mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-24 10:24:35 +00:00
llama : add Jina Embeddings architecture (#6826)
* feat: first things to do * feat: create tensors for Jina architecture * fix: use other tensors * feat: embedding gets results * fix: fix usage of ALIBI * fix: clean prints * fix: do some cleanup unused vars * fix: revert changes to Makefile and CMakeLists * fix: revert some changes * fix: fix small detail * fix: fix convert formatting * fix: fix linting and editor * feat: set proper vocab settings * fix: JinaBertForMaskedLM registration * feat: support q_normalization and k_normalization in Jina arch * feat: handle gpt2 tokenizer with Jina architecture * feat: example comments in embedding * feat: rename Jina Bert to Jina Bert V2 * fix: add some changes as per review * feat: proper KQ_pos for Jina embeddings * feat: add capacity to load models ES and DE for Spanish * llama : fix pre-tokenizers * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * minor : clean-up * embedding : add warning about missing SEP --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Joan Fontanals
GitHub
parent
9cb317f77e
commit
b83cc3f5b3
@ -74,6 +74,9 @@ models = [
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{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
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{"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
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{"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
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{"name": "jina-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
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{"name": "jina-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
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{"name": "jina-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
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]
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# make directory "models/tokenizers" if it doesn't exist
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@ -404,8 +404,17 @@ class Model:
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# ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf
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res = "olmo"
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if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e":
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# ref: https://huggingface.co/databricks/dbrx-instruct
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# ref: https://huggingface.co/databricks/dbrx-base
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res = "dbrx"
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if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f":
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# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
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res = "jina-en"
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if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643":
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# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es
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res = "jina-es"
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if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6":
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# ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de
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res = "jina-de"
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if res is None:
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logger.warning("\n")
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@ -2289,6 +2298,43 @@ class OlmoModel(Model):
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return [(self.map_tensor_name(name), data_torch)]
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@Model.register("JinaBertModel", "JinaBertForMaskedLM")
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class JinaBertV2Model(BertModel):
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model_arch = gguf.MODEL_ARCH.JINA_BERT_V2
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.intermediate_size = self.hparams["intermediate_size"]
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def get_tensors(self):
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for name, data in super().get_tensors():
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if 'gated_layers' in name:
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d1 = data[:self.intermediate_size, :]
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name1 = name.replace('gated_layers', 'gated_layers_w')
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d2 = data[self.intermediate_size:, :]
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name2 = name.replace('gated_layers', 'gated_layers_v')
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yield name1, d1
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yield name2, d2
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continue
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yield name, data
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def set_vocab(self, *args, **kwargs):
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tokenizer_class = 'BertTokenizer'
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with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f:
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tokenizer_class = json.load(f)['tokenizer_class']
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if tokenizer_class == 'BertTokenizer':
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super().set_vocab()
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elif tokenizer_class == 'RobertaTokenizer':
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self._set_vocab_gpt2()
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self.gguf_writer.add_token_type_count(2)
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else:
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raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel')
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self.gguf_writer.add_add_bos_token(True)
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self.gguf_writer.add_add_eos_token(True)
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###### CONVERSION LOGIC ######
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@ -49,6 +49,12 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
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}
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float * out = output + batch.seq_id[i][0] * n_embd;
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//TODO: I would also add a parameter here to enable normalization or not.
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/*fprintf(stdout, "unnormalized_embedding:");
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for (int hh = 0; hh < n_embd; hh++) {
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fprintf(stdout, "%9.6f ", embd[hh]);
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}
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fprintf(stdout, "\n");*/
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llama_embd_normalize(embd, out, n_embd);
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}
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}
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@ -123,10 +129,12 @@ int main(int argc, char ** argv) {
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inputs.push_back(inp);
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}
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// add SEP if not present
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// check if the last token is SEP
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// it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true'
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for (auto & inp : inputs) {
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if (inp.empty() || inp.back() != llama_token_sep(model)) {
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inp.push_back(llama_token_sep(model));
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fprintf(stderr, "%s: warning: last token in the prompt is not SEP\n", __func__);
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fprintf(stderr, "%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__);
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}
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}
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@ -118,6 +118,7 @@ class MODEL_ARCH(IntEnum):
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REFACT = auto()
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BERT = auto()
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NOMIC_BERT = auto()
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JINA_BERT_V2 = auto()
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BLOOM = auto()
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STABLELM = auto()
