from __future__ import annotations from typing import Sequence from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES class TensorNameMap: mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { # Token embeddings MODEL_TENSOR.TOKEN_EMBD: ( "gpt_neox.embed_in", # gptneox "transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais exaone "transformer.word_embeddings", # falcon "word_embeddings", # bloom "model.embed_tokens", # llama-hf nemotron olmoe olmo2 rwkv6qwen2 glm4-0414 "tok_embeddings", # llama-pth "embeddings.word_embeddings", # bert nomic-bert "language_model.embedding.word_embeddings", # persimmon "wte", # gpt2 "transformer.embd.wte", # phi2 "model.tok_embeddings", # internlm2 "model.embedding", # mamba-qbert "backbone.embedding", # mamba "backbone.embeddings", # mamba-hf "transformer.in_out_embed", # Grok "embedding.word_embeddings", # chatglm "transformer.token_embeddings", # openelm "shared", # t5 "rwkv.embeddings", # rwkv6 "model.embeddings", # rwkv7 "model.word_embeddings", # bailingmoe "language_model.model.embed_tokens", # llama4 "encoder", # neobert ), # Token type embeddings MODEL_TENSOR.TOKEN_TYPES: ( "embeddings.token_type_embeddings", # bert nomic-bert ), # Normalization of token embeddings MODEL_TENSOR.TOKEN_EMBD_NORM: ( "word_embeddings_layernorm", # bloom "embeddings.LayerNorm", # bert "emb_ln", # nomic-bert "transformer.norm", # openelm "rwkv.blocks.0.pre_ln", # rwkv "rwkv.blocks.0.pre_ln", # rwkv6 "model.pre_ln", # rwkv7 "model.layers.0.pre_norm", # rwkv7 "backbone.norm", # wavtokenizer ), # Position embeddings MODEL_TENSOR.POS_EMBD: ( "transformer.wpe", # gpt2 "embeddings.position_embeddings", # bert "wpe", # gpt2 ), # Output MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais nemotron exaone olmoe olmo2 phimoe "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 "output_layer", # chatglm "head", # rwkv "head.out", # wavtokenizer "lm_head", # llama4 ), # Output norm MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox "transformer.ln_f", # gpt2 gpt-j falcon jais exaone "model.norm", # llama-hf baichuan internlm2 olmoe olmo2 phimoe "norm", # llama-pth "transformer.norm_f", # mpt dbrx "ln_f", # refact bloom qwen gpt2 "language_model.encoder.final_layernorm", # persimmon "model.final_layernorm", # persimmon "lm_head.ln", # phi2 "model.norm_f", # mamba-qbert "backbone.norm_f", # mamba "transformer.rms_norm", # Grok "encoder.final_layernorm", # chatglm "transformer.norm", # openelm "model.norm", # nemotron "rwkv.ln_out", # rwkv6 "model.ln_out", # rwkv7 "backbone.final_layer_norm", # wavtokenizer "model.norm", # llama4 ), # Rope frequencies MODEL_TENSOR.ROPE_FREQS: ( "rope.freqs", # llama-pth "rotary_pos_emb.inv_freq", # chatglm ), MODEL_TENSOR.ROPE_FACTORS_LONG: (), MODEL_TENSOR.ROPE_FACTORS_SHORT: (), MODEL_TENSOR.CONV1D: ( "backbone.embed", # roberta ), } block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { # Attention norm MODEL_TENSOR.ATTN_NORM: ( "gpt_neox.layers.{bid}.input_layernorm", # gptneox "transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais exaone "transformer.blocks.{bid}.norm_1", # mpt "transformer.h.{bid}.input_layernorm", # falcon7b "h.{bid}.input_layernorm", # bloom "transformer.h.{bid}.ln_mlp", # falcon40b "model.layers.{bid}.input_layernorm", # llama-hf nemotron olmoe phimoe "layers.{bid}.attention_norm", # llama-pth "language_model.encoder.layers.{bid}.input_layernorm", # persimmon "model.layers.{bid}.ln1", # yi "h.{bid}.ln_1", # gpt2 "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo "model.layers.{bid}.attention_norm", # internlm2 "model.layers.{bid}.norm", # mamba-qbert "backbone.layers.{bid}.norm", # mamba "transformer.decoder_layer.{bid}.rms_norm", # Grok "transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx "encoder.layers.{bid}.input_layernorm", # chatglm "transformer.layers.