mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-31 08:51:55 +00:00
Merge branch 'master' into compilade/refactor-kv-cache
Also begin reverting some implicit state rollback code.
This commit is contained in:
Francis Couture-Harpin
@@ -13,7 +13,7 @@ class TensorNameMap:
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"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais exaone
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"transformer.word_embeddings", # falcon
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"word_embeddings", # bloom
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"model.embed_tokens", # llama-hf nemotron
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"model.embed_tokens", # llama-hf nemotron olmoe
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"tok_embeddings", # llama-pth
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"embeddings.word_embeddings", # bert nomic-bert
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"language_model.embedding.word_embeddings", # persimmon
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@@ -54,7 +54,7 @@ class TensorNameMap:
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# Output
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MODEL_TENSOR.OUTPUT: (
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"embed_out", # gptneox
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone
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"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe
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"output", # llama-pth bloom internlm2
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"word_embeddings_for_head", # persimmon
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"lm_head.linear", # phi2
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@@ -66,7 +66,7 @@ class TensorNameMap:
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MODEL_TENSOR.OUTPUT_NORM: (
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"gpt_neox.final_layer_norm", # gptneox
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"transformer.ln_f", # gpt2 gpt-j falcon jais exaone
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"model.norm", # llama-hf baichuan internlm2
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"model.norm", # llama-hf baichuan internlm2 olmoe
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"norm", # llama-pth
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"transformer.norm_f", # mpt dbrx
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"ln_f", # refact bloom qwen gpt2
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@@ -87,6 +87,9 @@ class TensorNameMap:
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"rope.freqs", # llama-pth
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"rotary_pos_emb.inv_freq", # chatglm
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),
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MODEL_TENSOR.ROPE_FACTORS_LONG: (),
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MODEL_TENSOR.ROPE_FACTORS_SHORT: (),
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}
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block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
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@@ -98,7 +101,7 @@ class TensorNameMap:
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"transformer.h.{bid}.input_layernorm", # falcon7b
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"h.{bid}.input_layernorm", # bloom
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"transformer.h.{bid}.ln_mlp", # falcon40b
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"model.layers.{bid}.input_layernorm", # llama-hf nemotron
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"model.layers.{bid}.input_layernorm", # llama-hf nemotron olmoe
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"layers.{bid}.attention_norm", # llama-pth
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"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
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"model.layers.{bid}.ln1", # yi
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@@ -142,7 +145,7 @@ class TensorNameMap:
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# Attention query
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MODEL_TENSOR.ATTN_Q: (
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"model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron
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"model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron olmoe
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"layers.{bid}.attention.wq", # llama-pth
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"encoder.layer.{bid}.attention.self.query", # bert
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"transformer.h.{bid}.attn.q_proj", # gpt-j
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@@ -154,7 +157,7 @@ class TensorNameMap:
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# Attention key
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MODEL_TENSOR.ATTN_K: (
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"model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron
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"model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron olmoe
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"layers.{bid}.attention.wk", # llama-pth
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"encoder.layer.{bid}.attention.self.key", # bert
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"transformer.h.{bid}.attn.k_proj", # gpt-j
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@@ -167,7 +170,7 @@ class TensorNameMap:
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# Attention value
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MODEL_TENSOR.ATTN_V: (
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"model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron
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"model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron olmoe
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"layers.{bid}.attention.wv", # llama-pth
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"encoder.layer.{bid}.attention.self.value", # bert
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"transformer.h.{bid}.attn.v_proj", # gpt-j
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@@ -185,7 +188,7 @@ class TensorNameMap:
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"transformer.blocks.{bid}.attn.out_proj", # mpt
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"transformer.h.{bid}.self_attention.dense", # falcon
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"h.{bid}.self_attention.dense", # bloom
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"model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron
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"model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe
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"layers.{bid}.attention.wo", # llama-pth
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"encoder.layer.{bid}.attention.output.dense", # bert
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"transformer.h.{bid}.attn.out_proj", # gpt-j
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@@ -229,7 +232,7 @@ class TensorNameMap:
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"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais exaone
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"h.{bid}.post_attention_layernorm", # bloom
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"transformer.blocks.{bid}.norm_2", # mpt
