gguf-py, convert-hf : model conversion support for T5 and FLAN-T5 model variants (#5763)

* gguf-py : add T5 model architecture

* gguf-py : add separate tensors for encoder and decoder

* gguf-py : add new model header parameters: decoder_start_token_id, attention.relative_buckets_count, tokenizer.ggml.remove_extra_whitespaces, tokenizer.ggml.precompiled_charsmap

* convert-hf : add model conversion support for T5ForConditionalGeneration and T5WithLMHeadModel

---------

Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>

convert-hf-to-gguf.py

@@ -80,7 +80,7 @@ class Model:
         if not self.is_safetensors:
             self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin")
         self.hparams = Model.load_hparams(self.dir_model)
-        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
+        self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer", "num_layers"])
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
         self.tensor_names = None
         if self.ftype == gguf.LlamaFileType.GUESSED:
@@ -2771,6 +2771,124 @@ class DeepseekV2Model(Model):
                 raise ValueError(f"Unprocessed experts: {experts}")
 
 
+@Model.register("T5ForConditionalGeneration")
+@Model.register("T5WithLMHeadModel")
+class T5Model(Model):
+    model_arch = gguf.MODEL_ARCH.T5
+
+    def set_vocab(self):
+        # to avoid TypeError: Descriptors cannot be created directly
+        # exception when importing sentencepiece_model_pb2
+        os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+        from sentencepiece import SentencePieceProcessor
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        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())
+        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))
+
+        vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size())
+
+        tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)]
+        scores: list[float] = [-10000.0] * vocab_size
+        toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size
+
+        for token_id in range(tokenizer.vocab_size()):
+            piece = tokenizer.IdToPiece(token_id)
+            text = piece.encode("utf-8")
+            score = tokenizer.GetScore(token_id)
+
+            toktype = SentencePieceTokenTypes.NORMAL
+            if tokenizer.IsUnknown(token_id):
+                toktype = SentencePieceTokenTypes.UNKNOWN
+            elif tokenizer.IsControl(token_id):
+                toktype = SentencePieceTokenTypes.CONTROL
+            elif tokenizer.IsUnused(token_id):
+                toktype = SentencePieceTokenTypes.UNUSED
+            elif tokenizer.IsByte(token_id):
+                toktype = SentencePieceTokenTypes.BYTE
+
+            tokens[token_id] = text
+            scores[token_id] = score
+            toktypes[token_id] = toktype
+
+        added_tokens_file = self.dir_model / 'added_tokens.json'
+        if added_tokens_file.is_file():
+            with open(added_tokens_file, "r", encoding="utf-8") as f:
+                added_tokens_json = json.load(f)
+                for key in added_tokens_json:
+                    token_id = added_tokens_json[key]
+                    if (token_id >= vocab_size):
+                        logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}')
+                        continue
+
+                    tokens[token_id] = key.encode("utf-8")
+                    scores[token_id] = -1000.0
+                    toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED
+
+        if vocab_size > len(tokens):
+            pad_count = vocab_size - len(tokens)
+            logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]")
+            for i in range(1, pad_count + 1):
+                tokens.append(bytes(f"[PAD{i}]", encoding="utf-8"))
+                scores.append(-1000.0)
+                toktypes.append(SentencePieceTokenTypes.UNUSED)
+
+        self.gguf_writer.add_tokenizer_model("t5")
+        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)
+        self.gguf_writer.add_add_space_prefix(add_prefix)
+        self.gguf_writer.add_remove_extra_whitespaces(remove_whitespaces)
+        if precompiled_charsmap:
+            self.gguf_writer.add_precompiled_charsmap(precompiled_charsmap)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        self.gguf_writer.add_add_bos_token(False)
+        self.gguf_writer.add_add_eos_token(True)
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_name("T5")
+        self.gguf_writer.add_context_length(self.hparams["n_positions"])
+        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"])
+        self.gguf_writer.add_head_count(self.hparams["num_heads"])
+        self.gguf_writer.add_key_length(self.hparams["d_kv"])
+        self.gguf_writer.add_value_length(self.hparams["d_kv"])
+        self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_relative_attn_buckets_count(self.hparams["relative_attention_num_buckets"])
+        self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
+        self.gguf_writer.add_decoder_start_token_id(self.hparams["decoder_start_token_id"])
+        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
+
+        # 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 []
+
+        return [(self.map_tensor_name(name), data_torch)]
+
+
 ###### CONVERSION LOGIC ######
 