{bid}.attn_norm", # openelm "rwkv.blocks.{bid}.ln1", # rwkv6 "model.layers.{bid}.ln1", # rwkv7 "model.layers.{bid}.input_layernorm", # llama4 "transformer_encoder.{bid}.attention_norm", # neobert ), # Attention norm 2 MODEL_TENSOR.ATTN_NORM_2: ( "transformer.h.{bid}.ln_attn", # falcon40b "encoder.layer.{bid}.layer_norm_1", # jina-v2-code "rwkv.blocks.{bid}.ln2", # rwkv6 "model.layers.{bid}.ln2", # rwkv7 ), # 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 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 "h.{bid}.self_attention.query_key_value", # bloom "language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon "model.layers.{bid}.self_attn.query_key_value", # persimmon "h.{bid}.attn.c_attn", # gpt2 "transformer.h.{bid}.mixer.Wqkv", # phi2 "encoder.layers.{bid}.attn.Wqkv", # nomic-bert "encoder.layers.{bid}.mixer.Wqkv", # jina "model.layers.{bid}.self_attn.qkv_proj", # phi3 "encoder.layers.{bid}.self_attention.query_key_value", # chatglm "transformer.layers.{bid}.attn.qkv_proj", # openelm "transformer_encoder.{bid}.qkv", # neobert ), # Attention query MODEL_TENSOR.ATTN_Q: ( "model.layers.{bid}.self_attn.q_proj", # llama-hf nemotron olmoe olmo2 phimoe "model.layers.{bid}.self_attn.q_proj_no_perm", # llama-custom "layers.{bid}.attention.wq", # llama-pth "encoder.layer.{bid}.attention.self.query", # bert "transformer.layer.{bid}.attention.q_lin", # distillbert "transformer.h.{bid}.attn.q_proj", # gpt-j "model.layers.layers.{bid}.self_attn.q_proj", # plamo "model.layers.{bid}.attention.wq", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.query",# Grok "transformer.h.{bid}.attn.attention.q_proj", # exaone "model.layers.{bid}.self_attn.q_proj", # llama4 ), # Attention key MODEL_TENSOR.ATTN_K: ( "model.layers.{bid}.self_attn.k_proj", # llama-hf nemotron olmoe olmo2 phimoe "model.layers.{bid}.self_attn.k_proj_no_perm", # llama-custom "layers.{bid}.attention.wk", # llama-pth "encoder.layer.{bid}.attention.self.key", # bert "transformer.layer.{bid}.attention.k_lin", # distillbert "transformer.h.{bid}.attn.k_proj", # gpt-j "transformer.h.{bid}.attn.k", # refact "model.layers.layers.{bid}.self_attn.k_proj", # plamo "model.layers.{bid}.attention.wk", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.key",# Grok "transformer.h.{bid}.attn.attention.k_proj", # exaone "model.layers.{bid}.self_attn.k_proj", # llama4 ), # Attention value MODEL_TENSOR.ATTN_V: ( "model.layers.{bid}.self_attn.v_proj", # llama-hf nemotron olmoe olmo2 phimoe "layers.{bid}.attention.wv", # llama-pth "encoder.layer.{bid}.attention.self.value", # bert "transformer.layer.{bid}.attention.v_lin", # distillbert "transformer.h.{bid}.attn.v_proj", # gpt-j "transformer.h.{bid}.attn.v", # refact "model.layers.layers.{bid}.self_attn.v_proj", # plamo "model.layers.{bid}.attention.wv", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.value",# Grok "transformer.h.{bid}.attn.attention.v_proj", # exaone "model.layers.{bid}.self_attn.v_proj", # llama4 ), # Attention output MODEL_TENSOR.ATTN_OUT: ( "gpt_neox.layers.{bid}.attention.dense", # gptneox "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 "model.layers.{bid}.self_attn.o_proj", # llama-hf nemotron olmoe olmo2 phimoe "model.layers.{bid}.self_attn.linear_attn", # deci "layers.{bid}.attention.wo", # llama-pth "encoder.layer.{bid}.attention.output.dense", # bert "transformer.layer.{bid}.attention.out_lin", # distillbert "transformer.h.{bid}.attn.out_proj", # gpt-j "language_model.encoder.layers.{bid}.self_attention.dense", # persimmon "model.layers.{bid}.self_attn.dense", # persimmon "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo "model.layers.{bid}.attention.wo", # internlm2 "encoder.layers.{bid}.attn.out_proj", # nomic-bert "encoder.layers.{bid}.mixer.out_proj", # jina "transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx "encoder.layers.