31,6 +31,7 @@
  29871 13 353
  525 3152
  15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739
+ 1738 6824 21004
  29871 29941
  29871 29941 29941
  29871 29941 29941 29941
@@ -40,4 +41,6 @@
  29871 29941 29941 29941 29941 29941 29941 29941
  29871 29941 29941 29941 29941 29941 29941 29941 29941
  29871 29941 29941 29941 29941 29941 29941 29941 29941 29941
+ 315 228 190 176 29874 10630 30529 29873
+ 29871 2313 3163
  29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931
diff --git a/models/ggml-vocab-mpt.gguf.inp b/models/ggml-vocab-mpt.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-mpt.gguf.inp
+++ b/models/ggml-vocab-mpt.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-mpt.gguf.out b/models/ggml-vocab-mpt.gguf.out
index d8d0fe909..372c751bf 100644
--- a/models/ggml-vocab-mpt.gguf.out
+++ b/models/ggml-vocab-mpt.gguf.out
@@ -31,6 +31,7 @@
  187 426
  8 8685
  12092 13 340 8 455 2 1359 403 368 49042 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241
+ 18963 4672
  20
  1610
  20084
@@ -40,4 +41,6 @@
  26409 20084
  26409 26409
  26409 1610 20084
+ 36 6829 244 66 17721 35177 85
+ 1262 2196
  586 1744 33525 186 209 623 28910 187 50276 187 50275 187 50274 187 50273 187 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 22692 101 236 14931 101 236 495 5922 30057 495 20084 495 26409 30057 20084 495 26409 1610 495 26409 20084 495 15 20 495 537 20 495 1051 20 209 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 14931 235 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 16081 6877 12880 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 42011 35033 34842 11202 9739 9739 33021 18963 4672 25561 8220 309 1849 644 686 42618 344 434 627 13 686 1848 368 2119 32 686 46 417 2119 309 1833 1056 352 13 686 37 368 751 690 10331 32 844 8 31516 247 8 77 45
diff --git a/models/ggml-vocab-phi-3.gguf.inp b/models/ggml-vocab-phi-3.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-phi-3.gguf.inp
+++ b/models/ggml-vocab-phi-3.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-phi-3.gguf.out b/models/ggml-vocab-phi-3.gguf.out
index 9c3327cb5..93aacf8ba 100644
--- a/models/ggml-vocab-phi-3.gguf.out
+++ b/models/ggml-vocab-phi-3.gguf.out
@@ -31,6 +31,7 @@
  29871 13 353
  525 3152
  15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739
+ 1738 6824 21004
  29871 29941
  29871 29941 29941
  29871 29941 29941 29941
@@ -40,4 +41,6 @@
  29871 29941 29941 29941 29941 29941 29941 29941
  29871 29941 29941 29941 29941 29941 29941 29941 29941
  29871 29941 29941 29941 29941 29941 29941 29941 29941 29941
+ 315 228 190 176 29874 10630 30529 29873
+ 29871 2313 3163
  29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931
diff --git a/models/ggml-vocab-qwen2.gguf.inp b/models/ggml-vocab-qwen2.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-qwen2.gguf.inp
+++ b/models/ggml-vocab-qwen2.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-qwen2.gguf.out b/models/ggml-vocab-qwen2.gguf.out
index 401a510e8..18b4b45cd 100644
--- a/models/ggml-vocab-qwen2.gguf.out
+++ b/models/ggml-vocab-qwen2.gguf.out
@@ -31,6 +31,7 @@
  198 284
  6 11385
  9707 11 379 64848 0 2585 525 498 26525 223 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216
+ 17085 2928
  18
  18 18
  18 18 18
@@ -40,4 +41,6 @@
  18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18
  18 18 18 18 18 18 18 18 18
+ 34 90063 128324
+ 2560 2347
  198 4710 14731 65497 7847 1572 2303 78672 10947 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 11162 99 247 149955 220 18 220 18 18 220 18 18 18 220 18 18 18 18 220 18 18 18 18 18 220 18 18 18 18 18 18 220 18 18 18 18 18 18 18 220 18 18 18 18 18 18 18 18 220 18 13 18 220 18 496 18 220 18 1112 18 220 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 144534 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 55460 53237 18658 14144 1456 13073 63471 33594 3038 133178 79012 3355 4605 4605 13874 13874 73594 3014 3014 28149 17085 2928 26610 7646 358 3003 1012 364 83 813 566 594 1052 11 364 787 498 2704 30 364 44 537 2704 358 3278 1281 432 11 364 35 498 1075 1045 15243 30 1205 6 42612 264 63866 43
diff --git a/models/ggml-vocab-refact.gguf.inp b/models/ggml-vocab-refact.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-refact.gguf.inp
+++ b/models/ggml-vocab-refact.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-refact.gguf.out b/models/ggml-vocab-refact.gguf.out
index 06b15c090..63d8305c3 100644
--- a/models/ggml-vocab-refact.gguf.out
+++ b/models/ggml-vocab-refact.gguf.out
@@ -31,6 +31,7 @@
  203 280
  25 34666
  8279 30 533 25 464 19 4971 884 844 18458 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838
+ 9163 3202
  37
  37 37
  37 37 37
@@ -40,4 +41,6 @@
  37 37 37 37 37 37 37
  37 37 37 37 37 37 37 37
  37 37 37 37 37 37 37 37 37
+ 53 33934 83 33217 17102 102
+ 1214 12258
  334 719 8878 202 10885 4222 16104 28570 203 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 5945 118 252 3807 118 252 225 37 225 37 37 225 37 37 37 225 37 37 37 37 225 37 37 37 37 37 225 37 37 37 37 37 37 225 37 37 37 37 37 37 37 225 37 37 37 37 37 37 37 37 225 37 32 37 225 37 497 37 225 37 1179 37 225 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 36628 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 20921 16623 13028 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 5881 9592 13299 914 31753 31359 9163 3202 35472 10397 439 4763 2583 330 102 1455 938 1182 2017 30 330 613 844 3654 49 330 63 646 3654 439 4621 1930 561 30 330 54 844 2124 1629 35993 49 2688 25 7709 312 25 94 62
diff --git a/models/ggml-vocab-starcoder.gguf.inp b/models/ggml-vocab-starcoder.gguf.inp
index 0a89107c6..9baf7d77a 100644
--- a/models/ggml-vocab-starcoder.gguf.inp
+++ b/models/ggml-vocab-starcoder.gguf.inp
@@ -73,6 +73,8 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
 __ggml_vocab_test__
+!!!!!!
+__ggml_vocab_test__
 3
 __ggml_vocab_test__
 33
@@ -91,6 +93,10 @@ __ggml_vocab_test__
 __ggml_vocab_test__
 333333333
 __ggml_vocab_test__
+Cửa Việt
+__ggml_vocab_test__
+ discards
+__ggml_vocab_test__
 
  
 
diff --git a/models/ggml-vocab-starcoder.gguf.out b/models/ggml-vocab-starcoder.gguf.out
index ccb55c7fe..87e2465d3 100644
--- a/models/ggml-vocab-starcoder.gguf.out
+++ b/models/ggml-vocab-starcoder.gguf.out
@@ -31,6 +31,7 @@
  222 299
  44 34719
  8302 49 553 44 483 38 4998 904 863 18445 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892
+ 9221 3226
  56
  56 56
  56 56 56
@@ -40,4 +41,6 @@
  56 56 56 56 56 56 56
  56 56 56 56 56 56 56 56
  56 56 56 56 56 56 56 56 56
+ 72 34269 102 33245 17234 121
+ 1236 12266
  353 736 8886 221 10883 4238 16101 28540 222 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 5954 137 271 3822 137 271 244 56 244 56 56 244 56 56 56 244 56 56 56 56 244 56 56 56 56 56 244 56 56 56 56 56 56 244 56 56 56 56 56 56 56 244 56 56 56 56 56 56 56 56 244 56 51 56 244 56 516 56 244 56 1198 56 244 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 36570 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892 20895 16625 13047 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721 5918 9643 13298 932 31723 31330 9221 3226 35426 10400 457 4783 2602 349 121 1477 957 1200 2038 49 349 632 863 3673 68 349 82 666 3673 457 4650 1949 580 49 349 73 863 2144 1649 35941 68 2726 44 7728 331 44 113 81
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 000000000..eb6baa6c7
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,1197 @@
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+
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+
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+dev = ["attrs[tests]", "pre-commit"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope-interface"]
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+tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
+
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+all = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
+audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+codecarbon = ["codecarbon (==1.2.0)"]
+deepspeed = ["accelerate (>=0.21.0)", "deepspeed (>=0.9.3)"]
+deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.21.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+dev = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch", "torchaudio", "torchvision"]
+docs-specific = ["hf-doc-builder"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.14,<0.19)"]
+torch = ["accelerate (>=0.21.0)", "torch"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
+[[package]]
+name = "typing-extensions"
+version = "4.9.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.9.0-py3-none-any.whl", hash = "sha256:af72aea155e91adfc61c3ae9e0e342dbc0cba726d6cba4b6c72c1f34e47291cd"},
+    {file = "typing_extensions-4.9.0.tar.gz", hash = "sha256:23478f88c37f27d76ac8aee6c905017a143b0b1b886c3c9f66bc2fd94f9f5783"},
+]
+
+[[package]]
+name = "urllib3"
+version = "2.2.1"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
+    {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
+]
+
+[package.extras]
+brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
+h2 = ["h2 (>=4,<5)"]
+socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "wcwidth"
+version = "0.2.13"
+description = "Measures the displayed width of unicode strings in a terminal"
+optional = false
+python-versions = "*"
+files = [
+    {file = "wcwidth-0.2.13-py2.py3-none-any.whl", hash = "sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859"},
+    {file = "wcwidth-0.2.13.tar.gz", hash = "sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.9"
+content-hash = "c8c4cc87637266a7b85debcbafa8887c5ad81cc8ef40e98a3f52c7c50af05c03"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..25e2e20b2
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,44 @@
+[tool.poetry]
+name = "llama-cpp-scripts"
+version = "0.0.0"
+description = "Scripts that ship with llama.cpp"
+authors = ["GGML <ggml@ggml.ai>"]
+readme = "README.md"
+homepage = "https://ggml.ai"
+repository = "https://github.com/ggerganov/llama.cpp"
+keywords = ["ggml", "gguf", "llama.cpp"]
+packages = [{ include = "*.py", from = "." }]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.9"
+numpy = "^1.25.0"
+sentencepiece = ">=0.1.98,<0.2.0"
+transformers = ">=4.35.2,<5.0.0"
+protobuf = ">=4.21.0,<5.0.0"
+gguf = { path = "./gguf-py" }
+torch = { version = "^2.2.0", source = "pytorch" }
+
+[tool.poetry.dev-dependencies]
+pytest = "^5.2"
+
+
+# Force wheel + cpu
+# For discussion and context see https://github.com/python-poetry/poetry#6409
+[[tool.poetry.source]]
+name = "pytorch"
+url = "https://download.pytorch.org/whl/cpu"
+priority = "explicit"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.poetry.scripts]
+llama-convert-hf-to-gguf = "convert_hf_to_gguf:main"
+llama-convert-llama-ggml-to-gguf = "convert_llama_ggml_to_gguf:main"
+llama-ggml-vk-generate-shaders = "ggml_vk_generate_shaders:main"
diff --git a/requirements.txt b/requirements.txt
index e5cfbf10b..1eca1a13f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -6,6 +6,6 @@
 
 -r ./requirements/requirements-convert-legacy-llama.txt
 
--r ./requirements/requirements-convert-hf-to-gguf.txt
--r ./requirements/requirements-convert-hf-to-gguf-update.txt
--r ./requirements/requirements-convert-llama-ggml-to-gguf.txt
+-r ./requirements/requirements-convert_hf_to_gguf.txt
+-r ./requirements/requirements-convert_hf_to_gguf_update.txt
+-r ./requirements/requirements-convert_llama_ggml_to_gguf.txt
diff --git a/requirements/requirements-convert-hf-to-gguf-update.txt b/requirements/requirements-convert_hf_to_gguf.txt
similarity index 100%
rename from requirements/requirements-convert-hf-to-gguf-update.txt
rename to requirements/requirements-convert_hf_to_gguf.txt
diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert_hf_to_gguf_update.txt
similarity index 100%
rename from requirements/requirements-convert-hf-to-gguf.txt
rename to requirements/requirements-convert_hf_to_gguf_update.txt
diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert_llama_ggml_to_gguf.txt
similarity index 100%
rename from requirements/requirements-convert-llama-ggml-to-gguf.txt
rename to requirements/requirements-convert_llama_ggml_to_gguf.txt
diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh
index 0c6afdd59..69a08c841 100755
--- a/scripts/check-requirements.sh
+++ b/scripts/check-requirements.sh
@@ -167,11 +167,11 @@ if (( do_cleanup )); then
 fi
 
 check_convert_script examples/convert-legacy-llama.py
-for py in convert-*.py; do
+for py in convert_*.py; do
     # skip convert-hf-to-gguf-update.py
     # TODO: the check is failing for some reason:
     #       https://github.com/ggerganov/llama.cpp/actions/runs/8875330981/job/24364557177?pr=6920
-    [[ $py == convert-hf-to-gguf-update.py ]] && continue
+    [[ $py == convert_hf_to_gguf_update.py ]] && continue
 
     check_convert_script "$py"
 done
diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh
index 586d6ea18..0d6d4032d 100644
--- a/scripts/pod-llama.sh
+++ b/scripts/pod-llama.sh
@@ -210,4 +210,3 @@ fi
 # more benches
 #GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf  4096 1 99 1 512,3200 128,128,800 1
 #GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
-
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
index ccb607e56..c2049df79 100644
--- a/src/CMakeLists.txt
+++ b/src/CMakeLists.txt
@@ -1,7 +1,5 @@
 # TODO: should not use this
 if (WIN32)
-    add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
-
     if (BUILD_SHARED_LIBS)
         set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
     endif()
diff --git a/src/llama.cpp b/src/llama.cpp
index 55251a415..d262f6d7e 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -104,8 +104,10 @@
 #define LLAMA_ATTRIBUTE_FORMAT(...)
 #endif
 