 

gguf-py/gguf/constants.py

@@ -49,6 +49,7 @@ class Keys:
         EXPERT_WEIGHTS_SCALE              = "{arch}.expert_weights_scale"
         POOLING_TYPE                      = "{arch}.pooling_type"
         LOGIT_SCALE                       = "{arch}.logit_scale"
+        DECODER_START_TOKEN_ID            = "{arch}.decoder_start_token_id"
 
     class Attention:
         HEAD_COUNT        = "{arch}.attention.head_count"
@@ -62,6 +63,7 @@ class Keys:
         CAUSAL            = "{arch}.attention.causal"
         Q_LORA_RANK       = "{arch}.attention.q_lora_rank"
         KV_LORA_RANK      = "{arch}.attention.kv_lora_rank"
+        REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
 
     class Rope:
         DIMENSION_COUNT         = "{arch}.rope.dimension_count"
@@ -97,6 +99,8 @@ class Keys:
         ADD_BOS              = "tokenizer.ggml.add_bos_token"
         ADD_EOS              = "tokenizer.ggml.add_eos_token"
         ADD_PREFIX           = "tokenizer.ggml.add_space_prefix"
+        REMOVE_EXTRA_WS      = "tokenizer.ggml.remove_extra_whitespaces"
+        PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap"
         HF_JSON              = "tokenizer.huggingface.json"
         RWKV                 = "tokenizer.rwkv.world"
         CHAT_TEMPLATE        = "tokenizer.chat_template"
@@ -150,6 +154,7 @@ class MODEL_ARCH(IntEnum):
     ARCTIC       = auto()
     DEEPSEEK2    = auto()
     BITNET       = auto()
+    T5           = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -203,6 +208,34 @@ class MODEL_TENSOR(IntEnum):
     ATTN_KV_A_NORM       = auto()
     FFN_SUB_NORM         = auto()
     ATTN_SUB_NORM        = auto()
+    DEC_ATTN_NORM        = auto()
+    DEC_ATTN_Q           = auto()
+    DEC_ATTN_K           = auto()
+    DEC_ATTN_V           = auto()
+    DEC_ATTN_OUT         = auto()
+    DEC_ATTN_REL_B       = auto()
+    DEC_CROSS_ATTN_NORM  = auto()
+    DEC_CROSS_ATTN_Q     = auto()
+    DEC_CROSS_ATTN_K     = auto()
+    DEC_CROSS_ATTN_V     = auto()
+    DEC_CROSS_ATTN_OUT   = auto()
+    DEC_CROSS_ATTN_REL_B = auto()
+    DEC_FFN_NORM         = auto()
+    DEC_FFN_GATE         = auto()
+    DEC_FFN_DOWN         = auto()
+    DEC_FFN_UP           = auto()
+    DEC_OUTPUT_NORM      = auto()
+    ENC_ATTN_NORM        = auto()
+    ENC_ATTN_Q           = auto()
+    ENC_ATTN_K           = auto()
+    ENC_ATTN_V           = auto()
+    ENC_ATTN_OUT         = auto()
+    ENC_ATTN_REL_B       = auto()
+    ENC_FFN_NORM         = auto()
+    ENC_FFN_GATE         = auto()
+    ENC_FFN_DOWN         = auto()
+    ENC_FFN_UP           = auto()
+    ENC_OUTPUT_NORM      = auto()
 