{bid}.self_attention.dense", # chatglm "transformer.layers.{bid}.attn.out_proj", # openelm "transformer.h.{bid}.attn.attention.out_proj", # exaone "model.layers.{bid}.self_attn.o_proj", # llama4 "transformer_encoder.{bid}.wo", # neobert ), # Attention output norm MODEL_TENSOR.ATTN_OUT_NORM: ( "encoder.layer.{bid}.attention.output.LayerNorm", # bert "transformer.layer.{bid}.sa_layer_norm", # distillbert "encoder.layers.{bid}.norm1", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_1", # Grok "transformer.blocks.{bid}.norm_attn_norm.norm_2", # dbrx ), MODEL_TENSOR.ATTN_POST_NORM: ( "model.layers.{bid}.post_attention_layernorm", # gemma2 olmo2 # ge "model.layers.{bid}.post_self_attn_layernorm", # glm-4-0414 ), # Rotary embeddings MODEL_TENSOR.ATTN_ROT_EMBD: ( "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth "model.layers.layers.{bid}.self_attn.rotary_emb.inv_freq", # plamo "transformer.h.{bid}.attn.rotary_emb.inv_freq", # codeshell ), # Feed-forward norm MODEL_TENSOR.FFN_NORM: ( "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox "transformer.h.{bid}.ln_2", # gpt2 refact qwen jais exaone "h.{bid}.post_attention_layernorm", # bloom "transformer.blocks.{bid}.norm_2", # mpt "model.layers.{bid}.post_attention_layernorm", # llama-hf nemotron olmoe phimoe "layers.{bid}.ffn_norm", # llama-pth "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "model.layers.{bid}.ln2", # yi "h.{bid}.ln_2", # gpt2 "model.layers.{bid}.ffn_norm", # internlm2 "transformer.decoder_layer.{bid}.rms_norm_2", # Grok "encoder.layers.{bid}.post_attention_layernorm", # chatglm "transformer.layers.{bid}.ffn_norm", # openelm "model.layers.{bid}.post_attention_layernorm", # llama4 "transformer_encoder.{bid}.ffn_norm", # neobert ), # Post feed-forward norm MODEL_TENSOR.FFN_PRE_NORM: ( "model.layers.{bid}.pre_feedforward_layernorm", # gemma2 ), # Post feed-forward norm MODEL_TENSOR.FFN_POST_NORM: ( "model.layers.{bid}.post_feedforward_layernorm", # gemma2 olmo2 "model.layers.{bid}.post_mlp_layernorm", # glm-4-0414 ), MODEL_TENSOR.FFN_GATE_INP: ( "layers.{bid}.feed_forward.gate", # mixtral "model.layers.{bid}.block_sparse_moe.gate", # mixtral phimoe "model.layers.{bid}.mlp.gate", # qwen2moe olmoe "transformer.decoder_layer.{bid}.router", # Grok "transformer.blocks.{bid}.ffn.router.layer", # dbrx "model.layers.{bid}.block_sparse_moe.router.layer", # granitemoe "model.layers.{bid}.feed_forward.router", # llama4 "encoder.layers.{bid}.mlp.router.layer", # nomic-bert-moe ), MODEL_TENSOR.FFN_GATE_INP_SHEXP: ( "model.layers.{bid}.mlp.shared_expert_gate", # qwen2moe ), MODEL_TENSOR.FFN_EXP_PROBS_B: ( "model.layers.{bid}.mlp.gate.e_score_correction", # deepseek-v3 dots1 ), # Feed-forward up MODEL_TENSOR.FFN_UP: ( "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox "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 "model.layers.{bid}.mlp.up_proj", # llama-hf refact nemotron olmo2 "layers.{bid}.feed_forward.w3", # llama-pth "encoder.layer.{bid}.intermediate.dense", # bert "transformer.layer.{bid}.ffn.lin1", # distillbert "transformer.h.{bid}.mlp.fc_in", # gpt-j "transformer.h.{bid}.mlp.linear_3", # refact "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon "model.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen "h.{bid}.mlp.c_fc", # gpt2 "transformer.h.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.gate_up_proj", # phi3 glm-4-0414 "model.layers.layers.{bid}.mlp.up_proj", # plamo "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert "encoder.layers.{bid}.mlp.fc1", # nomic-bert-moe "model.layers.{bid}.mlp.c_fc", # starcoder2 "encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2 (split up/gate, no longer used) "encoder.layer.{bid}.mlp.gated_layers", # jina-bert-v2 (GEGLU) "encoder.layer.{bid}.mlp.up_gated_layer", # jina-v2-code (GEGLU) "model.layers.