+// bump if necessary
 #define LLAMA_MAX_NODES   8192
-#define LLAMA_MAX_EXPERTS 160
+#define LLAMA_MAX_LAYERS  256
+#define LLAMA_MAX_EXPERTS 160  // DeepSeekV2
 
 //
 // logging
@@ -225,10 +227,12 @@ enum llm_arch {
     LLM_ARCH_COMMAND_R,
     LLM_ARCH_DBRX,
     LLM_ARCH_OLMO,
+    LLM_ARCH_OPENELM,
     LLM_ARCH_ARCTIC,
     LLM_ARCH_DEEPSEEK2,
     LLM_ARCH_BITNET,
     LLM_ARCH_T5,
+    LLM_ARCH_JAIS,
     LLM_ARCH_UNKNOWN,
 };
 
@@ -267,10 +271,12 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
     { LLM_ARCH_COMMAND_R,       "command-r"    },
     { LLM_ARCH_DBRX,            "dbrx"         },
     { LLM_ARCH_OLMO,            "olmo"         },
+    { LLM_ARCH_OPENELM,         "openelm"      },
     { LLM_ARCH_ARCTIC,          "arctic"       },
     { LLM_ARCH_DEEPSEEK2,       "deepseek2"    },
     { LLM_ARCH_BITNET,          "bitnet"       },
     { LLM_ARCH_T5,              "t5"           },
+    { LLM_ARCH_JAIS,            "jais"         },
     { LLM_ARCH_UNKNOWN,         "(unknown)"    },
 };
 
@@ -319,6 +325,7 @@ enum llm_kv {
     LLM_KV_ATTENTION_Q_LORA_RANK,
     LLM_KV_ATTENTION_KV_LORA_RANK,
     LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
+    LLM_KV_ATTENTION_SLIDING_WINDOW,
 
     LLM_KV_ROPE_DIMENSION_COUNT,
     LLM_KV_ROPE_FREQ_BASE,
@@ -411,6 +418,7 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
     { LLM_KV_ATTENTION_Q_LORA_RANK,            "%s.attention.q_lora_rank"            },
     { LLM_KV_ATTENTION_KV_LORA_RANK,           "%s.attention.kv_lora_rank"           },
     { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
+    { LLM_KV_ATTENTION_SLIDING_WINDOW,         "%s.attention.sliding_window"         },
 
     { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
     { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
@@ -1166,6 +1174,22 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_OPENELM,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_Q_NORM,     "blk.%d.attn_q_norm" },
+            { LLM_TENSOR_ATTN_K_NORM,     "blk.%d.attn_k_norm" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+        },
+    },
     {
         LLM_ARCH_ARCTIC,
         {
@@ -1270,6 +1294,21 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
             { LLM_TENSOR_ENC_FFN_UP,           "enc.blk.%d.ffn_up" },
         },
     },
+    {
+        LLM_ARCH_JAIS,
+        {
+            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
+            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
+            { LLM_TENSOR_OUTPUT,          "output" },
+            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
+            { LLM_TENSOR_ATTN_QKV,        "blk.%d.attn_qkv" },
+            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
+            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
+            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
+            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
+            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
+        },
+    },
     {
         LLM_ARCH_UNKNOWN,
         {
@@ -2061,14 +2100,23 @@ enum e_model {
     MODEL_17M,
     MODEL_22M,
     MODEL_33M,
+    MODEL_60M,
     MODEL_70M,
+    MODEL_80M,
     MODEL_109M,
     MODEL_137M,
     MODEL_160M,
+    MODEL_220M,
+    MODEL_250M,
+    MODEL_270M,
     MODEL_335M,
     MODEL_410M,
+    MODEL_450M,
+    MODEL_770M,
+    MODEL_780M,
     MODEL_0_5B,
     MODEL_1B,
+    MODEL_1_3B,
     MODEL_1_4B,
     MODEL_2B,
     MODEL_2_8B,
@@ -2077,6 +2125,7 @@ enum e_model {
     MODEL_6_9B,
     MODEL_7B,
     MODEL_8B,
+    MODEL_11B,
     MODEL_12B,
     MODEL_13B,
     MODEL_14B,
@@ -2117,19 +2166,19 @@ struct llama_hparams {
     uint32_t n_vocab;
     uint32_t n_ctx_train; // context size the model was trained on
     uint32_t n_embd;
-    uint32_t n_head;
-    uint32_t n_head_kv;
     uint32_t n_layer;
     uint32_t n_rot;
+    uint32_t n_swa = 0; // sliding window attention (SWA)
     uint32_t n_embd_head_k; // dimension of keys (d_k). d_q is assumed to be the same, but there are n_head q heads, and only n_head_kv k-v heads
     uint32_t n_embd_head_v; // dimension of values (d_v) aka n_embd_head
-    uint32_t n_ff;
     uint32_t n_expert = 0;
     uint32_t n_expert_used = 0;
     uint32_t n_vocab_type = 0; // for BERT-style token types
+    uint32_t n_rel_attn_bkts = 0;
 
-    // TODO: find a more compact way to add more per-layer hyper-parameters
-    std::vector<int32_t> n_head_kv_vec;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_arr;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
+    std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
 
     uint32_t n_layer_dense_lead = 0;
     uint32_t n_lora_q = 0;
@@ -2165,6 +2214,10 @@ struct llama_hparams {
     bool use_alibi     = false;
     bool attn_soft_cap = false;
 
+    // needed by encoder-decoder models (e.g. T5, FLAN-T5)
+    // ref: https://github.com/ggerganov/llama.cpp/pull/8141
+    llama_token dec_start_token_id = -1;
+
     enum llama_pooling_type      pooling_type            = LLAMA_POOLING_TYPE_NONE;
     enum llama_rope_type         rope_type               = LLAMA_ROPE_TYPE_NONE;
     enum llama_rope_scaling_type rope_scaling_type_train = LLAMA_ROPE_SCALING_TYPE_NONE;
@@ -2174,18 +2227,19 @@ struct llama_hparams {
         if (this->n_vocab       != other.n_vocab)       return true;
         if (this->n_ctx_train   != other.n_ctx_train)   return true;
         if (this->n_embd        != other.n_embd)        return true;
-        if (this->n_head        != other.n_head)        return true;
-        if (this->n_head_kv     != other.n_head_kv)     return true;
         if (this->n_layer       != other.n_layer)       return true;
         if (this->n_rot         != other.n_rot)         return true;
+        if (this->n_swa         != other.n_swa)         return true;
         if (this->n_embd_head_k != other.n_embd_head_k) return true;
         if (this->n_embd_head_v != other.n_embd_head_v) return true;
-        if (this->n_ff          != other.n_ff)          return true;
         if (this->n_expert      != other.n_expert)      return true;
         if (this->n_expert_used != other.n_expert_used) return true;
 
-        if (this->n_head_kv_vec != other.n_head_kv_vec) return true;
+        if (this->n_head_arr    != other.n_head_arr)    return true;
+        if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
+        if (this->n_ff_arr      != other.n_ff_arr)      return true;
 
+        if (this->n_rel_attn_bkts    != other.n_rel_attn_bkts)    return true;
         if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
         if (this->n_lora_q           != other.n_lora_q)           return true;
         if (this->n_lora_kv          != other.n_lora_kv)          return true;
@@ -2201,6 +2255,8 @@ struct llama_hparams {
         if (this->ssm_d_state != other.ssm_d_state) return true;
         if (this->ssm_dt_rank != other.ssm_dt_rank) return true;
 
+        if (this->dec_start_token_id != other.dec_start_token_id) return true;
+
         const float EPSILON = 1e-9f;
 
         if (!is_float_close(this->f_norm_eps,            other.f_norm_eps,            EPSILON)) return true;
@@ -2214,51 +2270,75 @@ struct llama_hparams {
         return false;
     }
 
-    uint32_t n_head_kv_l(uint32_t layer) const {
-        if (layer < n_head_kv_vec.size()) {
-            int32_t n_hkv_l = n_head_kv_vec[layer];
-            // TODO: what should happen when it's negative?
-            GGML_ASSERT(n_hkv_l >= 0);
-            return n_hkv_l;
+    uint32_t n_head(uint32_t il = 0) const {
+        if (il < n_layer) {
+            return n_head_arr[il];
         }
-        return n_head_kv;
+
+        GGML_ASSERT(false);
+        return 0;
     }
 
-    uint32_t n_gqa(uint32_t layer = 0) const {
-        uint32_t n_head_kv = n_head_kv_l(layer);
+    uint32_t n_head_kv(uint32_t il = 0) const {
+        if (il < n_layer) {
+            return n_head_kv_arr[il];
+        }
+
+        GGML_ASSERT(false);
+        return 0;
+    }
+
+    uint32_t n_ff(uint32_t il = 0) const {
+        if (il < n_layer) {
+            return n_ff_arr[il];
+        }
+
+        GGML_ASSERT(false);
+        return 0;
+    }
+
+    uint32_t n_gqa(uint32_t il = 0) const {
+        const uint32_t n_head    = this->n_head(il);
+        const uint32_t n_head_kv = this->n_head_kv(il);
+
         if (n_head_kv == 0) {
             return 0;
         }
+
         return n_head/n_head_kv;
     }
 
-    uint32_t n_embd_k_gqa(uint32_t layer = 0) const { // dimension of key embeddings across all k-v heads
-        uint32_t n_head_kv = n_head_kv_l(layer);
+    uint32_t n_embd_k_gqa(uint32_t il = 0) const { // dimension of key embeddings across all k-v heads
+        const uint32_t n_head_kv = this->n_head_kv(il);
+
         return n_embd_head_k * n_head_kv;
     }
 
-    uint32_t n_embd_v_gqa(uint32_t layer = 0) const { // dimension of value embeddings across all k-v heads
-        uint32_t n_head_kv = n_head_kv_l(layer);
+    uint32_t n_embd_v_gqa(uint32_t il = 0) const { // dimension of value embeddings across all k-v heads
+        const uint32_t n_head_kv = this->n_head_kv(il);
+
         return n_embd_head_v * n_head_kv;
     }
 
-    uint32_t n_embd_r(uint32_t layer) const { // dimension of the rolling state embeddings
+    uint32_t n_embd_r(uint32_t il) const { // dimension of the rolling state embeddings
         // TODO: support using an SSM in place of the MLP of a Transformer
-        if (n_head_kv_l(layer) != 0) { return 0; }
+        if (n_head_kv(il) != 0) { return 0; }
         // corresponds to Mamba's conv_states size
         // TODO: maybe support other convolution strides than 1
         // NOTE: since the first column of the conv_state is shifted out each time, it's not actually needed
         return (ssm_d_conv > 0 ? ssm_d_conv - 1 : 0) * ssm_d_inner;
     }
 
-    uint32_t n_embd_s(uint32_t layer) const { // dimension of the recurrent state embeddings
+    uint32_t n_embd_s(uint32_t il) const { // dimension of the recurrent state embeddings
         // TODO: support using an SSM in place of the MLP of a Transformer
-        if (n_head_kv_l(layer) != 0) { return 0; }
+        if (n_head_kv(il) != 0) { return 0; }
         // corresponds to Mamba's ssm_states size
         return ssm_d_state * ssm_d_inner;
     }
 };
 
+static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
+
 struct llama_cparams {
     uint32_t n_ctx;           // context size used during inference
     uint32_t n_batch;
@@ -2290,6 +2370,7 @@ struct llama_cparams {
     void * cb_eval_user_data;
 };
 
+// TODO: separate into "llama_layer_enc" and "llama_layer_dec"
 struct llama_layer {
     // normalization
     struct ggml_tensor * attn_norm;
@@ -2310,6 +2391,8 @@ struct llama_layer {
     struct ggml_tensor * ssm_dt_norm;
     struct ggml_tensor * ssm_b_norm;
     struct ggml_tensor * ssm_c_norm;
+    struct ggml_tensor * attn_norm_cross;
+    struct ggml_tensor * attn_norm_enc;
 
     // attention
     struct ggml_tensor * wq;
@@ -2321,6 +2404,14 @@ struct llama_layer {
     struct ggml_tensor * wq_b;
     struct ggml_tensor * wkv_a_mqa;
     struct ggml_tensor * wkv_b;
+    struct ggml_tensor * wq_cross;
+    struct ggml_tensor * wk_cross;
+    struct ggml_tensor * wv_cross;
+    struct ggml_tensor * wo_cross;
+    struct ggml_tensor * wq_enc;
+    struct ggml_tensor * wk_enc;
+    struct ggml_tensor * wv_enc;
+    struct ggml_tensor * wo_enc;
 
     // attention bias
     struct ggml_tensor * bq;
@@ -2329,6 +2420,11 @@ struct llama_layer {
     struct ggml_tensor * bo;
     struct ggml_tensor * bqkv;
 
+    // relative position bias
+    struct ggml_tensor * attn_rel_b;
+    struct ggml_tensor * attn_rel_b_enc;
+    struct ggml_tensor * attn_rel_b_cross;
+
     // normalization
     struct ggml_tensor * ffn_norm;
     struct ggml_tensor * ffn_norm_b;
@@ -2336,11 +2432,15 @@ struct llama_layer {
     struct ggml_tensor * layer_out_norm;
     struct ggml_tensor * layer_out_norm_b;
     struct ggml_tensor * ffn_norm_exps;
+    struct ggml_tensor * ffn_norm_enc;
 
     // ff
     struct ggml_tensor * ffn_gate; // w1
     struct ggml_tensor * ffn_down; // w2
     struct ggml_tensor * ffn_up;   // w3
+    struct ggml_tensor * ffn_gate_enc;
+    struct ggml_tensor * ffn_down_enc;
+    struct ggml_tensor * ffn_up_enc;
 