 
 MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
@@ -241,6 +274,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
     MODEL_ARCH.ARCTIC:         "arctic",
     MODEL_ARCH.DEEPSEEK2:      "deepseek2",
     MODEL_ARCH.BITNET:         "bitnet",
+    MODEL_ARCH.T5:             "t5",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -294,6 +328,34 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
     MODEL_TENSOR.ATTN_KV_A_NORM:       "blk.{bid}.attn_kv_a_norm",
     MODEL_TENSOR.ATTN_SUB_NORM:        "blk.{bid}.attn_sub_norm",
     MODEL_TENSOR.FFN_SUB_NORM:         "blk.{bid}.ffn_sub_norm",
+    MODEL_TENSOR.DEC_ATTN_NORM:        "dec.blk.{bid}.attn_norm",
+    MODEL_TENSOR.DEC_ATTN_Q:           "dec.blk.{bid}.attn_q",
+    MODEL_TENSOR.DEC_ATTN_K:           "dec.blk.{bid}.attn_k",
+    MODEL_TENSOR.DEC_ATTN_V:           "dec.blk.{bid}.attn_v",
+    MODEL_TENSOR.DEC_ATTN_OUT:         "dec.blk.{bid}.attn_o",
+    MODEL_TENSOR.DEC_ATTN_REL_B:       "dec.blk.{bid}.attn_rel_b",
+    MODEL_TENSOR.DEC_CROSS_ATTN_NORM:  "dec.blk.{bid}.cross_attn_norm",
+    MODEL_TENSOR.DEC_CROSS_ATTN_Q:     "dec.blk.{bid}.cross_attn_q",
+    MODEL_TENSOR.DEC_CROSS_ATTN_K:     "dec.blk.{bid}.cross_attn_k",
+    MODEL_TENSOR.DEC_CROSS_ATTN_V:     "dec.blk.{bid}.cross_attn_v",
+    MODEL_TENSOR.DEC_CROSS_ATTN_OUT:   "dec.blk.{bid}.cross_attn_o",
+    MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: "dec.blk.{bid}.cross_attn_rel_b",
+    MODEL_TENSOR.DEC_FFN_NORM:         "dec.blk.{bid}.ffn_norm",
+    MODEL_TENSOR.DEC_FFN_GATE:         "dec.blk.{bid}.ffn_gate",
+    MODEL_TENSOR.DEC_FFN_DOWN:         "dec.blk.{bid}.ffn_down",
+    MODEL_TENSOR.DEC_FFN_UP:           "dec.blk.{bid}.ffn_up",
+    MODEL_TENSOR.DEC_OUTPUT_NORM:      "dec.output_norm",
+    MODEL_TENSOR.ENC_ATTN_NORM:        "enc.blk.{bid}.attn_norm",
+    MODEL_TENSOR.ENC_ATTN_Q:           "enc.blk.{bid}.attn_q",
+    MODEL_TENSOR.ENC_ATTN_K:           "enc.blk.{bid}.attn_k",
+    MODEL_TENSOR.ENC_ATTN_V:           "enc.blk.{bid}.attn_v",
+    MODEL_TENSOR.ENC_ATTN_OUT:         "enc.blk.{bid}.attn_o",
+    MODEL_TENSOR.ENC_ATTN_REL_B:       "enc.blk.{bid}.attn_rel_b",
+    MODEL_TENSOR.ENC_FFN_NORM:         "enc.blk.{bid}.ffn_norm",
+    MODEL_TENSOR.ENC_FFN_GATE:         "enc.blk.{bid}.ffn_gate",
+    MODEL_TENSOR.ENC_FFN_DOWN:         "enc.blk.{bid}.ffn_down",
+    MODEL_TENSOR.ENC_FFN_UP:           "enc.blk.{bid}.ffn_up",
+    MODEL_TENSOR.ENC_OUTPUT_NORM:      "enc.output_norm",
 }
 
 MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
@@ -829,6 +891,38 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
         MODEL_TENSOR.ATTN_SUB_NORM,
         MODEL_TENSOR.FFN_SUB_NORM,
     ],
+    MODEL_ARCH.T5: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.DEC_ATTN_NORM,
+        MODEL_TENSOR.DEC_ATTN_Q,
+        MODEL_TENSOR.DEC_ATTN_K,
+        MODEL_TENSOR.DEC_ATTN_V,
+        MODEL_TENSOR.DEC_ATTN_OUT,
+        MODEL_TENSOR.DEC_ATTN_REL_B,
+        MODEL_TENSOR.DEC_CROSS_ATTN_NORM,
+        MODEL_TENSOR.DEC_CROSS_ATTN_Q,
+        MODEL_TENSOR.DEC_CROSS_ATTN_K,
+        MODEL_TENSOR.DEC_CROSS_ATTN_V,
+        MODEL_TENSOR.DEC_CROSS_ATTN_OUT,
+        MODEL_TENSOR.DEC_CROSS_ATTN_REL_B,
+        MODEL_TENSOR.DEC_FFN_NORM,
+        MODEL_TENSOR.DEC_FFN_GATE,
+        MODEL_TENSOR.DEC_FFN_DOWN,
+        MODEL_TENSOR.DEC_FFN_UP,
+        MODEL_TENSOR.DEC_OUTPUT_NORM,
+        MODEL_TENSOR.ENC_ATTN_NORM,
+        MODEL_TENSOR.ENC_ATTN_Q,
+        MODEL_TENSOR.ENC_ATTN_K,
+        MODEL_TENSOR.ENC_ATTN_V,
+        MODEL_TENSOR.ENC_ATTN_OUT,
+        MODEL_TENSOR.ENC_ATTN_REL_B,
+        MODEL_TENSOR.ENC_FFN_NORM,
+        MODEL_TENSOR.ENC_FFN_GATE,
+        MODEL_TENSOR.ENC_FFN_DOWN,
+        MODEL_TENSOR.ENC_FFN_UP,
+        MODEL_TENSOR.ENC_OUTPUT_NORM,
+    ],
     # TODO
 }
 

gguf-py/gguf/gguf_writer.py

@@ -400,6 +400,9 @@ class GGUFWriter:
     def add_parallel_residual(self, use: bool) -> None:
         self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
 
+    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)
 
@@ -448,6 +451,9 @@ class GGUFWriter:
     def add_kv_lora_rank(self, length: int) -> None:
         self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length)
 
+    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_pooling_type(self, value: PoolingType) -> None:
         self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)
 