{bid}.residual_mlp.w3", # arctic "encoder.layers.{bid}.mlp.dense_h_to_4h", # chatglm "transformer.h.{bid}.mlp.c_fc_1", # exaone "model.layers.{bid}.feed_forward.up_proj", # llama4 "transformer_encoder.{bid}.ffn.w12", # neobert ), MODEL_TENSOR.FFN_UP_EXP: ( "layers.{bid}.feed_forward.experts.w3", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear_v", # Grok (merged) "transformer.blocks.{bid}.ffn.experts.mlp.v1", # dbrx "model.layers.{bid}.mlp.experts.up_proj", # qwen2moe olmoe (merged) "model.layers.{bid}.block_sparse_moe.experts.w3", # phimoe (merged) "model.layers.{bid}.feed_forward.experts.up_proj", # llama4 "encoder.layers.{bid}.mlp.experts.mlp.w1", # nomic-bert-moe ), MODEL_TENSOR.FFN_UP_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek deepseek2 "model.layers.{bid}.feed_forward.shared_expert.up_proj", # llama4 ), # AWQ-activation gate MODEL_TENSOR.FFN_ACT: ( "transformer.blocks.{bid}.ffn.act", # mpt ), # Feed-forward gate MODEL_TENSOR.FFN_GATE: ( "model.layers.{bid}.mlp.gate_proj", # llama-hf refact olmo2 "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 "encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2 (split up/gate, no longer used) "transformer.h.{bid}.mlp.linear_1", # refact "model.layers.{bid}.residual_mlp.w1", # arctic "transformer.h.{bid}.mlp.c_fc_0", # exaone "model.layers.{bid}.feed_forward.gate_proj", # llama4 ), MODEL_TENSOR.FFN_GATE_EXP: ( "layers.{bid}.feed_forward.experts.w1", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear", # Grok (merged) "transformer.blocks.{bid}.ffn.experts.mlp.w1", # dbrx "model.layers.{bid}.mlp.experts.gate_proj", # qwen2moe olmoe (merged) "model.layers.{bid}.block_sparse_moe.experts.w1", # phimoe (merged) "model.layers.{bid}.feed_forward.experts.gate_proj", # llama4 ), MODEL_TENSOR.FFN_GATE_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek deepseek2 "model.layers.{bid}.feed_forward.shared_expert.gate_proj", # llama4 ), # 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 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 "model.layers.{bid}.mlp.down_proj", # llama-hf nemotron olmo2 "layers.{bid}.feed_forward.w2", # llama-pth "encoder.layer.{bid}.output.dense", # bert "transformer.layer.{bid}.ffn.lin2", # distillbert "transformer.h.{bid}.mlp.fc_out", # gpt-j "language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon "model.layers.{bid}.mlp.dense_4h_to_h", # persimmon "h.{bid}.mlp.c_proj", # gpt2 "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo "model.layers.{bid}.feed_forward.w2", # internlm2 "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 "encoder.layers.{bid}.mlp.dense_4h_to_h", # chatglm "model.layers.h.{bid}.mlp.c_proj", # exaone "model.layers.{bid}.feed_forward.down_proj", # llama4 "transformer_encoder.{bid}.ffn.w3", # neobert ), MODEL_TENSOR.FFN_DOWN_EXP: ( "layers.{bid}.feed_forward.experts.w2", # mixtral (merged) "transformer.decoder_layer.{bid}.moe.linear_1", # Grok (merged) "transformer.blocks.{bid}.ffn.experts.mlp.w2", # dbrx "model.layers.{bid}.mlp.experts.down_proj", # qwen2moe olmoe (merged) "model.layers.{bid}.block_sparse_moe.output_linear", # granitemoe "model.layers.{bid}.block_sparse_moe.experts.w2", # phimoe (merged) "model.layers.{bid}.feed_forward.experts.down_proj", # llama4 "encoder.layers.{bid}.mlp.experts.mlp.w2", # nomic-bert-moe ), MODEL_TENSOR.FFN_DOWN_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek deepseek2 "model.layers.{bid}.feed_forward.shared_expert.down_proj", # llama4 "model.layers.{bid}.shared_mlp.output_linear", # granitemoe ), MODEL_TENSOR.ATTN_Q_NORM: ( "language_model.encoder.layers.{bid}.self_attention.q_layernorm", "model.layers.{bid}.self_attn.q_layernorm", # persimmon "model.layers.{bid}.self_attn.q_norm", # cohere olmoe chameleon olmo2 "transformer.blocks.{bid}.attn.q_ln", # sea-lion "encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2 "transformer.layers.