     // ff MoE
     struct ggml_tensor * ffn_gate_inp;
@@ -3298,6 +3398,7 @@ struct llama_model {
     struct ggml_tensor * output_norm_b;
     struct ggml_tensor * output;
     struct ggml_tensor * output_b;
+    struct ggml_tensor * output_norm_enc;
 
     std::vector<llama_layer> layers;
 
@@ -3764,6 +3865,13 @@ struct llama_context {
     // populated only when pooling_type != LLAMA_POOLING_TYPE_NONE
     std::map<llama_seq_id, std::vector<float>> embd_seq;
 
+    // whether we are computing encoder output or decoder output
+    bool is_encoding = false;
+
+    // output of the encoder part of the encoder-decoder models
+    std::vector<float> embd_enc;
+    std::vector<std::set<llama_seq_id>> seq_ids_enc;
+
     // memory buffers used to evaluate the model
     std::vector<uint8_t> buf_compute_meta;
     ggml_backend_sched_t sched = nullptr;
@@ -3772,16 +3880,20 @@ struct llama_context {
     void *              abort_callback_data = nullptr;
 
     // input tensors
-    struct ggml_tensor * inp_tokens;    // I32 [n_batch]
-    struct ggml_tensor * inp_embd;      // F32 [n_embd, n_batch]
-    struct ggml_tensor * inp_pos;       // I32 [n_batch]
-    struct ggml_tensor * inp_out_ids;   // I32 [n_outputs]
-    struct ggml_tensor * inp_KQ_mask;   // F32 [kv_size, n_batch]
-    struct ggml_tensor * inp_K_shift;   // I32 [kv_size]
-    struct ggml_tensor * inp_mean;      // F32 [n_batch, n_batch]
-    struct ggml_tensor * inp_cls;       // I32 [n_batch]
-    struct ggml_tensor * inp_s_copy;    // I32 [n_rs]
-    struct ggml_tensor * inp_s_mask;    // F32 [1, n_rs]
+    struct ggml_tensor * inp_tokens;      // I32 [n_batch]
+    struct ggml_tensor * inp_embd;        // F32 [n_embd, n_batch]
+    struct ggml_tensor * inp_pos;         // I32 [n_batch]
+    struct ggml_tensor * inp_out_ids;     // I32 [n_outputs]
+    struct ggml_tensor * inp_KQ_mask;     // F32 [kv_size, n_batch]
+    struct ggml_tensor * inp_KQ_mask_swa; // F32 [kv_size, n_batch]
+    struct ggml_tensor * inp_K_shift;     // I32 [kv_size]
+    struct ggml_tensor * inp_mean;        // F32 [n_batch, n_batch]
+    struct ggml_tensor * inp_cls;         // I32 [n_batch]
+    struct ggml_tensor * inp_s_copy;      // I32 [n_rs]
+    struct ggml_tensor * inp_s_mask;      // F32 [1, n_rs]
+    struct ggml_tensor * inp_pos_bucket;    // I32 [n_batch|n_kv, n_batch]
+    struct ggml_tensor * inp_embd_enc;      // F32 [n_embd, n_outputs_enc]
+    struct ggml_tensor * inp_KQ_mask_cross; // F32 [n_outputs_enc, n_batch]
 
     // control vectors
     struct llama_control_vector cvec;
@@ -3909,13 +4021,15 @@ static bool llama_past_init(
 
     const struct llama_hparams & hparams = model.hparams;
 
-    const bool     has_kv       = hparams.n_head_kv != 0 && hparams.causal_attn;
-    const bool     has_r        = hparams.ssm_d_conv != 0 && hparams.ssm_d_inner != 0;
-    const bool     has_s        = hparams.ssm_d_state != 0 && hparams.ssm_d_inner != 0;
-    const bool     has_rs       = has_r || has_s;
-    const uint32_t kv_size      = has_kv ? cparams.n_ctx : 0;
-    const uint32_t rs_size      = has_rs ? cparams.n_seq_max : 0;
-    const int64_t  n_layer      = hparams.n_layer;
+    const auto & n_head_kv_iter = hparams.n_head_kv_arr.begin();
+
+    const int64_t  n_layer = hparams.n_layer;
+    const bool     has_kv  = std::any_of(n_head_kv_iter, n_head_kv_iter + n_layer, [](uint32_t n) { return n > 0; }) && hparams.causal_attn;
+    const bool     has_r   = hparams.ssm_d_conv != 0 && hparams.ssm_d_inner != 0;
+    const bool     has_s   = hparams.ssm_d_state != 0 && hparams.ssm_d_inner != 0;
+    const bool     has_rs  = has_r || has_s;
+    const uint32_t kv_size = has_kv ? cparams.n_ctx : 0;
+    const uint32_t rs_size = has_rs ? cparams.n_seq_max : 0;
 
     cache.kv.size = kv_size;
 
@@ -5201,23 +5315,53 @@ struct llama_model_loader {
         struct GGUFMeta::ArrayInfo arr_info =
             GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
 
-        // TODO: allow ANY lossless cast
-        // GGML_ASSERT(gguf_type_size(arr_info.gt) == sizeof(T));
         switch (arr_info.gt) {
-            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value));    break;
-            case GGUF_TYPE_INT32:   GGML_ASSERT((std::is_same<T, int32_t>::value));  break;
+            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
+            case GGUF_TYPE_INT32:   GGML_ASSERT(
+                                            (std::is_same<T,  int32_t>::value) ||
+                                            (std::is_same<T, uint32_t>::value));  break;
             default:
                 throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
         }
 
-        result.reserve(arr_info.length);
-        result.assign((const T *)arr_info.data, (const T *)arr_info.data + arr_info.length);
+        result.resize(arr_info.length);
+        result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length);
+
+        return true;
+    }
+
+    template<typename T, size_t N_MAX>
+    bool get_arr(const std::string & key, std::array<T, N_MAX> & result, const bool required = true) {
+        const int kid = gguf_find_key(meta, key.c_str());
+
+        if (kid < 0 || gguf_get_kv_type(meta, kid) != GGUF_TYPE_ARRAY) {
+            if (required) {
+                throw std::runtime_error(format("array key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        struct GGUFMeta::ArrayInfo arr_info =
+            GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+        switch (arr_info.gt) {
+            case GGUF_TYPE_FLOAT32: GGML_ASSERT((std::is_same<T, float>::value)); break;
+            case GGUF_TYPE_INT32:   GGML_ASSERT(
+                                            (std::is_same<T,  int32_t>::value) ||
+                                            (std::is_same<T, uint32_t>::value));  break;
+            default:
+                throw std::runtime_error(format("%s is not a float32, int32 array", key.c_str()));
+        }
+
+        GGML_ASSERT(arr_info.length <= N_MAX);
+
+        std::copy((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length, result.begin());
 
         return true;
     }
 
     template<typename T>
-    bool get_arr(const enum llm_kv kid, T& result, const bool required = true) {
+    bool get_arr(const enum llm_kv kid, T & result, const bool required = true) {
         return get_arr(llm_kv(kid), result, required);
     }
 
@@ -5242,6 +5386,50 @@ struct llama_model_loader {
         return get_key(llm_kv(kid), result, required);
     }
 
+    // get array of n <= N_MAX elements, or a single element repeated n times
+    template<typename T, size_t N_MAX>
+    bool get_key_or_arr(const std::string & key, std::array<T, N_MAX> & result, uint32_t n, const bool required = true) {
+        GGML_ASSERT(n <= N_MAX);
+
+        const int kid = gguf_find_key(meta, key.c_str());
+
+        if (kid < 0) {
+            if (required) {
+                throw std::runtime_error(format("key not found in model: %s", key.c_str()));
+            }
+            return false;
+        }
+
+        if (gguf_get_kv_type(meta, kid) == GGUF_TYPE_ARRAY) {
+            struct GGUFMeta::ArrayInfo arr_info =
+                GGUFMeta::GKV<GGUFMeta::ArrayInfo>::get_kv(meta, kid);
+
+            if (n != arr_info.length) {
+                throw std::runtime_error(format("key %s has wrong array length; expected %u, got %u", key.c_str(), n, (uint32_t) arr_info.length));
+            }
+
+            return get_arr(key, result, required);
+        } else {
+            T value;
+
+            bool ok = get_key(key, value, required);
+            if (!ok) {
+                return false;
+            }
+
+            for (uint32_t i = 0; i < n; i++) {
+                result[i] = value;
+            }
+
+            return true;
+        }
+    }
+
+    template<typename T>
+    bool get_key_or_arr(const enum llm_kv kid, T & result, uint32_t n, const bool required = true) {
+        return get_key_or_arr(llm_kv(kid), result, n, required);
+    }
+
     std::string get_arch_name() const {
         return arch_name;
     }
@@ -5726,14 +5914,23 @@ static const char * llama_model_type_name(e_model type) {
         case MODEL_17M:           return "17M";
         case MODEL_22M:           return "22M";
         case MODEL_33M:           return "33M";
+        case MODEL_60M:           return "60M";
         case MODEL_70M:           return "70M";
+        case MODEL_80M:           return "80M";
         case MODEL_109M:          return "109M";
         case MODEL_137M:          return "137M";
         case MODEL_160M:          return "160M";
+        case MODEL_220M:          return "220M";
+        case MODEL_250M:          return "250M";
+        case MODEL_270M:          return "270M";
         case MODEL_335M:          return "335M";
         case MODEL_410M:          return "410M";
+        case MODEL_450M:          return "450M";
+        case MODEL_770M:          return "770M";
+        case MODEL_780M:          return "780M";
         case MODEL_0_5B:          return "0.5B";
         case MODEL_1B:            return "1B";
+        case MODEL_1_3B:          return "1.3B";
         case MODEL_1_4B:          return "1.4B";
         case MODEL_2B:            return "2B";
         case MODEL_2_8B:          return "2.8B";
@@ -5742,6 +5939,7 @@ static const char * llama_model_type_name(e_model type) {
         case MODEL_6_9B:          return "6.9B";
         case MODEL_7B:            return "7B";
         case MODEL_8B:            return "8B";
+        case MODEL_11B:           return "11B";
         case MODEL_12B:           return "12B";
         case MODEL_13B:           return "13B";
         case MODEL_14B:           return "14B";
@@ -5811,20 +6009,18 @@ static void llm_load_hparams(
     ml.get_key(LLM_KV_GENERAL_NAME, model.name, false);
 
     // get hparams kv
-    ml.get_key(LLM_KV_VOCAB_SIZE,           hparams.n_vocab,       false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
+    ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
 
     // everything past this point is not vocab-related
     if (hparams.vocab_only) {
         return;
     }
 
-    ml.get_key(LLM_KV_CONTEXT_LENGTH,       hparams.n_ctx_train);
-    ml.get_key(LLM_KV_EMBEDDING_LENGTH,     hparams.n_embd);
-    ml.get_key(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff);
-    ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head);
-    ml.get_key(LLM_KV_BLOCK_COUNT,          hparams.n_layer);
-    ml.get_key(LLM_KV_EXPERT_COUNT,         hparams.n_expert,      false);
-    ml.get_key(LLM_KV_EXPERT_USED_COUNT,    hparams.n_expert_used, false);
+    ml.get_key(LLM_KV_CONTEXT_LENGTH,    hparams.n_ctx_train);
+    ml.get_key(LLM_KV_EMBEDDING_LENGTH,  hparams.n_embd);
+    ml.get_key(LLM_KV_BLOCK_COUNT,       hparams.n_layer);
+    ml.get_key(LLM_KV_EXPERT_COUNT,      hparams.n_expert,      false);
+    ml.get_key(LLM_KV_EXPERT_USED_COUNT, hparams.n_expert_used, false);
 
     GGML_ASSERT(hparams.n_expert <= LLAMA_MAX_EXPERTS);
     GGML_ASSERT(hparams.n_expert_used <= hparams.n_expert);
@@ -5834,14 +6030,18 @@ static void llm_load_hparams(
         GGML_ASSERT(hparams.n_expert_used == 0);
     }
 
-    // n_head_kv is optional, default to n_head
-    hparams.n_head_kv = hparams.n_head;
+    // zero-out the per-layer hparams
+    std::fill(hparams.n_head_arr.begin(),    hparams.n_head_arr.end(),    0);
+    std::fill(hparams.n_head_kv_arr.begin(), hparams.n_head_kv_arr.end(), 0);
+    std::fill(hparams.n_ff_arr.begin(),      hparams.n_ff_arr.end(),      0);
 
-    // per-layer n_head_kv
-    if (!ml.get_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_vec, false)) {
-        // global/fallback n_head_kv
-        ml.get_key(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv, false);
-    }
+    ml.get_key_or_arr(LLM_KV_FEED_FORWARD_LENGTH,  hparams.n_ff_arr,   hparams.n_layer);
+    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT, hparams.n_head_arr, hparams.n_layer);
+
+    // n_head_kv is optional, default to n_head
+    hparams.n_head_kv_arr = hparams.n_head_arr;
+
+    ml.get_key_or_arr(LLM_KV_ATTENTION_HEAD_COUNT_KV, hparams.n_head_kv_arr, hparams.n_layer, false);
 
     bool rope_finetuned = false;
     ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false);
@@ -5869,27 +6069,32 @@ static void llm_load_hparams(
 
     ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false);
 
-    // sanity check for n_rot (optional)
-    {
-        hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
+    // non-transformer models do not have attention heads
+    if (hparams.n_head() > 0) {
+        // sanity check for n_rot (optional)
+        hparams.n_rot = hparams.n_embd / hparams.n_head();
 
         ml.get_key(LLM_KV_ROPE_DIMENSION_COUNT, hparams.n_rot, false);
 
         if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) {
-            if (hparams.n_rot != hparams.n_embd / hparams.n_head) {
-                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head));
+            if (hparams.n_rot != hparams.n_embd / hparams.n_head()) {
+                throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head()));
             }
         }
         // gpt-neox n_rot = rotary_pct * (n_embd / n_head)
         // gpt-j n_rot = rotary_dim
+
+        hparams.n_embd_head_k = hparams.n_embd / hparams.n_head();
+        ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
+
+        hparams.n_embd_head_v = hparams.n_embd / hparams.n_head();
+        ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
+    } else {
+        hparams.n_rot = 0;
+        hparams.n_embd_head_k = 0;
+        hparams.n_embd_head_v = 0;
     }
 