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"model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron
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"model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron olmoe
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"layers.{bid}.ffn_norm", # llama-pth
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"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
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"model.layers.{bid}.ln2", # yi
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@@ -253,12 +256,13 @@ class TensorNameMap:
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),
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MODEL_TENSOR.FFN_GATE_INP: (
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"layers.{bid}.feed_forward.gate", # mixtral
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"model.layers.{bid}.block_sparse_moe.gate", # mixtral
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"model.layers.{bid}.mlp.gate", # qwen2moe
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"transformer.decoder_layer.{bid}.router", # Grok
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"transformer.blocks.{bid}.ffn.router.layer", # dbrx
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"model.layers.{bid}.feed_forward.router", # jamba
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"layers.{bid}.feed_forward.gate", # mixtral
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"model.layers.{bid}.block_sparse_moe.gate", # mixtral
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"model.layers.{bid}.mlp.gate", # qwen2moe olmoe
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"transformer.decoder_layer.{bid}.router", # Grok
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"transformer.blocks.{bid}.ffn.router.layer", # dbrx
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"model.layers.{bid}.feed_forward.router", # jamba
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"model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe
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),
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MODEL_TENSOR.FFN_GATE_INP_SHEXP: (
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@@ -299,7 +303,7 @@ class TensorNameMap:
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"layers.{bid}.feed_forward.experts.w3", # mixtral (merged)
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"transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged)
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"transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx
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"model.layers.{bid}.mlp.experts.up_proj", # qwen2moe (merged)
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"model.layers.{bid}.mlp.experts.up_proj", # qwen2moe olmoe (merged)
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),
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MODEL_TENSOR.FFN_UP_SHEXP: (
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@@ -332,7 +336,7 @@ class TensorNameMap:
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"layers.{bid}.feed_forward.experts.w1", # mixtral (merged)
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"transformer.decoder_layer.{bid}.moe.linear", # Grok (merged)
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"transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx
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"model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe (merged)
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"model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe olmoe (merged)
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),
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MODEL_TENSOR.FFN_GATE_SHEXP: (
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@@ -370,10 +374,11 @@ class TensorNameMap:
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),
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MODEL_TENSOR.FFN_DOWN_EXP: (
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"layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
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"transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
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"transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
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"model.layers.{bid}.mlp.experts.down_proj", # qwen2moe (merged)
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"layers.{bid}.feed_forward.experts.w2", # mixtral (merged)
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"transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged)
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"transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx
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"model.layers.{bid}.mlp.experts.down_proj", # qwen2moe olmoe (merged)
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"model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe
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),
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MODEL_TENSOR.FFN_DOWN_SHEXP: (
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@@ -384,7 +389,7 @@ class TensorNameMap:
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MODEL_TENSOR.ATTN_Q_NORM: (
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"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
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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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"model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon
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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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"transformer.layers.{bid}.attn.q_norm", # openelm
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@@ -393,7 +398,7 @@ class TensorNameMap:
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MODEL_TENSOR.ATTN_K_NORM: (
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"language_model.encoder.layers.{bid}.self_attention.k_layernorm",
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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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"model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon
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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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"transformer.layers.{bid}.attn.k_norm", # openelm
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@@ -704,6 +709,15 @@ class TensorNameMap:
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MODEL_TENSOR.ENC_OUTPUT_NORM: (
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"encoder.final_layer_norm", # t5
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),
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MODEL_TENSOR.CLS: (
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"classifier", # jina
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"classifier.dense", # roberta
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),
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MODEL_TENSOR.CLS_OUT: (
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"classifier.out_proj", # roberta
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),
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}
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# architecture-specific block mappings
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