{bid}.attn.q_norm", # openelm ), MODEL_TENSOR.ATTN_K_NORM: ( "language_model.encoder.layers.{bid}.self_attention.k_layernorm", "model.layers.{bid}.self_attn.k_layernorm", # persimmon "model.layers.{bid}.self_attn.k_norm", # cohere olmoe chameleon olmo2 "transformer.blocks.{bid}.attn.k_ln", # sea-lion "encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2 "transformer.layers.{bid}.attn.k_norm", # openelm ), MODEL_TENSOR.ROPE_FREQS: ( "language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon ), MODEL_TENSOR.LAYER_OUT_NORM: ( "encoder.layer.{bid}.output.LayerNorm", # bert "transformer.layer.{bid}.output_layer_norm", # distillbert "encoder.layers.{bid}.norm2", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_3", # Grok "encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2 "encoder.layer.{bid}.layer_norm_2" # jina-v2-code ), MODEL_TENSOR.SSM_IN: ( "model.layers.{bid}.in_proj", "backbone.layers.{bid}.mixer.in_proj", ), MODEL_TENSOR.SSM_CONV1D: ( "model.layers.{bid}.conv1d", "backbone.layers.{bid}.mixer.conv1d", ), MODEL_TENSOR.SSM_X: ( "model.layers.{bid}.x_proj", "backbone.layers.{bid}.mixer.x_proj", ), MODEL_TENSOR.SSM_DT: ( "model.layers.{bid}.dt_proj", "backbone.layers.{bid}.mixer.dt_proj", ), MODEL_TENSOR.SSM_A: ( "model.layers.{bid}.A_log", "backbone.layers.{bid}.mixer.A_log", ), MODEL_TENSOR.SSM_D: ( "model.layers.{bid}.D", "backbone.layers.{bid}.mixer.D", ), MODEL_TENSOR.SSM_OUT: ( "model.layers.{bid}.out_proj", "backbone.layers.{bid}.mixer.out_proj", ), MODEL_TENSOR.TIME_MIX_W0: ( "model.layers.{bid}.attention.w0", # rwkv7 ), MODEL_TENSOR.TIME_MIX_W1: ( "rwkv.blocks.{bid}.attention.time_maa_w1", # rwkv6 "model.layers.{bid}.self_attn.time_maa_w1", # rwkv6qwen2 "model.layers.{bid}.attention.w1", # rwkv7 ), MODEL_TENSOR.TIME_MIX_W2: ( "rwkv.blocks.{bid}.attention.time_maa_w2", # rwkv6 "model.layers.{bid}.self_attn.time_maa_w2", # rwkv6qwen2 "model.layers.{bid}.attention.w2", # rwkv7 ), MODEL_TENSOR.TIME_MIX_A0: ( "model.layers.{bid}.attention.a0", # rwkv7 ), MODEL_TENSOR.TIME_MIX_A1: ( "model.layers.{bid}.attention.a1", # rwkv7 ), MODEL_TENSOR.TIME_MIX_A2: ( "model.layers.{bid}.attention.a2", # rwkv7 ), MODEL_TENSOR.TIME_MIX_V0: ( "model.layers.{bid}.attention.v0", # rwkv7 ), MODEL_TENSOR.TIME_MIX_V1: ( "model.layers.{bid}.attention.v1", # rwkv7 ), MODEL_TENSOR.TIME_MIX_V2: ( "model.layers.{bid}.attention.v2", # rwkv7 ), MODEL_TENSOR.TIME_MIX_G1: ( "model.layers.{bid}.attention.g1", # rwkv7 ), MODEL_TENSOR.TIME_MIX_G2: ( "model.layers.{bid}.attention.g2", # rwkv7 ), MODEL_TENSOR.TIME_MIX_K_K: ( "model.layers.{bid}.attention.k_k", # rwkv7 ), MODEL_TENSOR.TIME_MIX_K_A: ( "model.layers.{bid}.attention.k_a", # rwkv7 ), MODEL_TENSOR.TIME_MIX_R_K: ( "model.layers.{bid}.attention.r_k", # rwkv7 ), MODEL_TENSOR.TIME_MIX_LERP_X: ( "rwkv.blocks.{bid}.attention.time_maa_x", # rwkv6 "model.layers.{bid}.self_attn.time_maa_x", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_LERP_K: ( "rwkv.blocks.{bid}.attention.time_maa_k", # rwkv6 "model.layers.{bid}.self_attn.time_maa_k", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_LERP_V: ( "rwkv.blocks.{bid}.attention.time_maa_v", # rwkv6 "model.layers.{bid}.self_attn.time_maa_v", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_LERP_R: ( "rwkv.blocks.{bid}.attention.time_maa_r", # rwkv6 "model.layers.{bid}.self_attn.time_maa_r", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_LERP_G: ( "rwkv.blocks.{bid}.attention.time_maa_g", # rwkv6 "model.layers.{bid}.self_attn.time_maa_g", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_LERP_W: ( "rwkv.blocks.{bid}.attention.time_maa_w", # rwkv6 "model.layers.{bid}.self_attn.time_maa_w", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_FIRST: ( "rwkv.blocks.{bid}.attention.time_faaaa", # rwkv6 ), MODEL_TENSOR.TIME_MIX_DECAY: ( "rwkv.blocks.