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QWEN = auto()
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@ -195,6 +196,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.REFACT: "refact",
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MODEL_ARCH.BERT: "bert",
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MODEL_ARCH.NOMIC_BERT: "nomic-bert",
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MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2",
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MODEL_ARCH.BLOOM: "bloom",
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MODEL_ARCH.STABLELM: "stablelm",
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MODEL_ARCH.QWEN: "qwen",
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@ -380,6 +382,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.LAYER_OUT_NORM,
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],
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MODEL_ARCH.JINA_BERT_V2: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.TOKEN_EMBD_NORM,
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MODEL_TENSOR.TOKEN_TYPES,
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MODEL_TENSOR.ATTN_OUT_NORM,
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MODEL_TENSOR.ATTN_Q,
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MODEL_TENSOR.ATTN_Q_NORM,
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MODEL_TENSOR.ATTN_K,
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MODEL_TENSOR.ATTN_K_NORM,
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MODEL_TENSOR.ATTN_V,
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MODEL_TENSOR.ATTN_OUT,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_GATE,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.LAYER_OUT_NORM,
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],
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MODEL_ARCH.MPT: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -243,6 +243,7 @@ class TensorNameMap:
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"model.layers.{bid}.feed_forward.w3", # internlm2
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"encoder.layers.{bid}.mlp.fc11", # nomic-bert
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"model.layers.{bid}.mlp.c_fc", # starcoder2
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"encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2
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),
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MODEL_TENSOR.FFN_UP_EXP: (
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@ -269,6 +270,7 @@ class TensorNameMap:
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"model.layers.layers.{bid}.mlp.gate_proj", # plamo
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"model.layers.{bid}.feed_forward.w1", # internlm2
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"encoder.layers.{bid}.mlp.fc12", # nomic-bert
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"encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2
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"transformer.h.{bid}.mlp.linear_1", # refact
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),
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@ -303,6 +305,7 @@ class TensorNameMap:
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"model.layers.{bid}.feed_forward.w2", # internlm2
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"encoder.layers.{bid}.mlp.fc2", # nomic-bert
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"model.layers.{bid}.mlp.c_proj", # starcoder2
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"encoder.layer.{bid}.mlp.wo", # jina-bert-v2
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),
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MODEL_TENSOR.FFN_DOWN_EXP: (
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@ -321,6 +324,7 @@ class TensorNameMap:
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"model.layers.{bid}.self_attn.q_layernorm", # persimmon
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"model.layers.{bid}.self_attn.q_norm", # cohere
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"transformer.blocks.{bid}.attn.q_ln", # sea-lion
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"encoder.layer.{bid}.attention.self.layer_norm_q" # jina-bert-v2
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),
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MODEL_TENSOR.ATTN_K_NORM: (
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@ -328,6 +332,7 @@ class TensorNameMap:
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"model.layers.{bid}.self_attn.k_layernorm", # persimmon
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"model.layers.{bid}.self_attn.k_norm", # cohere
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"transformer.blocks.{bid}.attn.k_ln", # sea-lion
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"encoder.layer.{bid}.attention.self.layer_norm_k" # jina-bert-v2
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),
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MODEL_TENSOR.ROPE_FREQS: (
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@ -338,6 +343,7 @@ class TensorNameMap:
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"encoder.layer.{bid}.output.LayerNorm", # bert
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"encoder.layers.{bid}.norm2", # nomic-bert
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"transformer.decoder_layer.{bid}.rms_norm_3", # Grok
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"encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2
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),
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MODEL_TENSOR.SSM_IN: (
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124
llama.cpp
124
llama.cpp
@ -205,6 +205,7 @@ enum llm_arch {
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LLM_ARCH_REFACT,
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LLM_ARCH_BERT,
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LLM_ARCH_NOMIC_BERT,
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LLM_ARCH_JINA_BERT_V2,
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LLM_ARCH_BLOOM,
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LLM_ARCH_STABLELM,
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LLM_ARCH_QWEN,
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@ -241,6 +242,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
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{ LLM_ARCH_REFACT, "refact" },
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{ LLM_ARCH_BERT, "bert" },
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{ LLM_ARCH_NOMIC_BERT, "nomic-bert" },
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{ LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" },
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{ LLM_ARCH_BLOOM, "bloom" },
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{ LLM_ARCH_STABLELM, "stablelm" },
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{ LLM_ARCH_QWEN, "qwen" },
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@ -691,6 +693,25 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_JINA_BERT_V2,
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{
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{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
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{ LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" },
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{ LLM_TENSOR_TOKEN_TYPES, "token_types" },
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{ LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" },
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{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" },
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{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" },
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{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
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{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
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{ LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" },
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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},
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},
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{
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LLM_ARCH_BLOOM,
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{
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@ -3778,6 +3799,12 @@ static void llm_load_hparams(
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// get hparams kv
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ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab);
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// everything past this point is not vocab-related
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if (hparams.vocab_only) {