-    hparams.n_embd_head_k = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
-    ml.get_key(LLM_KV_ATTENTION_KEY_LENGTH, hparams.n_embd_head_k, false);
-
-    hparams.n_embd_head_v = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head;
-    ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH, hparams.n_embd_head_v, false);
-
     // arch-specific KVs
     switch (model.arch) {
         case LLM_ARCH_LLAMA:
@@ -5912,7 +6117,7 @@ static void llm_load_hparams(
                         case 40: model.type = e_model::MODEL_13B; break;
                         case 48: model.type = e_model::MODEL_34B; break;
                         case 60: model.type = e_model::MODEL_30B; break;
-                        case 80: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_65B : e_model::MODEL_70B; break;
+                        case 80: model.type = hparams.n_head() == hparams.n_head_kv() ? e_model::MODEL_65B : e_model::MODEL_70B; break;
                         default: model.type = e_model::MODEL_UNKNOWN;
                     }
                 }
@@ -6081,7 +6286,7 @@ static void llm_load_hparams(
                 switch (hparams.n_layer) {
                     case 24: model.type = hparams.n_embd == 1024 ? e_model::MODEL_0_5B : e_model::MODEL_1B; break;
                     case 32: model.type = e_model::MODEL_7B; break;
-                    case 40: model.type = hparams.n_head == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
+                    case 40: model.type = hparams.n_head() == 20 ? e_model::MODEL_4B : e_model::MODEL_13B; break;
                     case 80: model.type = e_model::MODEL_70B; break;
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
@@ -6177,6 +6382,8 @@ static void llm_load_hparams(
             } break;
         case LLM_ARCH_GEMMA2:
             {
+                hparams.n_swa = 4096; // default value of gemma 2
+                ml.get_key(LLM_KV_ATTENTION_SLIDING_WINDOW, hparams.n_swa, false);
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
                 ml.get_key(LLM_KV_ATTN_LOGIT_SOFTCAPPING, hparams.f_attn_logit_softcapping, false);
                 ml.get_key(LLM_KV_FINAL_LOGIT_SOFTCAPPING, hparams.f_final_logit_softcapping, false);
@@ -6287,46 +6494,58 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_OPENELM:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+                switch (hparams.n_layer) {
+                case 16: model.type = e_model::MODEL_270M; break;
+                case 20: model.type = e_model::MODEL_450M; break;
+                case 28: model.type = e_model::MODEL_1B; break;
+                case 36: model.type = e_model::MODEL_3B; break;
+                default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         case LLM_ARCH_GPTNEOX:
             {
                 ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
                 ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res);
                 switch (hparams.n_layer) {
                     case 6:
-                        switch (hparams.n_ff) {
+                        switch (hparams.n_ff()) {
                             case 512: model.type = e_model::MODEL_14M; break;
                             case 2048: model.type = e_model::MODEL_70M; break;
                             default: model.type = e_model::MODEL_UNKNOWN;
                         } break;
                     case 12:
-                        switch (hparams.n_ff) {
+                        switch (hparams.n_ff()) {
                             case 3072: model.type = e_model::MODEL_160M; break;
                             default: model.type = e_model::MODEL_UNKNOWN;
                         } break;
                     case 16:
-                        switch (hparams.n_ff) {
+                        switch (hparams.n_ff()) {
                             case 8192: model.type = e_model::MODEL_1B; break;
                             default: model.type = e_model::MODEL_UNKNOWN;
                         } break;
                     case 24:
-                        switch (hparams.n_ff) {
+                        switch (hparams.n_ff()) {
                             case 4096: model.type = e_model::MODEL_410M; break;
                             case 8192: model.type = e_model::MODEL_1_4B; break;
                             default: model.type = e_model::MODEL_UNKNOWN;
                         } break;
                     case 32:
-                        switch (hparams.n_ff) {
+                        switch (hparams.n_ff()) {
                             case 10240: model.type = e_model::MODEL_2_8B; break;
                             case 16384: model.type = e_model::MODEL_6_9B; break;
                             default: model.type = e_model::MODEL_UNKNOWN;
                         } break;
                     case 36:
-                        switch (hparams.n_ff) {
+                        switch (hparams.n_ff()) {
                             case 20480: model.type = e_model::MODEL_12B; break;
                             default: model.type = e_model::MODEL_UNKNOWN;
                         } break;
                     case 44:
-                        switch (hparams.n_ff) {
+                        switch (hparams.n_ff()) {
                             case 24576: model.type = e_model::MODEL_20B; break;
                             default: model.type = e_model::MODEL_UNKNOWN;
                         } break;
@@ -6375,6 +6594,50 @@ static void llm_load_hparams(
                     default: model.type = e_model::MODEL_UNKNOWN;
                 }
             } break;
+        case LLM_ARCH_T5:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+                ml.get_key(LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, hparams.n_rel_attn_bkts);
+
+                uint32_t dec_start_token_id;
+                if (ml.get_key(LLM_KV_DECODER_START_TOKEN_ID, dec_start_token_id, false)) {
+                    hparams.dec_start_token_id = dec_start_token_id;
+                }
+
+                switch (hparams.n_layer) {
+                    case 6:  model.type = e_model::MODEL_60M;  break; // t5-small
+                    case 8:  model.type = e_model::MODEL_80M;  break; // flan-t5-small
+                    case 12:
+                        switch (hparams.n_ff()) {
+                            case 3072: model.type = e_model::MODEL_220M; break; // t5-base
+                            case 2048: model.type = e_model::MODEL_250M; break; // flan-t5-base
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    case 24:
+                        switch (hparams.n_ff()) {
+                            case 4096:  model.type = e_model::MODEL_770M; break; // t5-large
+                            case 2816:  model.type = e_model::MODEL_780M; break; // flan-t5-large
+                            case 16384: model.type = e_model::MODEL_3B;   break; // t5-3b
+                            case 5120:  model.type = e_model::MODEL_3B;   break; // flan-t5-xl
+                            case 65536: model.type = e_model::MODEL_11B;  break; // t5-11b
+                            case 10240: model.type = e_model::MODEL_11B;  break; // flan-t5-xxl
+                            default: model.type = e_model::MODEL_UNKNOWN;
+                        } break;
+                    default: model.type = e_model::MODEL_UNKNOWN;
+               }
+            } break;
+        case LLM_ARCH_JAIS:
+            {
+                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
+                ml.get_key(LLM_KV_ATTENTION_MAX_ALIBI_BIAS, hparams.f_max_alibi_bias);
+
+                switch (hparams.n_layer) {
+                    case 24: model.type = e_model::MODEL_1_3B; break;
+                    case 40: model.type = e_model::MODEL_13B; break;
+                    /* TODO: add variants */
+                    default: model.type = e_model::MODEL_UNKNOWN;
+                }
+            } break;
         default: (void)0;
     }
 
@@ -6606,6 +6869,9 @@ static void llm_load_vocab(
             } else if (
                 tokenizer_pre == "viking") {
                 vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_VIKING;
+            } else if (
+                tokenizer_pre == "jais") {
+                vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_JAIS;
             } else {
                 throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
             }
@@ -6891,43 +7157,78 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
 
     const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train);
 
+    auto print_f = [](const std::function<uint32_t(uint32_t)> & f, uint32_t n) {
+        bool is_var = false;
+
+        std::vector<uint32_t> v;
+        for (uint32_t i = 0; i < n; ++i) {
+            v.push_back(f(i));
+            if (v[i] != v[0]) {
+                is_var = true;
+            }
+        }
+
+        std::stringstream ss;
+
+        if (is_var) {
+            ss << "[";
+            for (uint32_t i = 0; i < n; ++i) {
+                ss << v[i];
+                if (i < n - 1) {
+                    ss << ", ";
+                }
+            }
+            ss << "]";
+        } else {
+            ss << v[0];
+        }
+
+        return ss.str();
+    };
+
     // hparams
     LLAMA_LOG_INFO("%s: format           = %s\n",     __func__, llama_file_version_name(ml.fver));
     LLAMA_LOG_INFO("%s: arch             = %s\n",     __func__, LLM_ARCH_NAMES.at(model.arch));
     LLAMA_LOG_INFO("%s: vocab type       = %s\n",     __func__, llama_model_vocab_type_name(vocab.type));
     LLAMA_LOG_INFO("%s: n_vocab          = %u\n",     __func__, hparams.n_vocab);
     LLAMA_LOG_INFO("%s: n_merges         = %u\n",     __func__, (int) vocab.bpe_ranks.size());
-    LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
-    LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
-    LLAMA_LOG_INFO("%s: n_head           = %u\n",     __func__, hparams.n_head);
-    LLAMA_LOG_INFO("%s: n_head_kv        = %u\n",     __func__, hparams.n_head_kv);
-    LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
-    LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
-    LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
-    LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
-    LLAMA_LOG_INFO("%s: n_gqa            = %u\n",     __func__, hparams.n_gqa());
-    LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %u\n",     __func__, hparams.n_embd_k_gqa());
-    LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %u\n",     __func__, hparams.n_embd_v_gqa());
-    LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
-    LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
-    LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
-    LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
-    LLAMA_LOG_INFO("%s: f_logit_scale    = %.1e\n",   __func__, hparams.f_logit_scale);
-    LLAMA_LOG_INFO("%s: n_ff             = %u\n",     __func__, hparams.n_ff);
-    LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
-    LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
-    LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
-    LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
-    LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
-    LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
-    LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
-    LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
-    LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
-    LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
-    LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
-    LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
-    LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
-    LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
+    LLAMA_LOG_INFO("%s: vocab_only       = %d\n",     __func__, hparams.vocab_only);
+
+    if (!hparams.vocab_only) {
+        LLAMA_LOG_INFO("%s: n_ctx_train      = %u\n",     __func__, hparams.n_ctx_train);
+        LLAMA_LOG_INFO("%s: n_embd           = %u\n",     __func__, hparams.n_embd);
+        LLAMA_LOG_INFO("%s: n_layer          = %u\n",     __func__, hparams.n_layer);
+        LLAMA_LOG_INFO("%s: n_head           = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head(il);    }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_head_kv        = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_head_kv(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_rot            = %u\n",     __func__, hparams.n_rot);
+        LLAMA_LOG_INFO("%s: n_swa            = %u\n",     __func__, hparams.n_swa);
+        LLAMA_LOG_INFO("%s: n_embd_head_k    = %u\n",     __func__, hparams.n_embd_head_k);
+        LLAMA_LOG_INFO("%s: n_embd_head_v    = %u\n",     __func__, hparams.n_embd_head_v);
+        LLAMA_LOG_INFO("%s: n_gqa            = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_gqa(il);        }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_embd_k_gqa     = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_k_gqa(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_embd_v_gqa     = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_embd_v_gqa(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: f_norm_eps       = %.1e\n",   __func__, hparams.f_norm_eps);
+        LLAMA_LOG_INFO("%s: f_norm_rms_eps   = %.1e\n",   __func__, hparams.f_norm_rms_eps);
+        LLAMA_LOG_INFO("%s: f_clamp_kqv      = %.1e\n",   __func__, hparams.f_clamp_kqv);
+        LLAMA_LOG_INFO("%s: f_max_alibi_bias = %.1e\n",   __func__, hparams.f_max_alibi_bias);
+        LLAMA_LOG_INFO("%s: f_logit_scale    = %.1e\n",   __func__, hparams.f_logit_scale);
+        LLAMA_LOG_INFO("%s: n_ff             = %s\n",     __func__, print_f([&](uint32_t il) { return hparams.n_ff(il); }, hparams.n_layer).c_str());
+        LLAMA_LOG_INFO("%s: n_expert         = %u\n",     __func__, hparams.n_expert);
+        LLAMA_LOG_INFO("%s: n_expert_used    = %u\n",     __func__, hparams.n_expert_used);
+        LLAMA_LOG_INFO("%s: causal attn      = %d\n",     __func__, hparams.causal_attn);
+        LLAMA_LOG_INFO("%s: pooling type     = %d\n",     __func__, hparams.pooling_type);
+        LLAMA_LOG_INFO("%s: rope type        = %d\n",     __func__, hparams.rope_type);
+        LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
+        LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
+        LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
+        LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
+        LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
+        LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
+        LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
+        LLAMA_LOG_INFO("%s: ssm_d_state      = %u\n",     __func__, hparams.ssm_d_state);
+        LLAMA_LOG_INFO("%s: ssm_dt_rank      = %u\n",     __func__, hparams.ssm_dt_rank);
+    }
+
     LLAMA_LOG_INFO("%s: model type       = %s\n",     __func__, llama_model_type_name(model.type));
     LLAMA_LOG_INFO("%s: model ftype      = %s\n",     __func__, llama_model_ftype_name(model.ftype).c_str());
     if (ml.n_elements >= 1e12) {
@@ -7122,13 +7423,13 @@ static bool llm_load_tensors(
     // create tensors for the weights
     {
         const int64_t n_embd       = hparams.n_embd;
-        const int64_t n_embd_head  = (hparams.n_head == 0) ? 0 : n_embd / hparams.n_head;
+        const int64_t n_embd_head  = hparams.n_head() > 0 ? n_embd / hparams.n_head() : 0;
         const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
         const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
         const int64_t n_embd_gqa   = n_embd_v_gqa;
         const int64_t n_vocab      = hparams.n_vocab;
         const int64_t n_vocab_type = hparams.n_vocab_type;
-        const int64_t n_ff         = hparams.n_ff;
+        const int64_t n_ff         = hparams.n_ff();
         const int64_t n_expert     = hparams.n_expert;
 
         if (n_expert > 0 && hparams.n_expert_used == 0) {
@@ -7649,8 +7950,8 @@ static bool llm_load_tensors(
                         layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V,   "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         // optional q and k layernorms, present in StableLM 2 12B
-                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head()}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv()}, llama_model_loader::TENSOR_NOT_REQUIRED);
 