{bid}.attention.time_decay", # rwkv6 "model.layers.{bid}.self_attn.time_decay", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_DECAY_W1: ( "rwkv.blocks.{bid}.attention.time_decay_w1", # rwkv6 "model.layers.{bid}.self_attn.time_decay_w1", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_DECAY_W2: ( "rwkv.blocks.{bid}.attention.time_decay_w2", # rwkv6 "model.layers.{bid}.self_attn.time_decay_w2", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_KEY: ( "rwkv.blocks.{bid}.attention.key", # rwkv6 "model.layers.{bid}.self_attn.k_proj", # rwkv6qwen2 "model.layers.{bid}.attention.key", # rwkv7 "model.layers.{bid}.attention.k_proj", # rwkv7 ), MODEL_TENSOR.TIME_MIX_VALUE: ( "rwkv.blocks.{bid}.attention.value", # rwkv6 "model.layers.{bid}.self_attn.v_proj", # rwkv6qwen2 "model.layers.{bid}.attention.value", # rwkv7 "model.layers.{bid}.attention.v_proj", # rwkv7 ), MODEL_TENSOR.TIME_MIX_RECEPTANCE: ( "rwkv.blocks.{bid}.attention.receptance", # rwkv6 "model.layers.{bid}.self_attn.q_proj", # rwkv6qwen2 "model.layers.{bid}.attention.receptance", # rwkv7 "model.layers.{bid}.attention.r_proj", # rwkv7 ), MODEL_TENSOR.TIME_MIX_GATE: ( "rwkv.blocks.{bid}.attention.gate", # rwkv6 "model.layers.{bid}.self_attn.gate", # rwkv6qwen2 ), MODEL_TENSOR.TIME_MIX_LN: ( "rwkv.blocks.{bid}.attention.ln_x", # rwkv6 "model.layers.{bid}.attention.ln_x" # rwkv7 ), MODEL_TENSOR.TIME_MIX_OUTPUT: ( "rwkv.blocks.{bid}.attention.output", # rwkv6 "model.layers.{bid}.self_attn.o_proj", # rwkv6qwen2 "model.layers.{bid}.attention.output", # rwkv7 "model.layers.{bid}.attention.o_proj", # rwkv7 ), MODEL_TENSOR.CHANNEL_MIX_LERP_K: ( "rwkv.blocks.{bid}.feed_forward.time_maa_k", # rwkv6 "model.layers.{bid}.feed_forward.x_k", # rwkv7 ), MODEL_TENSOR.CHANNEL_MIX_LERP_R: ( "rwkv.blocks.{bid}.feed_forward.time_maa_r", # rwkv6 ), MODEL_TENSOR.CHANNEL_MIX_KEY: ( "rwkv.blocks.{bid}.feed_forward.key", # rwkv6 "model.layers.{bid}.feed_forward.key", # rwkv7 ), MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: ( "rwkv.blocks.{bid}.feed_forward.receptance", # rwkv6 ), MODEL_TENSOR.CHANNEL_MIX_VALUE: ( "rwkv.blocks.{bid}.feed_forward.value", # rwkv6 "model.layers.{bid}.feed_forward.value", # rwkv7 ), MODEL_TENSOR.ATTN_Q_A: ( "model.layers.{bid}.self_attn.q_a_proj", # deepseek2 ), MODEL_TENSOR.ATTN_Q_B: ( "model.layers.{bid}.self_attn.q_b_proj", # deepseek2 ), MODEL_TENSOR.ATTN_KV_A_MQA: ( "model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2 ), MODEL_TENSOR.ATTN_KV_B: ( "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2 ), MODEL_TENSOR.ATTN_K_B: ( "model.layers.{bid}.self_attn.k_b_proj", # deepseek2 ), MODEL_TENSOR.ATTN_V_B: ( "model.layers.{bid}.self_attn.v_b_proj", # deepseek2 ), MODEL_TENSOR.ATTN_Q_A_NORM: ( "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2 ), MODEL_TENSOR.ATTN_KV_A_NORM: ( "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2 ), MODEL_TENSOR.ATTN_SUB_NORM: ( "model.layers.{bid}.self_attn.inner_attn_ln", # bitnet ), 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 ), ############################################################################ # TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg MODEL_TENSOR.ENC_OUTPUT_NORM: ( "encoder.final_layer_norm", # t5 "layer_norm", # neobert ), MODEL_TENSOR.CLS: ( "classifier", # jina "classifier.dense", # roberta "pre_classifier", # distillbert "dense", # neobert ), MODEL_TENSOR.CLS_OUT: ( "classifier.out_proj", # roberta ), ############################################################################# MODEL_TENSOR.CONVNEXT_DW: ( "backbone.convnext.{bid}.dwconv", # wavtokenizer ), MODEL_TENSOR.CONVNEXT_NORM: ( "backbone.convnext.{bid}.norm", # wavtokenizer ), MODEL_TENSOR.CONVNEXT_PW1: ( "backbone.convnext.{bid}.pwconv1", # wavtokenizer ), MODEL_TENSOR.CONVNEXT_PW2: ( "backbone.convnext.