@@ -538,6 +544,12 @@ class GGUFWriter:
     def add_add_space_prefix(self, value: bool) -> None:
         self.add_bool(Keys.Tokenizer.ADD_PREFIX, value)
 
+    def add_remove_extra_whitespaces(self, value: bool) -> None:
+        self.add_bool(Keys.Tokenizer.REMOVE_EXTRA_WS, value)
+
+    def add_precompiled_charsmap(self, charsmap: Sequence[bytes]) -> None:
+        self.add_array(Keys.Tokenizer.PRECOMPILED_CHARSMAP, charsmap)
+
     def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None:
         if not isinstance(value, str):
             template_default = None
@@ -599,6 +611,9 @@ class GGUFWriter:
             kv_data += self._pack("Q", len(encoded_val))
             kv_data += encoded_val
         elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val:
+            if isinstance(val, bytes):
+                ltype = GGUFValueType.UINT8
+            else:
                 ltype = GGUFValueType.get_type(val[0])
                 if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
                     raise ValueError("All items in a GGUF array should be of the same type")

gguf-py/gguf/tensor_mapping.py

@@ -24,6 +24,7 @@ class TensorNameMap:
             "backbone.embedding",                        # mamba
             "backbone.embeddings",                       # mamba-hf
             "transformer.in_out_embed",                  # Grok
+            "shared",                                    # t5
         ),
 
         # Token type embeddings
@@ -421,6 +422,120 @@ class TensorNameMap:
         MODEL_TENSOR.FFN_SUB_NORM: (
             "model.layers.{bid}.mlp.ffn_layernorm",  # bitnet
         ),
+
+        MODEL_TENSOR.DEC_ATTN_NORM: (
+            "decoder.block.{bid}.layer.0.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_Q: (
+            "decoder.block.{bid}.layer.0.SelfAttention.q", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_K: (
+            "decoder.block.{bid}.layer.0.SelfAttention.k", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_V: (
+            "decoder.block.{bid}.layer.0.SelfAttention.v", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_OUT: (
+            "decoder.block.{bid}.layer.0.SelfAttention.o", # t5
+        ),
+
+        MODEL_TENSOR.DEC_ATTN_REL_B: (
+            "decoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_NORM: (
+            "decoder.block.{bid}.layer.1.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_Q: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.q", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_K: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.k", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_V: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.v", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_OUT: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.o", # t5
+        ),
+
+        MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: (
+            "decoder.block.{bid}.layer.1.EncDecAttention.relative_attention_bias", # t5
+        ),
+
+        MODEL_TENSOR.DEC_FFN_NORM: (
+            "decoder.block.{bid}.layer.2.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.DEC_FFN_GATE: (
+            "decoder.block.{bid}.layer.2.DenseReluDense.wi_0", # flan-t5
+        ),
+
+        MODEL_TENSOR.DEC_FFN_UP: (
+            "decoder.block.{bid}.layer.2.DenseReluDense.wi",   # t5
+            "decoder.block.{bid}.layer.2.DenseReluDense.wi_1", # flan-t5
+        ),
+
+        MODEL_TENSOR.DEC_FFN_DOWN: (
+            "decoder.block.{bid}.layer.2.DenseReluDense.wo", # t5
+        ),
+
+        MODEL_TENSOR.DEC_OUTPUT_NORM: (
+            "decoder.final_layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_NORM: (
+            "encoder.block.{bid}.layer.0.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_Q: (
+            "encoder.block.{bid}.layer.0.SelfAttention.q", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_K: (
+            "encoder.block.{bid}.layer.0.SelfAttention.k", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_V: (
+            "encoder.block.{bid}.layer.0.SelfAttention.v", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_OUT: (
+            "encoder.block.{bid}.layer.0.SelfAttention.o", # t5
+        ),
+
+        MODEL_TENSOR.ENC_ATTN_REL_B: (
+            "encoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias", # t5
+        ),
+
+        MODEL_TENSOR.ENC_FFN_NORM: (
+            "encoder.block.{bid}.layer.1.layer_norm", # t5
+        ),
+
+        MODEL_TENSOR.ENC_FFN_GATE: (
+            "encoder.block.{bid}.layer.1.DenseReluDense.wi_0", # flan-t5
+        ),
+
+        MODEL_TENSOR.ENC_FFN_UP: (
+            "encoder.block.{bid}.layer.1.DenseReluDense.wi",   # t5
+            "encoder.block.{bid}.layer.1.DenseReluDense.wi_1", # flan-t5
+        ),
+
+        MODEL_TENSOR.ENC_FFN_DOWN: (
+            "encoder.block.{bid}.layer.1.DenseReluDense.wo", # t5
+        ),
+
+        MODEL_TENSOR.ENC_OUTPUT_NORM: (
+            "encoder.final_layer_norm", # t5
+        ),
     }
 
     # architecture-specific block mappings