                         // optional FFN norm, not present in StableLM 2 12B which uses parallel residual
                         layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
@@ -8021,6 +8322,7 @@ static bool llm_load_tensors(
                     model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                     model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
 
+                    const int64_t n_ff          = hparams.n_ff();
                     const int64_t n_embd_head_k = hparams.n_embd_head_k;
 
                     for (uint32_t i = 0; i < n_layer; ++i) {
@@ -8031,10 +8333,10 @@ static bool llm_load_tensors(
 
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head()});
                         layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
                         layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head(), n_embd});
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
@@ -8050,7 +8352,7 @@ static bool llm_load_tensors(
                     model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
                     model.output      = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD,  "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading
 
-                    const int64_t n_ff          = hparams.n_ff;
+                    const int64_t n_ff          = hparams.n_ff();
                     const int64_t n_embd_head_k = hparams.n_embd_head_k;
                     const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa();
                     const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
@@ -8063,10 +8365,10 @@ static bool llm_load_tensors(
 
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
-                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head});
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * hparams.n_head()});
                         layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
                         layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head, n_embd});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * hparams.n_head(), n_embd});
                         layer.attn_post_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_POST_NORM, "weight", i), {n_embd});
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
@@ -8182,7 +8484,6 @@ static bool llm_load_tensors(
 
                     // only an expansion factor of 2 is supported for now
                     GGML_ASSERT(2 * n_embd == d_inner);
-                    GGML_ASSERT((int64_t) hparams.n_head_kv_vec.size() == n_layer);
 
                     model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
 
@@ -8198,7 +8499,7 @@ static bool llm_load_tensors(
                     }
 
                     for (int i = 0; i < n_layer; ++i) {
-                        const int64_t n_head_kv = hparams.n_head_kv_vec[i];
+                        const int64_t n_head_kv = hparams.n_head_kv(i);
                         const int64_t n_embd_gqa = hparams.n_embd_v_gqa(i);
 
                         ggml_context * ctx_layer = ctx_for_layer(i);
@@ -8327,8 +8628,8 @@ static bool llm_load_tensors(
                         layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
 
                         if (n_layer >= 64){
-                            layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head});
-                            layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv});
+                            layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head()});
+                            layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv()});
                         }
 
                         layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd});
@@ -8370,6 +8671,42 @@ static bool llm_load_tensors(
                         layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
                     }
                 } break;
+            case LLM_ARCH_OPENELM:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        // init output from the input tok embed
+                        model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        const int64_t n_head = hparams.n_head(i);
+                        const int64_t n_head_qkv = 2*hparams.n_head_kv(i) + n_head;
+                        const int64_t n_embd_head = hparams.n_embd_head_k;
+
+                        const int64_t n_ff = hparams.n_ff(i);
+
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_head_qkv*n_embd_head});
+                        layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head});
+                        layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_head*n_embd_head, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                    }
+                } break;
             case LLM_ARCH_GPTNEOX:
                 {
                     model.tok_embd   = ml.create_tensor(ctx_input,  tn(LLM_TENSOR_TOKEN_EMBD,      "weight"), {n_embd, n_vocab});
@@ -8477,13 +8814,13 @@ static bool llm_load_tensors(
 
                         if (!is_lite) {
                             layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A,   "weight", i), {n_embd, q_lora_rank});
-                            layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B,   "weight", i), {q_lora_rank, hparams.n_head * hparams.n_embd_head_k});
+                            layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B,   "weight", i), {q_lora_rank, hparams.n_head() * hparams.n_embd_head_k});
                         } else {
                             layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
                         }
                         layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA,   "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope});
-                        layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B,   "weight", i), {kv_lora_rank, hparams.n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)});
-                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {hparams.n_head * hparams.n_embd_head_v, n_embd});
+                        layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, hparams.n_head() * (n_embd_head_qk_nope + hparams.n_embd_head_v)});
+                        layer.wo    = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT,  "weight", i), {              hparams.n_head() * (                      hparams.n_embd_head_v), n_embd});
 
                         layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
 
@@ -8547,6 +8884,102 @@ static bool llm_load_tensors(
                         layer.ffn_up_scale = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "scale", i), {1});
                     }
                 } break;
+            case LLM_ARCH_T5:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm_enc = ml.create_tensor(ctx_output, tn(LLM_TENSOR_ENC_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm     = ml.create_tensor(ctx_output, tn(LLM_TENSOR_DEC_OUTPUT_NORM, "weight"), {n_embd});
+
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
+                        }
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm_enc  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_ATTN_NORM,  "weight", i), {n_embd});
+                        layer.attn_rel_b_enc = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ENC_ATTN_REL_B, "weight", i), {hparams.n_head(), hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        layer.wq_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wk_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
+
+                        layer.ffn_norm_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate_enc = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ENC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_down_enc = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up_enc   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ENC_FFN_UP,   "weight", i), {n_embd,   n_ff});
+
+                        layer.attn_norm  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_ATTN_NORM,  "weight", i), {n_embd});
+                        layer.attn_rel_b = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_ATTN_REL_B, "weight", i), {hparams.n_head(), hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
+
+                        layer.attn_norm_cross  = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_CROSS_ATTN_NORM,  "weight", i), {n_embd});
+                        // this tensor seems to be unused in HF transformers implementation
+                        layer.attn_rel_b_cross = ml.create_tensor(ctx_input, tn(LLM_TENSOR_DEC_CROSS_ATTN_REL_B, "weight", i), {hparams.n_head(), hparams.n_rel_attn_bkts}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
+                        layer.wq_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wk_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
+                        layer.wv_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
+                        layer.wo_cross = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_CROSS_ATTN_OUT, "weight", i), {n_embd_v_gqa, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_gate = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_DEC_FFN_GATE, "weight", i), {n_embd,   n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_DEC_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                    }
+                } break;
+            case LLM_ARCH_JAIS:
+                {
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // Output
+                    {
+                        model.output_norm   = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output_norm_b = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "bias"),   {n_embd});
+                        model.output        = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "weight", i), {n_embd});
+                        layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM,   "bias", i),   {n_embd});
+
+                        layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa});
+                        layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i),   {n_embd + 2*n_embd_gqa});
+
+                        layer.wo   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd});
+                        layer.bo   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i),   {n_embd});
+
+                        layer.ffn_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+                        layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i),   {n_embd});
+
+                        layer.ffn_down   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd});
+                        layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
+
+                        layer.ffn_gate     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_gate_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE,   "bias", i),   {n_ff});
+
+                        layer.ffn_up     = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd, n_ff});
+                        layer.ffn_up_b   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP,   "bias", i),   {n_ff});
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -9119,12 +9552,12 @@ static struct ggml_tensor * llm_build_kqv(
          const llm_build_cb & cb,
                     int       il) {
     const int64_t n_ctx         = cparams.n_ctx;
-    const int64_t n_head        = hparams.n_head;
-    const int64_t n_head_kv     = hparams.n_head_kv_l(il);
+    const int64_t n_head        = hparams.n_head(il);
+    const int64_t n_head_kv     = hparams.n_head_kv(il);
     const int64_t n_embd_head_k = hparams.n_embd_head_k;
     const int64_t n_embd_k_gqa  = hparams.n_embd_k_gqa(il);
     const int64_t n_embd_head_v = hparams.n_embd_head_v;
-    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa();
+    const int64_t n_embd_v_gqa  = hparams.n_embd_v_gqa(il);
 
     struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3);
     cb(q, "q", il);
@@ -9484,6 +9917,7 @@ struct llm_build_context {
     const int32_t n_kv;     // size of KV cache to consider (n_kv <= kv_self.size)
     const int32_t n_rs;
     const int32_t n_outputs;
+    const int32_t n_outputs_enc;
     const int32_t kv_head;  // index of where we store new KV data in the cache
     const int32_t rs_head;
     const int32_t n_ctx_orig;
@@ -9516,8 +9950,8 @@ struct llm_build_context {
         n_layer          (hparams.n_layer),
         n_rot            (hparams.n_rot),
         n_ctx            (cparams.n_ctx),
-        n_head           (hparams.n_head),
-        n_head_kv        (hparams.n_head_kv),
+        n_head           (hparams.n_head()),
+        n_head_kv        (hparams.n_head_kv()),
         n_embd_head_k    (hparams.n_embd_head_k),
         n_embd_head_v    (hparams.n_embd_head_v),
         n_expert         (hparams.n_expert),
@@ -9536,6 +9970,7 @@ struct llm_build_context {
         n_kv             (worst_case ? kv_self.size : kv_self.n),
         n_rs             (worst_case ? rs_self.size : rs_self.n),
         n_outputs        (worst_case ? n_tokens     : lctx.n_outputs),
+        n_outputs_enc    (worst_case ? n_tokens     : lctx.embd_enc.size() / hparams.n_embd),
         kv_head          (worst_case ? kv_self.size - n_tokens : kv_self.head),
         rs_head          (worst_case ? 0 : rs_self.head),
         n_ctx_orig       (cparams.n_ctx_orig_yarn),
@@ -9556,16 +9991,20 @@ struct llm_build_context {
 
         ctx0 = ggml_init(params);
 
-        lctx.inp_tokens  = nullptr;
-        lctx.inp_embd    = nullptr;
-        lctx.inp_pos     = nullptr;
-        lctx.inp_out_ids = nullptr;
-        lctx.inp_KQ_mask = nullptr;
-        lctx.inp_K_shift = nullptr;
-        lctx.inp_mean    = nullptr;
-        lctx.inp_cls     = nullptr;
-        lctx.inp_s_copy  = nullptr;
-        lctx.inp_s_mask  = nullptr;
+        lctx.inp_tokens      = nullptr;
+        lctx.inp_embd        = nullptr;
+        lctx.inp_pos         = nullptr;
+        lctx.inp_out_ids     = nullptr;
+        lctx.inp_KQ_mask     = nullptr;
+        lctx.inp_KQ_mask_swa = nullptr;
+        lctx.inp_K_shift     = nullptr;
+        lctx.inp_mean        = nullptr;
+        lctx.inp_cls         = nullptr;
+        lctx.inp_s_copy      = nullptr;
+        lctx.inp_s_mask      = nullptr;
+        lctx.inp_pos_bucket    = nullptr;
+        lctx.inp_embd_enc      = nullptr;
+        lctx.inp_KQ_mask_cross = nullptr;
     }
 
     void free() {
@@ -9584,14 +10023,15 @@ struct llm_build_context {
         cb(lctx.inp_K_shift, "K_shift", -1);
         ggml_set_input(lctx.inp_K_shift);
 
-
         for (int il = 0; il < n_layer; ++il) {
+            const int64_t n_head_kv = hparams.n_head_kv(il);
+            const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
             struct ggml_tensor * rope_factors = build_rope_factors(il);
             struct ggml_tensor * tmp =
                 // we rotate only the first n_rot dimensions
                 ggml_rope_ext_inplace(ctx0,
                         ggml_view_3d(ctx0, kv_self.k_l[il],
-                            n_embd_head_k, hparams.n_head_kv_l(il), n_ctx,
+                            n_embd_head_k, n_head_kv, n_ctx,
                             ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
                             ggml_row_size(kv_self.k_l[il]->type, hparams.n_embd_k_gqa(il)),
                             0),
@@ -9622,8 +10062,8 @@ struct llm_build_context {
             }
 
             for (int il = 0; il < n_layer; ++il) {
-                int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
-                int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+                const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+                const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
 
                 ggml_tensor * view_k_src = ggml_view_2d(ctx0, kv_self.k_l[il],
                         n_embd_k_gqa, nm,
@@ -9699,16 +10139,27 @@ struct llm_build_context {
     }
 
     struct ggml_tensor * build_inp_KQ_mask(bool causal = true) {
-        if (causal) {
-            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        } else {
-            lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
-        }
+        lctx.inp_KQ_mask = causal
+            ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
+            : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
         cb(lctx.inp_KQ_mask, "KQ_mask", -1);
         ggml_set_input(lctx.inp_KQ_mask);
+
         return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask;
     }
 