{bid}.pwconv2", # wavtokenizer ), MODEL_TENSOR.CONVNEXT_GAMMA: ( "backbone.convnext.{bid}.gamma", # wavtokenizer ), MODEL_TENSOR.POSNET_CONV1: ( "backbone.posnet.{bid}.conv1", # wavtokenizer ), MODEL_TENSOR.POSNET_CONV2: ( "backbone.posnet.{bid}.conv2", # wavtokenizer ), MODEL_TENSOR.POSNET_NORM: ( "backbone.posnet.{bid}.norm", # wavtokenizer ), MODEL_TENSOR.POSNET_NORM1: ( "backbone.posnet.{bid}.norm1", # wavtokenizer ), MODEL_TENSOR.POSNET_NORM2: ( "backbone.posnet.{bid}.norm2", # wavtokenizer ), MODEL_TENSOR.POSNET_ATTN_NORM: ( "backbone.posnet.{bid}.norm", # wavtokenizer ), MODEL_TENSOR.POSNET_ATTN_Q: ( "backbone.posnet.{bid}.q", # wavtokenizer ), MODEL_TENSOR.POSNET_ATTN_K: ( "backbone.posnet.{bid}.k", # wavtokenizer ), MODEL_TENSOR.POSNET_ATTN_V: ( "backbone.posnet.{bid}.v", # wavtokenizer ), MODEL_TENSOR.POSNET_ATTN_OUT: ( "backbone.posnet.{bid}.proj_out", # wavtokenizer ), ############################################################################# ## Vision encoder MODEL_TENSOR.V_MMPROJ: ( "multi_modal_projector.linear_{bid}", "visual.merger.mlp.{bid}", # qwen2vl ), MODEL_TENSOR.V_MMPROJ_FC: ( "model.connector.modality_projection.proj", # SmolVLM ), MODEL_TENSOR.V_MMPROJ_MLP: ( "model.mm_projector.mlp.mlp.{bid}", "vision_model.vision_adapter.mlp.fc{bid}", # llama 4 "mlp1.{bid}", # InternVL ), MODEL_TENSOR.V_MMPROJ_PEG: ( "model.mm_projector.peg.peg.{bid}", ), MODEL_TENSOR.V_ENC_EMBD_CLS: ( "vision_tower.vision_model.embeddings.class_embedding", "vision_model.class_embedding", # llama 4 ), MODEL_TENSOR.V_ENC_EMBD_PATCH: ( "vision_tower.vision_model.embeddings.patch_embedding", "vpm.embeddings.patch_embedding", "model.vision_model.embeddings.patch_embedding", # SmolVLM "vision_tower.patch_conv", # pixtral "vision_model.patch_embedding.linear", # llama 4 "visual.patch_embed.proj", # qwen2vl ), MODEL_TENSOR.V_ENC_EMBD_POS: ( "vision_tower.vision_model.embeddings.position_embedding", "vpm.embeddings.position_embedding", "model.vision_model.embeddings.position_embedding", # SmolVLM "vision_model.positional_embedding_vlm", # llama 4 ), MODEL_TENSOR.V_ENC_ATTN_Q: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.q_proj", "vpm.encoder.layers.{bid}.self_attn.q_proj", "model.vision_model.encoder.layers.{bid}.self_attn.q_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.q_proj", # llama4 "vision_tower.transformer.layers.{bid}.attention.q_proj", # pixtral "visual.blocks.{bid}.attn.q", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_ATTN_Q_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.attn.q_norm", # InternVL ), MODEL_TENSOR.V_ENC_ATTN_K: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.k_proj", "vpm.encoder.layers.{bid}.self_attn.k_proj", "model.vision_model.encoder.layers.{bid}.self_attn.k_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.k_proj", # llama4 "vision_tower.transformer.layers.{bid}.attention.k_proj", # pixtral "visual.blocks.{bid}.attn.k", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_ATTN_K_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.attn.k_norm", # InternVL ), MODEL_TENSOR.V_ENC_ATTN_V: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.v_proj", "vpm.encoder.layers.{bid}.self_attn.v_proj", "model.vision_model.encoder.layers.{bid}.self_attn.v_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.v_proj", # llama4 "vision_tower.transformer.layers.{bid}.attention.v_proj", # pixtral "visual.blocks.{bid}.attn.v", # qwen2vl, generated ), MODEL_TENSOR.V_ENC_INPUT_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.layer_norm1", "vision_tower.vision_model.encoder.layers.{bid}.norm1", # InternVL "vpm.encoder.layers.{bid}.layer_norm1", "model.vision_model.encoder.layers.{bid}.layer_norm1", # SmolVLM "vision_tower.transformer.layers.{bid}.attention_norm", # pixtral "vision_model.model.layers.{bid}.input_layernorm", # llama4 "visual.blocks.