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return;
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}
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ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train);
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ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd);
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ml.get_key(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff);
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@ -3961,6 +3988,19 @@ static void llm_load_hparams(
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model.type = e_model::MODEL_335M; break; // bge-large
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}
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} break;
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case LLM_ARCH_JINA_BERT_V2:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn);
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ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type);
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ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type);
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hparams.f_max_alibi_bias = 8.0f;
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switch (hparams.n_layer) {
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case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small
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case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base
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}
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} break;
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case LLM_ARCH_NOMIC_BERT:
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{
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ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps);
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@ -4382,7 +4422,9 @@ static void llm_load_vocab(
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tokenizer_pre == "starcoder") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER;
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} else if (
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tokenizer_pre == "gpt-2") {
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tokenizer_pre == "gpt-2" ||
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tokenizer_pre == "jina-es" ||
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tokenizer_pre == "jina-de") {
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vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2;
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} else if (
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tokenizer_pre == "refact") {
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@ -5241,6 +5283,50 @@ static bool llm_load_tensors(
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layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
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}
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} break;
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case LLM_ARCH_JINA_BERT_V2:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings
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model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); //token_type_embeddings
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model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm
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model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias
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for (int i = 0; i < n_layer; ++i) {
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ggml_context * ctx_layer = ctx_for_layer(i);
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ggml_context * ctx_split = ctx_for_layer_split(i);
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auto & layer = model.layers[i]; // JinaBertLayer
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layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd});
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layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd});
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layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
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layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false);
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layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa});
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layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa});
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layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
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layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false);
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layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa});
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layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa});
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layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens
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layer.bo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens
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layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
|
||||
layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd});
|
||||
|
||||
layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
|
||||
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_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd});
|
||||
|
||||
layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
|
||||
layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd});
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_BLOOM:
|
||||
{
|
||||
model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
|
||||
@ -6317,7 +6403,7 @@ static struct ggml_tensor * llm_build_ffn(
|
||||
llm_ffn_gate_type type_gate,
|
||||
const llm_build_cb & cb,
|
||||
int il) {
|
||||
struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur);
|
||||
struct ggml_tensor * tmp = up ? ggml_mul_mat(ctx, up, cur) : cur;
|
||||
cb(tmp, "ffn_up", il);
|
||||
|
||||
if (up_b) {
|
||||
@ -8118,8 +8204,11 @@ struct llm_build_context {
|
||||
|
||||
struct ggml_tensor * cur;
|
||||
struct ggml_tensor * inpL;
|
||||
struct ggml_tensor * inp_pos = nullptr;
|
||||
|
||||
struct ggml_tensor * inp_pos = build_inp_pos();
|
||||
if (model.arch != LLM_ARCH_JINA_BERT_V2) {
|
||||
inp_pos = build_inp_pos();
|
||||
}
|
||||
struct ggml_tensor * inp_mean = build_inp_mean();
|
||||
struct ggml_tensor * inp_cls = build_inp_cls();
|
||||
|
||||
@ -8150,13 +8239,26 @@ struct llm_build_context {
|
||||
struct ggml_tensor * Vcur;
|
||||
|
||||
// self-attention
|
||||
if (model.arch == LLM_ARCH_BERT) {
|
||||
if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) {
|
||||
Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
if (model.layers[il].attn_q_norm) {
|
||||
Qcur = llm_build_norm(ctx0, Qcur, hparams,
|
||||
model.layers[il].attn_q_norm,
|
||||
model.layers[il].attn_q_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
}
|
||||
|
||||
Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
if (model.layers[il].attn_k_norm) {
|
||||
Kcur = llm_build_norm(ctx0, Kcur, hparams,
|
||||
model.layers[il].attn_k_norm,
|
||||
model.layers[il].attn_k_norm_b,
|
||||
LLM_NORM, cb, il);
|
||||
}
|
||||
Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
@ -8247,6 +8349,13 @@ struct llm_build_context {
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
||||
NULL,
|
||||
LLM_FFN_GELU, LLM_FFN_SEQ, cb, il);
|
||||
} else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
|
||||
cur = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up, NULL,
|
||||
model.layers[il].ffn_gate, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b,
|
||||
NULL,
|
||||
LLM_FFN_GELU, LLM_FFN_PAR, cb, il);
|
||||
} else {
|
||||
cur = llm_build_ffn(ctx0, cur,
|
||||
model.layers[il].ffn_up, NULL,
|
||||
@ -10769,6 +10878,7 @@ static struct ggml_cgraph * llama_build_graph(
|
||||
result = llm.build_refact();
|
||||
} break;
|
||||
case LLM_ARCH_BERT:
|
||||
case LLM_ARCH_JINA_BERT_V2:
|
||||
case LLM_ARCH_NOMIC_BERT:
|
||||
{
|
||||
result = llm.build_bert();
|
||||
@ -12695,7 +12805,10 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab &
|
||||
}
|
||||
}
|
||||
|
||||
GGML_ASSERT(vocab.special_add_eos != 1);
|
||||
if (add_special && vocab.special_add_eos == 1) {
|
||||
GGML_ASSERT(vocab.special_add_eos != -1);
|
||||
output.push_back(vocab.special_eos_id);
|
||||
}
|
||||
} break;
|
||||
case LLAMA_VOCAB_TYPE_WPM:
|
||||
{
|
||||
@ -15746,6 +15859,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
|
||||
case LLM_ARCH_REFACT:
|
||||
case LLM_ARCH_BLOOM:
|
||||
case LLM_ARCH_MAMBA:
|
||||
case LLM_ARCH_JINA_BERT_V2:
|
||||
return LLAMA_ROPE_TYPE_NONE;
|
||||
|
||||
// use what we call a normal RoPE, operating on pairs of consecutive head values
|
||||
|
||||
Loading…
Reference in New Issue
Block a user