+    struct ggml_tensor * build_inp_KQ_mask_swa(bool causal = true) {
+        GGML_ASSERT(hparams.n_swa > 0);
+
+        lctx.inp_KQ_mask_swa = causal
+            ? ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv,     GGML_PAD(n_tokens, GGML_KQ_MASK_PAD))
+            : ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        cb(lctx.inp_KQ_mask_swa, "KQ_mask_swa", -1);
+        ggml_set_input(lctx.inp_KQ_mask_swa);
+
+        return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask_swa, GGML_TYPE_F16) : lctx.inp_KQ_mask_swa;
+    }
+
     struct ggml_tensor * build_inp_mean() {
         lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens);
         cb(lctx.inp_mean, "inp_mean", -1);
@@ -9781,6 +10232,53 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_tensor * llm_build_pos_bucket(bool causal) {
+        if (causal) {
+            lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_kv,     n_tokens);
+        } else {
+            lctx.inp_pos_bucket = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_tokens);
+        }
+
+        ggml_set_input(lctx.inp_pos_bucket);
+        cb(lctx.inp_pos_bucket, "pos_bucket", -1);
+
+        return lctx.inp_pos_bucket;
+    }
+
+    struct ggml_tensor * llm_build_pos_bias(struct ggml_tensor * pos_bucket, struct ggml_tensor * attn_rel_b) {
+        struct ggml_tensor * pos_bucket_1d = ggml_view_1d(ctx0, pos_bucket, pos_bucket->ne[0] * pos_bucket->ne[1], 0);
+        cb(pos_bucket_1d, "pos_bucket_1d", -1);
+
+        struct ggml_tensor * pos_bias = ggml_get_rows(ctx0, attn_rel_b, pos_bucket_1d);
+        cb(pos_bias, "pos_bias", -1);
+
+        pos_bias = ggml_view_3d(ctx0, pos_bias, pos_bias->ne[0], lctx.inp_pos_bucket->ne[0], lctx.inp_pos_bucket->ne[1], ggml_element_size(pos_bias) * pos_bias->ne[0], ggml_element_size(pos_bias) * pos_bias->ne[0] * lctx.inp_pos_bucket->ne[0],  0);
+        cb(pos_bias, "pos_bias", -1);
+
+        pos_bias = ggml_permute(ctx0, pos_bias, 2, 0, 1, 3);
+        cb(pos_bias, "pos_bias", -1);
+
+        pos_bias = ggml_cont(ctx0, pos_bias);
+        cb(pos_bias, "pos_bias", -1);
+
+        return pos_bias;
+    }
+
+    struct ggml_tensor * llm_build_inp_embd_enc() {
+        const int64_t n_embd = hparams.n_embd;
+        lctx.inp_embd_enc = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_outputs_enc);
+        ggml_set_input(lctx.inp_embd_enc);
+        cb(lctx.inp_embd_enc, "embd_enc", -1);
+        return lctx.inp_embd_enc;
+    }
+
+    struct ggml_tensor * llm_build_inp_KQ_mask_cross() {
+        lctx.inp_KQ_mask_cross = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_outputs_enc, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD));
+        ggml_set_input(lctx.inp_KQ_mask_cross);
+        cb(lctx.inp_KQ_mask_cross, "KQ_mask_cross", -1);
+        return lctx.inp_KQ_mask_cross;
+    }
+
     struct ggml_cgraph * build_llama() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -12782,9 +13280,14 @@ struct llm_build_context {
         struct ggml_tensor * inp_pos = build_inp_pos();
 
         // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
-        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+        // gemma 2 requires different mask for layers using sliding window (SWA)
+        struct ggml_tensor * KQ_mask     = build_inp_KQ_mask(true);
+        struct ggml_tensor * KQ_mask_swa = build_inp_KQ_mask_swa(true);
 
         for (int il = 0; il < n_layer; ++il) {
+            // (il % 2) layers use SWA
+            struct ggml_tensor * KQ_mask_l = (il % 2 == 0) ? KQ_mask_swa : KQ_mask;
+
             // norm
             cur = llm_build_norm(ctx0, inpL, hparams,
                     model.layers[il].attn_norm, NULL,
@@ -12820,7 +13323,7 @@ struct llm_build_context {
 
                 cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il);
+                        Kcur, Vcur, Qcur, KQ_mask_l, n_tokens, kv_head, n_kv, 1.0f, cb, il);
             }
 
             cur = llm_build_norm(ctx0, cur, hparams,
@@ -13082,7 +13585,7 @@ struct llm_build_context {
         struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
 
         for (int il = 0; il < n_layer; ++il) {
-            const int64_t n_head_kv = hparams.n_head_kv_l(il);
+            const int64_t n_head_kv = hparams.n_head_kv(il);
 
             cur = llm_build_norm(ctx0, inpL, hparams,
                     model.layers[il].attn_norm, NULL,
@@ -13461,6 +13964,131 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_openelm() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            const int64_t n_head    = hparams.n_head(il);
+            const int64_t n_head_kv = hparams.n_head_kv(il);
+            const int64_t n_head_qkv = 2*n_head_kv + n_head;
+
+            cur = inpL;
+            struct ggml_tensor * residual = cur;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_reshape_3d(ctx0, cur, n_embd_head_k, n_head_qkv, n_tokens);
+
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head, n_tokens, cur->nb[1], cur->nb[2], 0));
+                cb(Qcur, "Qcur", il);
+
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*n_head));
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_3d(ctx0, cur, n_embd_head, n_head_kv, n_tokens, cur->nb[1], cur->nb[2], cur->nb[1]*(n_head+n_head_kv)));
+                cb(Vcur, "Vcur", il);
+
+                Qcur = llm_build_norm(ctx0, Qcur, hparams,
+                        model.layers[il].attn_q_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(Qcur, "Qcur", il);
+
+                Kcur = llm_build_norm(ctx0, Kcur, hparams,
+                        model.layers[il].attn_k_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(Kcur, "Kcur", il);
+
+                Qcur = ggml_rope_ext(
+                    ctx0, Qcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, Kcur, inp_pos, NULL, n_rot, rope_type, n_ctx_orig,
+                    freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                Vcur = ggml_reshape_2d(ctx0, Vcur, n_embd_head * n_head_kv, n_tokens);
+                cb(Qcur, "Vcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                residual = ggml_get_rows(ctx0, residual, inp_out_ids);
+                cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL, NULL,
+                        model.layers[il].ffn_gate, NULL, NULL,
+                        model.layers[il].ffn_down, NULL, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
+            cur = lctx.cvec.apply_to(ctx0, cur, il);
+            cb(cur, "l_out", il);
+
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        // norm
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
     struct ggml_cgraph * build_gptneox() {
         struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
 
@@ -14108,6 +14736,412 @@ struct llm_build_context {
         return gf;
     }
 
+    struct ggml_cgraph * build_t5() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        if (lctx.is_encoding) {
+            struct ggml_tensor * pos_bucket_enc = llm_build_pos_bucket(false);
+
+            // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+            struct ggml_tensor * KQ_mask_enc = build_inp_KQ_mask(false);
+
+            for (int il = 0; il < n_layer; ++il) {
+                struct ggml_tensor * inpSA = inpL;
+
+                // norm
+                cur = llm_build_norm(ctx0, inpL, hparams,
+                        model.layers[il].attn_norm_enc, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "attn_norm", il);
+
+                // self-attention
+                {
+                    struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_enc, cur);
+                    cb(Qcur, "Qcur", il);
+
+                    struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_enc, cur);
+                    cb(Kcur, "Kcur", il);
+
+                    struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_enc, cur);
+                    cb(Vcur, "Vcur", il);
+
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+
+                    struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                    struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+
+                    struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+                    cb(kq, "kq", il);
+
+                    struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b_enc ? model.layers[il].attn_rel_b_enc : model.layers[0].attn_rel_b_enc;
+                    struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_enc, attn_rel_b);
+                    struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
+                    cb(kq_b, "kq_b", il);
+
+                    kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_enc, 1.0f, hparams.f_max_alibi_bias);
+                    cb(kq, "kq_soft_max_ext", il);
+
+                    struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens)));
+                    cb(v, "v", il);
+
+                    struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_tokens, n_embd_head, n_head_kv), kq);
+                    cb(kqv, "kqv", il);
+
+                    struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                    cb(kqv_merged, "kqv_merged", il);
+
+                    cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+                    cb(cur, "kqv_merged_cont", il);
+
+                    ggml_build_forward_expand(gf, cur);
+
+                    cur = ggml_mul_mat(ctx0, model.layers[il].wo_enc, cur);
+                    cb(cur, "kqv_out", il);
+                }
+
+                if (il == n_layer - 1) {
+                    // skip computing output for unused tokens
+                    struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                    n_tokens = n_outputs;
+                    cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                    inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+                }
+
+                struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+                cb(ffn_inp, "ffn_inp", il);
+
+                // feed-forward network
+                {
+                    cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                            model.layers[il].ffn_norm_enc, NULL,
+                            LLM_NORM_RMS, cb, il);
+                    cb(cur, "ffn_norm", il);
+
+                    // T5 uses relu, flan-T5 uses gelu-gated
+                    cur = llm_build_ffn(ctx0, cur,
+                            model.layers[il].ffn_up_enc,   NULL, NULL,
+                            model.layers[il].ffn_gate_enc, NULL, NULL,
+                            model.layers[il].ffn_down_enc, NULL, NULL,
+                            NULL,
+                            model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
+                            model.layers[il].ffn_gate_enc ? LLM_FFN_PAR  : LLM_FFN_SEQ,
+                            cb, il);
+                    cb(cur, "ffn_out", il);
+                }
+
+                cur = ggml_add(ctx0, cur, ffn_inp);
+                cb(cur, "ffn_out", il);
+
+                ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+                if (layer_dir != nullptr) {
+                    cur = ggml_add(ctx0, cur, layer_dir);
+                }
+                cb(cur, "l_out", il);
+
+                // input for next layer
+                inpL = cur;
+            }
+
+            cur = inpL;
+            cb(cur, "result_embd", -1);
+
+            cur = llm_build_norm(ctx0, cur, hparams,
+                    model.output_norm_enc, NULL,
+                    LLM_NORM_RMS, cb, -1);
+            cb(cur, "result_norm", -1);
+        } else {
+            struct ggml_tensor * embd_enc       = llm_build_inp_embd_enc();
+            struct ggml_tensor * pos_bucket_dec = llm_build_pos_bucket(true);
+
+            struct ggml_tensor * KQ_mask_dec   = build_inp_KQ_mask();
+            struct ggml_tensor * KQ_mask_cross = llm_build_inp_KQ_mask_cross();
+
+            for (int il = 0; il < n_layer; ++il) {
+                struct ggml_tensor * inpSA = inpL;
+
+                // norm
+                cur = llm_build_norm(ctx0, inpL, hparams,
+                        model.layers[il].attn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "attn_norm", il);
+
+                // self-attention
+                {
+                    struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                    cb(Qcur, "Qcur", il);
+
+                    struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                    cb(Kcur, "Kcur", il);
+
+                    struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                    cb(Vcur, "Vcur", il);
+
+                    llm_build_kv_store(ctx0, hparams, cparams, kv_self, gf, Kcur, Vcur, n_tokens, kv_head, cb, il);
+
+                    struct ggml_tensor * k =
+                        ggml_view_3d(ctx0, kv_self.k_l[il],
+                                n_embd_head_k, n_kv, n_head_kv,
+                                ggml_row_size(kv_self.k_l[il]->type, n_embd_gqa),
+                                ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
+                                0);
+                    cb(k, "k", il);
+
+                    struct ggml_tensor * v =
+                        ggml_view_3d(ctx0, kv_self.v_l[il],
+                                n_kv, n_embd_head_v, n_head_kv,
+                                ggml_element_size(kv_self.v_l[il])*n_ctx,
+                                ggml_element_size(kv_self.v_l[il])*n_ctx*n_embd_head_v,
+                                0);
+                    cb(v, "v", il);
+
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+                    struct ggml_tensor * q = ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+
+                    struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+                    cb(kq, "kq", il);
+
+                    struct ggml_tensor * attn_rel_b = model.layers[il].attn_rel_b ? model.layers[il].attn_rel_b : model.layers[0].attn_rel_b;
+                    struct ggml_tensor * pos_bias = llm_build_pos_bias(pos_bucket_dec, attn_rel_b);
+                    struct ggml_tensor * kq_b = ggml_add(ctx0, kq, pos_bias);
+                    cb(kq_b, "kq_b", il);
+
+                    kq = ggml_soft_max_ext(ctx0, kq_b, KQ_mask_dec, 1.0f, hparams.f_max_alibi_bias);
+                    cb(kq, "kq_soft_max_ext", il);
+
+                    struct ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq);
+                    cb(kqv, "kqv", il);
+
+                    struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                    cb(kqv_merged, "kqv_merged", il);
+
+                    cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+                    cb(cur, "kqv_merged_cont", il);
+
+                    ggml_build_forward_expand(gf, cur);
+
+                    cur = ggml_mul_mat(ctx0, model.layers[il].wo, cur);
+                    cb(cur, "kqv_out", il);
+                }
+
+                cur = ggml_add(ctx0, cur, inpSA);
+                cb(cur, "cross_inp", il);
+
+                struct ggml_tensor * inpCA = cur;
+
+                // norm
+                cur = llm_build_norm(ctx0, cur, hparams,
+                        model.layers[il].attn_norm_cross, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "attn_norm_cross", il);
+
+                // cross-attention
+                {
+                    struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq_cross, cur);
+                    cb(Qcur, "Qcur", il);
+
+                    struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk_cross, embd_enc);
+                    cb(Kcur, "Kcur", il);
+
+                    struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv_cross, embd_enc);
+                    cb(Vcur, "Vcur", il);
+
+                    Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens);
+                    Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_outputs_enc);
+
+                    struct ggml_tensor * q =                 ggml_permute(ctx0, Qcur, 0, 2, 1, 3);
+                    struct ggml_tensor * k = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 0, 2, 1, 3));
+
+                    struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q);
+                    cb(kq, "kq", il);
+
+                    kq = ggml_soft_max_ext(ctx0, kq, KQ_mask_cross, 1.0f, hparams.f_max_alibi_bias);
+                    cb(kq, "kq_soft_max_ext", il);
+
+                    struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_outputs_enc)));
+                    cb(v, "v", il);
+
+                    struct ggml_tensor * kqv = ggml_mul_mat(ctx0, ggml_reshape_3d(ctx0, v, n_outputs_enc, n_embd_head, n_head_kv), kq);
+                    cb(kqv, "kqv", il);
+
+                    struct ggml_tensor * kqv_merged = ggml_permute(ctx0, kqv, 0, 2, 1, 3);
+                    cb(kqv_merged, "kqv_merged", il);
+
+                    cur = ggml_cont_2d(ctx0, kqv_merged, n_embd_gqa, n_tokens);
+                    cb(cur, "kqv_merged_cont", il);
+
+                    ggml_build_forward_expand(gf, cur);
+
+                    cur = ggml_mul_mat(ctx0, model.layers[il].wo_cross, cur);
+                    cb(cur, "kqv_out", il);
+                }
+
+                if (il == n_layer - 1) {
+                    // skip computing output for unused tokens
+                    struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                    n_tokens = n_outputs;
+                    cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                    inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+                    inpCA = ggml_get_rows(ctx0, inpCA, inp_out_ids);
+                }
+
+                struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpCA);
+                cb(ffn_inp, "ffn_inp", il);
+
+                // feed-forward network
+                {
+                    cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                            model.layers[il].ffn_norm, NULL,
+                            LLM_NORM_RMS, cb, il);
+                    cb(cur, "ffn_norm", il);
+
+                    // T5 uses relu, flan-T5 uses gelu-gated
+                    cur = llm_build_ffn(ctx0, cur,
+                            model.layers[il].ffn_up,   NULL, NULL,
+                            model.layers[il].ffn_gate, NULL, NULL,
+                            model.layers[il].ffn_down, NULL, NULL,
+                            NULL,
+                            model.layers[il].ffn_gate_enc ? LLM_FFN_GELU : LLM_FFN_RELU,
+                            model.layers[il].ffn_gate_enc ? LLM_FFN_PAR : LLM_FFN_SEQ,
+                            cb, il);
+                    cb(cur, "ffn_out", il);
+                }
+
+                cur = ggml_add(ctx0, cur, ffn_inp);
+                cb(cur, "ffn_out", il);
+
+                ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+                if (layer_dir != nullptr) {
+                    cur = ggml_add(ctx0, cur, layer_dir);
+                }
+                cb(cur, "l_out", il);
+
+                // input for next layer
+                inpL = cur;
+            }
+
+            cur = inpL;
+            cb(cur, "result_embd", -1);
+
+            cur = llm_build_norm(ctx0, cur, hparams,
+                    model.output_norm, NULL,
+                    LLM_NORM_RMS, cb, -1);
+            cb(cur, "result_norm", -1);
+
+            // lm_head
+            cur = ggml_mul_mat(ctx0, model.output, cur);
+            cb(cur, "result_output", -1);
+        }
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_jais() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        const int64_t n_embd_head = hparams.n_embd_head_v;
+        const int64_t n_embd_gqa  = hparams.n_embd_v_gqa();
+        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm,
+                    model.layers[il].attn_norm_b,
+                    LLM_NORM, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self-attention
+            {
+                cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur);
+                cb(cur, "wqkv", il);
+
+                cur = ggml_add(ctx0, cur, model.layers[il].bqkv);
+                cb(cur, "bqkv", il);
+
+                struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd,     n_tokens, cur->nb[1], 0*cur->nb[0]*(n_embd)));
+                struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd)));
+                struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*cur->nb[0]*(n_embd + n_embd_gqa)));
+
+                cb(Qcur, "Qcur", il);
+                cb(Kcur, "Kcur", il);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, model.layers[il].bo,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/float(n_embd_head), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                cur  = ggml_get_rows(ctx0,  cur, inp_out_ids);
+                inpL = ggml_get_rows(ctx0, inpL, inp_out_ids);
+            }
+
+            // add the input
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // FF
+            {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm,
+                        model.layers[il].ffn_norm_b,
+                        LLM_NORM, cb, il);
+                cb(cur, "ffn_norm", il);
+
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            }
+
+            inpL = ggml_add(ctx0, cur, ffn_inp);
+            cb(inpL, "l_out", il);
+        }
+
+        cur = llm_build_norm(ctx0, inpL, hparams,
+                model.output_norm,
+                model.output_norm_b,
+                LLM_NORM, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
 };
 
 static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
@@ -14310,6 +15344,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_olmo();
             } break;
+        case LLM_ARCH_OPENELM:
+            {
+                result = llm.build_openelm();
+            } break;
         case LLM_ARCH_GPTNEOX:
             {
                 result = llm.build_gptneox();
@@ -14326,6 +15364,14 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_bitnet();
             } break;
+        case LLM_ARCH_T5:
+            {
+                result = llm.build_t5();
+            } break;
+        case LLM_ARCH_JAIS:
+            {
+                result = llm.build_jais();
+            } break;
         default:
             GGML_ASSERT(false);
     }
@@ -14352,6 +15398,30 @@ static void llama_set_k_shift(llama_context & lctx) {
     }
 }
 
+static int32_t llama_relative_position_bucket(llama_pos x, llama_pos y, uint64_t n_buckets, bool bidirectional) {
+    // TODO move to hparams if a T5 variant appears that uses a different value
+    const int64_t max_distance = 128;
+
+    if (bidirectional) {
+        n_buckets >>= 1;
+    }
+
+    const int64_t max_exact = n_buckets >> 1;
+
+    int32_t relative_position = x - y;
+    int32_t relative_bucket = 0;
+    if (bidirectional) {
+        relative_bucket += (relative_position > 0) * n_buckets;
+        relative_position = abs(relative_position);
+    } else {
+        relative_position = -std::min<int32_t>(relative_position, 0);
+    }
+    int32_t relative_position_if_large = floorf(max_exact + logf(1.0 * relative_position / max_exact) * (n_buckets - max_exact) / log(1.0 * max_distance / max_exact));
+    relative_position_if_large = std::min<int32_t>(relative_position_if_large, n_buckets - 1);
+    relative_bucket += (relative_position < max_exact ? relative_position : relative_position_if_large);
+    return relative_bucket;
+}
+
 static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
     //
     // set input data
@@ -14418,7 +15488,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
 
     if (lctx.inp_KQ_mask) {
         // NOTE: hparams.causal_attn indicates the model is capable of generation and uses the kv cache.
-        if (cparams.causal_attn) {
+        if (cparams.causal_attn && !lctx.is_encoding) {
             const int64_t n_kv         = kv_self.n;
             const int64_t n_tokens     = batch.n_tokens;
             const int64_t n_seq_tokens = batch.n_seq_tokens;
@@ -14426,7 +15496,12 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
 
             GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
 
-            float * data = (float *) lctx.inp_KQ_mask->data;
+            float * data     = (float *) lctx.inp_KQ_mask->data;
+            float * data_swa = nullptr;
+
+            if (lctx.inp_KQ_mask_swa) {
+                data_swa = (float *) lctx.inp_KQ_mask_swa->data;
+            }
 
             // For causal attention, use only the previous KV cells
             // of the correct sequence for each token of the batch.
@@ -14449,7 +15524,15 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
                                     f = 0.0f;
                                 }
                             }
-                            data[h*(n_kv*n_seq_tokens*n_seqs) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
+                            data[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
+
+                            // may need to cut off old tokens for sliding window
+                            if (data_swa) {
+                                if (pos - kv_self.cells[i].pos >= (int32_t)hparams.n_swa) {
+                                    f = -INFINITY;
+                                }
+                                data_swa[h*(n_kv*n_tokens) + s*(n_kv*n_seq_tokens) + j*n_kv + i] = f;
+                            }
                         }
                     }
                 }
@@ -14465,7 +15548,7 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
             const int64_t n_seq_tokens = batch.n_seq_tokens;
             const int64_t n_seqs       = batch.n_seqs;
             // when using kv cache, the mask needs to match the kv cache size
-            const int64_t n_stride = hparams.causal_attn ? kv_self.n : n_tokens;
+            const int64_t n_stride = hparams.causal_attn && !lctx.is_encoding ? kv_self.n : n_tokens;
 
             GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer));
 
@@ -14650,6 +15733,72 @@ static void llama_set_inputs(llama_context & lctx, const llama_ubatch & batch) {
             }
         }
     }
+
+    if (lctx.inp_pos_bucket) {
+        const int64_t n_tokens = batch.n_tokens;
+
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_pos_bucket->buffer));
+
+        int32_t * data = (int32_t *) lctx.inp_pos_bucket->data;
+
+        // FIXME: use batch.n_seqs
+        if (!lctx.is_encoding) {
+            const int64_t n_kv = kv_self.n;
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    for (int i = 0; i < n_kv; ++i) {
+                        data[h*(n_kv*n_tokens) + j*n_kv + i] = llama_relative_position_bucket(lctx.cache.kv.cells[i].pos, batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
+                    }
+                }
+            }
+        } else {
+            for (int h = 0; h < 1; ++h) {
+                for (int j = 0; j < n_tokens; ++j) {
+                    for (int i = 0; i < n_tokens; ++i) {
+                        data[h*(n_tokens*n_tokens) + j*n_tokens + i] = llama_relative_position_bucket(batch.pos[i], batch.pos[j], hparams.n_rel_attn_bkts, lctx.is_encoding);
+                    }
+                }
+            }
+        }
+    }
+
+    if (!lctx.is_encoding && lctx.inp_embd_enc) {
+        assert(lctx.inp_embd_enc->type == GGML_TYPE_F32);
+        assert((size_t) ggml_nelements(lctx.inp_embd_enc) == lctx.embd_enc.size());
+
+        ggml_backend_tensor_set(lctx.inp_embd_enc, lctx.embd_enc.data(), 0, ggml_nbytes(lctx.inp_embd_enc));
+    }
+
+    if (!lctx.is_encoding && lctx.inp_KQ_mask_cross) {
+        const int64_t n_output_enc = lctx.embd_enc.size() / hparams.n_embd;
+        const int64_t n_tokens = batch.n_tokens;
+
+        GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask_cross->buffer));
+
+        float * data = (float *) lctx.inp_KQ_mask_cross->data;
+
+        // FIXME: use batch.n_seqs
+        for (int h = 0; h < 1; ++h) {
+            for (int j = 0; j < n_tokens; ++j) {
+                for (int i = 0; i < n_output_enc; ++i) {
+                    float f = -INFINITY;
+                    for (int s = 0; s < batch.n_seq_id[j]; ++s) {
+                        const llama_seq_id seq_id = batch.seq_id[j][s];
+                        if (lctx.seq_ids_enc[i].find(seq_id) != lctx.seq_ids_enc[i].end()) {
+                            f = 0.0f;
+                        }
+                    }
+                    data[h*(n_output_enc*n_tokens) + j*n_output_enc + i] = f;
+                }
+            }
+
+            for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
+                for (int j = 0; j < n_output_enc; ++j) {
+                    data[h*(n_output_enc*n_tokens) + i*n_output_enc + j] = -INFINITY;
+                }
+            }
+        }
+    }
 }
 
 // Make sure enough space is available for outputs.
@@ -14666,7 +15815,7 @@ static size_t llama_output_reserve(llama_context & lctx, size_t n_outputs) {
 
     // TODO: use a per-batch flag for logits presence instead
     const bool has_logits = !cparams.embeddings;
-    const bool has_embd   =  cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE);
+    const bool has_embd   =  lctx.is_encoding || (cparams.embeddings && (cparams.pooling_type == LLAMA_POOLING_TYPE_NONE));
 
     const size_t logits_size = has_logits ? n_vocab*n_outputs_max : 0;
     const size_t embd_size   = has_embd   ?  n_embd*n_outputs_max : 0;
@@ -14758,6 +15907,7 @@ static int llama_decode_internal(
          llama_context & lctx,
            llama_batch   batch_all) { // TODO: rename back to batch
 
+    lctx.is_encoding = false;
     const uint32_t n_tokens_all = batch_all.n_tokens;
 
     if (n_tokens_all == 0) {
@@ -14791,6 +15941,12 @@ static int llama_decode_internal(
 
     const auto n_ubatch = cparams.n_ubatch;
 
+    // TODO: simplify or deprecate
+    std::vector<llama_pos> pos;
+    std::vector<int32_t>                   n_seq_id;
+    std::vector<llama_seq_id *>            seq_id_arr;
+    std::vector<std::vector<llama_seq_id>> seq_id;
+
     // count outputs
     if (cparams.embeddings && cparams.pooling_type != LLAMA_POOLING_TYPE_NONE) {
         n_outputs = n_tokens_all;
@@ -15009,6 +16165,110 @@ static int llama_decode_internal(
     return 0;
 }
 
+// encode a batch of tokens by evaluating the encoder part of the transformer
+//
+//   - lctx:      llama context
+//   - batch:     batch to evaluate
+//
+// return 0 on success
+// return positive int on warning
+// return negative int on error
+//
+static int llama_encode_internal(
+         llama_context & lctx,
+           llama_batch   batch) {
+
+    lctx.is_encoding = true;
+
+    const uint32_t n_tokens = batch.n_tokens;
+
+    if (n_tokens == 0) {
+        LLAMA_LOG_ERROR("%s: n_tokens == 0", __func__);
+        return -1;
+    }
+
+    const auto & model   = lctx.model;
+    const auto & hparams = model.hparams;
+    const auto & cparams = lctx.cparams;
+
+    GGML_ASSERT((!batch.token && batch.embd) || (batch.token && !batch.embd)); // NOLINT
+
+    // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
+    GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");
+
+    if (lctx.t_compute_start_us == 0) {
+        lctx.t_compute_start_us = ggml_time_us();
+    }
+
+    lctx.n_queued_tokens += n_tokens;
+
+    const int64_t n_embd = hparams.n_embd;
+
+    lctx.sbatch.from_batch(batch, n_embd);
+
+    const llama_ubatch ubatch = lctx.sbatch.split_simple(n_tokens);
+
+    // reserve output buffer
+    if (llama_output_reserve(lctx, n_tokens) < n_tokens) {
+        LLAMA_LOG_ERROR("%s: could not reserve space for batch with %u outputs\n", __func__, n_tokens);
+        return -2;
+    };
+
+    for (uint32_t i = 0; i < n_tokens; ++i) {
+        lctx.output_ids[i] = i;
+    }
+
+    lctx.inp_embd_enc = NULL;
+    lctx.n_outputs = n_tokens;
+
+    const int n_threads = n_tokens == 1 ? cparams.n_threads : cparams.n_threads_batch;
+    GGML_ASSERT(n_threads > 0);
+
+    ggml_backend_sched_reset(lctx.sched);
+    ggml_backend_sched_set_eval_callback(lctx.sched, lctx.cparams.cb_eval, lctx.cparams.cb_eval_user_data);
+
+    ggml_cgraph * gf = llama_build_graph(lctx, ubatch, false);
+
+    // the output embeddings after the final encoder normalization
+    struct ggml_tensor * embd = gf->nodes[gf->n_nodes - 1];
+
+    GGML_ASSERT(strcmp(embd->name, "result_norm") == 0);
+
+    ggml_backend_sched_alloc_graph(lctx.sched, gf);
+
+    llama_set_inputs(lctx, ubatch);
+
+    llama_graph_compute(lctx, gf, n_threads);
+
+    // extract embeddings
+    if (embd) {
+        ggml_backend_t backend_embd = ggml_backend_sched_get_tensor_backend(lctx.sched, embd);
+        GGML_ASSERT(backend_embd != nullptr);
+
+        // extract token embeddings
+        GGML_ASSERT(lctx.embd != nullptr);
+
+        lctx.embd_enc.resize(n_tokens*n_embd);
+        float * embd_out = lctx.embd_enc.data();
+
+        ggml_backend_tensor_get_async(backend_embd, embd, embd_out, 0, n_tokens*n_embd*sizeof(float));
+
+        // remember the sequence ids used during the encoding - needed for cross attention later
+        lctx.seq_ids_enc.resize(n_tokens);
+        for (uint32_t i = 0; i < n_tokens; i++) {
+            for (int s = 0; s < batch.n_seq_id[i]; s++) {
+                llama_seq_id seq_id = batch.seq_id[i][s];
+                lctx.seq_ids_enc[i].insert(seq_id);
+            }
+        }
+    }
+
+    // Reset state for the next token before backend sync, to allow the CPU activities in the reset to
+    // overlap with device computation.
+    ggml_backend_sched_reset(lctx.sched);
+
+    return 0;
+}
 
 // find holes from the beginning of the KV cache and fill them by moving data from the end of the cache
 static void llama_kv_cache_defrag_internal(struct llama_context & lctx) {
@@ -15623,6 +16883,7 @@ struct llm_tokenizer_bpe {
                 break;
             case LLAMA_VOCAB_PRE_TYPE_GPT2:
             case LLAMA_VOCAB_PRE_TYPE_OLMO:
+            case LLAMA_VOCAB_PRE_TYPE_JAIS:
                 regex_exprs = {
                     "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)",
                 };
@@ -16055,11 +17316,14 @@ struct llm_tokenizer_ugm {
         std::string normalized;
         normalize(text, &normalized);
         size_t input_len = normalized.size();
+        if (input_len == 0) {
+            return;
+        }
 
         // initialize score_sum to -FLT_MAX so it will be always lower than sums of token scores
-        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {0, 0, -FLT_MAX});
+        std::vector<struct best_tokenization> tokenization_results(input_len + 1, {vocab.special_unk_id, 0, -FLT_MAX});
         // at the beginning tokenization score is zero
-        tokenization_results[0] = { 0, 0, 0 };
+        tokenization_results[0] = { vocab.special_unk_id, 0, 0 };
 
         for (size_t input_offset = 0; input_offset < input_len;) {
             size_t prefix_offset = input_offset;
@@ -16079,7 +17343,7 @@ struct llm_tokenizer_ugm {
                         single_codepoint_token_found = true;
                     }
                     llama_token token_id = node->value;
-                    const auto &token_data = vocab.id_to_token[token_id];
+                    const auto & token_data = vocab.id_to_token[token_id];
 
                     // we set the user-defined token scores to 0 to make them more likely to be selected
                     // (normal token scores are log probabilities, so they are negative)
@@ -16289,7 +17553,7 @@ private:
                 size_t prefix_offset = input_offset;
                 unicode_cpt_from_utf8(input, prefix_offset);
                 return { &input[input_offset], prefix_offset - input_offset, prefix_offset - input_offset };
-            } catch(std::invalid_argument & ex) {
+            } catch (std::invalid_argument & /*ex*/) {
                 // if no, consume 1 byte and return U+FFFD - REPLACEMENT CHARACTER
                 return { "\xEF\xBF\xBD", 3, 1 };
             }
@@ -18362,14 +19626,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
 
     // sanity checks
     {
-        const auto & n_head_kv_vec = model.hparams.n_head_kv_vec;
-        int n_attn_layer;
-        if (model.hparams.n_head_kv == 0) {
-            // Mamba models don't have attention layers
-            n_attn_layer = 0;
-        } else {
-            // Transformers and hybrid models (like Jamba) have attention layers
-            n_attn_layer = model.hparams.n_layer - std::count(n_head_kv_vec.begin(), n_head_kv_vec.end(), 0);
+        const auto & n_head_kv_iter = model.hparams.n_head_kv_arr.begin();
+        // attention layers have a non-zero number of kv heads
+        int32_t n_attn_layer = model.hparams.n_layer - std::count(n_head_kv_iter, n_head_kv_iter + model.hparams.n_layer, 0);
+        if (llama_model_has_encoder(&model)) {
+            n_attn_layer *= 3;
         }
         GGML_ASSERT((qs.n_attention_wv == n_attn_layer) && "n_attention_wv is unexpected");
     }
@@ -18497,6 +19758,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         quantize &= name.find("ssm_x.weight")      == std::string::npos;
         quantize &= name.find("ssm_dt.weight")     == std::string::npos;
 
+        // do not quantize relative position bias (T5)
+        quantize &= name.find("attn_rel_b.weight") == std::string::npos;
+
         enum ggml_type new_type;
         void * new_data;
         size_t new_size;
@@ -19522,6 +20786,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_JAMBA:
         case LLM_ARCH_JINA_BERT_V2:
         case LLM_ARCH_T5:
+        case LLM_ARCH_JAIS:
             return LLAMA_ROPE_TYPE_NONE;
 
         // use what we call a normal RoPE, operating on pairs of consecutive head values
@@ -19556,6 +20821,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_GEMMA:
         case LLM_ARCH_GEMMA2:
         case LLM_ARCH_STARCODER2:
+        case LLM_ARCH_OPENELM:
         case LLM_ARCH_GPTNEOX:
             return LLAMA_ROPE_TYPE_NEOX;
 
@@ -19665,6 +20931,17 @@ struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const ch
     return it->second;
 }
 
+bool llama_model_has_encoder(const struct llama_model * model) {
+    switch (model->arch) {
+        case LLM_ARCH_T5: return true;
+        default:          return false;
+    }
+}
+
+llama_token llama_model_decoder_start_token(const struct llama_model * model) {
+    return model->hparams.dec_start_token_id;
+}
+
 uint32_t llama_model_quantize(
         const char * fname_inp,
         const char * fname_out,
@@ -20192,8 +21469,6 @@ static void llama_state_get_data_internal(struct llama_context * ctx, llama_data
         const auto & hparams = ctx->model.hparams;
 
         const uint32_t n_layer      = hparams.n_layer;
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); // FIXME: per layer
-        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
         // NOTE: kv_size and kv_buf_size are mostly used for sanity checks
         const uint32_t kv_head     = llama_kv_cache_cell_max(kv_self);
@@ -20213,6 +21488,9 @@ static void llama_state_get_data_internal(struct llama_context * ctx, llama_data
 
             std::vector<uint8_t> tmp_buf;
             for (int il = 0; il < (int) n_layer; ++il) {
+                const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
                 const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
 
                 tmp_buf.resize(k_size);
@@ -20344,8 +21622,6 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
         const auto & hparams = ctx->model.hparams;
 
         const uint32_t n_layer      = hparams.n_layer;
-        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); // FIXME: per layer
-        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
         size_t   kv_buf_size;
         uint32_t kv_head;
@@ -20377,6 +21653,9 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) {
             GGML_ASSERT(kv_self.total_size() >= kv_buf_size);
 
             for (int il = 0; il < (int) n_layer; ++il) {
+                const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+                const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
                 const size_t k_size = ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*kv_head);
 
                 ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size);
@@ -20537,8 +21816,6 @@ size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id)
     const auto & hparams = ctx->model.hparams;
 
     const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); // FIXME: per layer
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
     for (uint32_t i = 0; i < kv_self.size; ++i) {
         const auto & cell = kv_self.cells[i];
@@ -20549,6 +21826,9 @@ size_t llama_state_seq_get_size(struct llama_context* ctx, llama_seq_id seq_id)
     }
 
     for (int il = 0; il < (int)n_layer; ++il) {
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+        const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
         // types of keys and values
         s_cell_data_size += sizeof(int32_t) * 2;
         // k_size_row and v_size_el values of layer
@@ -20623,14 +21903,15 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam
 
     const auto & hparams = ctx->model.hparams;
     const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); // FIXME: per layer
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
 
     // Write the layer count
     data_ctx.write(&n_layer, sizeof(n_layer));
 
-    // Write n_embd_v_gqa
-    data_ctx.write(&n_embd_v_gqa, sizeof(n_embd_v_gqa));
+    // Write n_embd_v_gqa (reference value)
+    {
+        const uint32_t n_embd_v_gqa_ref = hparams.n_embd_v_gqa();
+        data_ctx.write(&n_embd_v_gqa_ref, sizeof(n_embd_v_gqa_ref));
+    }
 
     // Iterate the ranges and write all the pos (this is the token position in the prompt)
     for (const auto & range : cell_ranges) {
@@ -20644,6 +21925,8 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam
     // Get whole range at a time
     std::vector<uint8_t> tmp_buf;
     for (int il = 0; il < (int)n_layer; ++il) {
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+
         // Write key type
         const int32_t k_type_i = (int32_t)kv_self.k_l[il]->type;
         data_ctx.write(&k_type_i, sizeof(k_type_i));
@@ -20664,6 +21947,8 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam
     // TODO: simplify, reduce copy-paste
     if (!kv_self.v_trans) {
         for (int il = 0; il < (int)n_layer; ++il) {
+            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
             // Write value type
             const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
             data_ctx.write(&v_type_i, sizeof(v_type_i));
@@ -20684,6 +21969,8 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam
         // For the values, they are transposed, so we also need the element size and get the element ranges from each row
         const uint32_t kv_size = kv_self.size;
         for (int il = 0; il < (int)n_layer; ++il) {
+            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
             // Write value type
             const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type;
             data_ctx.write(&v_type_i, sizeof(v_type_i));
@@ -20753,14 +22040,14 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src,
     // Sanity check model compatibility
     const auto & hparams = ctx->model.hparams;
     const uint32_t n_layer = hparams.n_layer;
-    const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(); // FIXME: per layer
-    const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa();
+
     if (n_layer != n_layer_ref) {
         LLAMA_LOG_ERROR("%s: mismatched n_layer (%d != %d)\n", __func__, n_layer, n_layer_ref);
         return 0;
     }
-    if (n_embd_v_gqa != n_embd_v_gqa_ref) {
-        LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, n_embd_v_gqa, n_embd_v_gqa_ref);
+
+    if (hparams.n_embd_v_gqa() != n_embd_v_gqa_ref) {
+        LLAMA_LOG_ERROR("%s: mismatched n_embd_v_gqa (%d != %d)\n", __func__, hparams.n_embd_v_gqa(), n_embd_v_gqa_ref);
         return 0;
     }
 
@@ -20798,6 +22085,8 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src,
 
     // For each layer, read the keys for each cell, one row is one cell, read as one contiguous blo
     for (int il = 0; il < (int)n_layer; ++il) {
+        const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa(il);
+
         // Read type of key
         int32_t k_type_i_ref;
         memcpy(&k_type_i_ref, inp, sizeof(k_type_i_ref));
@@ -20830,6 +22119,8 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src,
     // TODO: simplify, reduce copy-paste
     if (!kv_self.v_trans) {
         for (int il = 0; il < (int)n_layer; ++il) {
+            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
             // Read type of value
             int32_t v_type_i_ref;
             memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
@@ -20861,6 +22152,8 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src,
     } else {
         // For each layer, read the values for each cell (transposed)
         for (int il = 0; il < (int)n_layer; ++il) {
+            const uint32_t n_embd_v_gqa = hparams.n_embd_v_gqa(il);
+
             // Read type of value
             int32_t v_type_i_ref;
             memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref));
@@ -21061,6 +22354,17 @@ void llama_batch_free(struct llama_batch batch) {
     if (batch.logits)   free(batch.logits);
 }
 
+int32_t llama_encode(
+        struct llama_context * ctx,
+          struct llama_batch   batch) {
+    const int ret = llama_encode_internal(*ctx, batch);
+    if (ret < 0) {
+        LLAMA_LOG_ERROR("%s: failed to encode, ret = %d\n", __func__, ret);
+    }
+
+    return ret;
+}
+
 int32_t llama_decode(
         struct llama_context * ctx,
           struct llama_batch   batch) {
@@ -21369,7 +22673,7 @@ static std::string llama_decode_text(const std::string & text) {
         const auto utf8 = unicode_cpt_to_utf8(cpt);
         try {
             decoded_text += unicode_utf8_to_byte(utf8);
-        } catch (const std::out_of_range & e) {
+        } catch (const std::out_of_range & /*e*/) {
             decoded_text += "[UNK_BYTE_0x";
             for (const auto c : utf8) {
                 decoded_text += format("%02x", (uint8_t) c);
diff --git a/src/unicode-data.cpp b/src/unicode-data.cpp
index 4a939898b..02bdf7823 100644
--- a/src/unicode-data.cpp
+++ b/src/unicode-data.cpp
@@ -7030,4 +7030,3 @@ const std::vector<range_nfd> unicode_ranges_nfd = {  // start, last, nfd
 {0x02FA1C, 0x02FA1C, 0x009F3B},
 {0x02FA1D, 0x02FA1D, 0x02A600},
 };
-
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index f74c0db47..2bb71ac03 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -2052,6 +2052,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
         GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S,
         GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M,
         GGML_TYPE_IQ4_NL, GGML_TYPE_IQ3_S, GGML_TYPE_IQ4_XS,
+        GGML_TYPE_BF16,
     };
 
     // unary ops
diff --git a/tests/test-rope.cpp b/tests/test-rope.cpp
index f0895ffaa..8159e276a 100644
--- a/tests/test-rope.cpp
+++ b/tests/test-rope.cpp
@@ -218,4 +218,3 @@ int main(int /*argc*/, const char ** /*argv*/) {
 
     return 0;
 }
-