{bid}.norm1", # qwen2vl ), MODEL_TENSOR.V_ENC_ATTN_O: ( "vision_tower.vision_model.encoder.layers.{bid}.self_attn.out_proj", "vision_tower.vision_model.encoder.layers.{bid}.attn.proj", # InternVL "vpm.encoder.layers.{bid}.self_attn.out_proj", "model.vision_model.encoder.layers.{bid}.self_attn.out_proj", # SmolVLM "vision_model.model.layers.{bid}.self_attn.o_proj", # llama4 "vision_tower.transformer.layers.{bid}.attention.o_proj", # pixtral "visual.blocks.{bid}.attn.proj", # qwen2vl ), MODEL_TENSOR.V_ENC_POST_ATTN_NORM: ( "vision_tower.vision_model.encoder.layers.{bid}.layer_norm2", "vision_tower.vision_model.encoder.layers.{bid}.norm2", # InternVL "vpm.encoder.layers.{bid}.layer_norm2", "model.vision_model.encoder.layers.{bid}.layer_norm2", # SmolVLM "vision_model.model.layers.{bid}.post_attention_layernorm", # llama4 "vision_tower.transformer.layers.{bid}.ffn_norm", # pixtral "visual.blocks.{bid}.norm2", # qwen2vl ), MODEL_TENSOR.V_ENC_FFN_UP: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc1", "vpm.encoder.layers.{bid}.mlp.fc1", "model.vision_model.encoder.layers.{bid}.mlp.fc1", # SmolVLM, gemma3 "vision_tower.transformer.layers.{bid}.feed_forward.up_proj", # pixtral "vision_model.model.layers.{bid}.mlp.fc1", # llama4 "visual.blocks.{bid}.mlp.fc1", # qwen2vl "visual.blocks.{bid}.mlp.up_proj", # qwen2.5vl ), MODEL_TENSOR.V_ENC_FFN_GATE: ( "vision_tower.transformer.layers.{bid}.feed_forward.gate_proj", # pixtral "visual.blocks.{bid}.mlp.gate_proj", # qwen2.5vl ), MODEL_TENSOR.V_ENC_FFN_DOWN: ( "vision_tower.vision_model.encoder.layers.{bid}.mlp.fc2", "vpm.encoder.layers.{bid}.mlp.fc2", "model.vision_model.encoder.layers.{bid}.mlp.fc2", # SmolVLM, gemma3 "vision_tower.transformer.layers.{bid}.feed_forward.down_proj", # pixtral "vision_model.model.layers.{bid}.mlp.fc2", # llama4 "visual.blocks.{bid}.mlp.fc2", # qwen2vl "visual.blocks.{bid}.mlp.down_proj", # qwen2.5vl ), MODEL_TENSOR.V_LAYER_SCALE_1: ( "vision_tower.vision_model.encoder.layers.{bid}.ls1", # InternVL ), MODEL_TENSOR.V_LAYER_SCALE_2: ( "vision_tower.vision_model.encoder.layers.{bid}.ls2", # InternVL ), MODEL_TENSOR.V_PRE_NORM: ( "vision_tower.vision_model.pre_layrnorm", "vision_tower.ln_pre", # pixtral "vision_model.layernorm_pre", # llama4 ), MODEL_TENSOR.V_POST_NORM: ( "vision_tower.vision_model.post_layernorm", "model.vision_model.post_layernorm", # SmolVLM "vision_model.layernorm_post", # llama4 "visual.merger.ln_q", # qwen2vl ), MODEL_TENSOR.V_MM_INP_PROJ: ( "multi_modal_projector.mm_input_projection", ), MODEL_TENSOR.V_MM_INP_NORM: ( "multi_modal_projector.norm", ), MODEL_TENSOR.V_MM_SOFT_EMB_NORM: ( "multi_modal_projector.mm_soft_emb_norm", ), MODEL_TENSOR.V_RESMPL_POS_EMBD_K: ( "resampler.pos_embed_k", ), MODEL_TENSOR.V_RESMPL_ATTN_Q: ( "resampler.attn.in_proj_q", # tensor generated from resampler.attn.in_proj ), MODEL_TENSOR.V_RESMPL_ATTN_K: ( "resampler.attn.in_proj_k", # tensor generated from resampler.attn.in_proj ), MODEL_TENSOR.V_RESMPL_ATTN_V: ( "resampler.attn.in_proj_v", # tensor generated from resampler.attn.in_proj ), MODEL_TENSOR.V_RESMPL_ATTN_OUT: ( "resampler.attn.out_proj", ), MODEL_TENSOR.V_RESMPL_KV: ( "resampler.kv_proj", ), MODEL_TENSOR.V_RESMPL_POST_NORM: ( "resampler.ln_post", ), MODEL_TENSOR.V_RESMPL_KV_NORM: ( "resampler.ln_kv", ), MODEL_TENSOR.V_RESMPL_Q_NORM: ( "resampler.ln_q", ), MODEL_TENSOR.V_RESMPL_PROJ: ( "resampler.proj", ), MODEL_TENSOR.V_RESMPL_QUERY: ( "resampler.query", ), MODEL_TENSOR.V_TOK_EMBD_IMG_BREAK: ( "v.token_embd.img_break", # for pixtral, this is a generated vector ), MODEL_TENSOR.V_MM_PATCH_MERGER: ( "multi_modal_projector.patch_merger.merging_layer", # mistral small 3.1 ), # audio (mtmd) MODEL_TENSOR.A_ENC_EMBD_POS: ( "audio_tower.embed_positions", # ultravox ), MODEL_TENSOR.A_ENC_CONV1D: ( "audio_tower.conv{bid}", # ultravox ), MODEL_TENSOR.A_PRE_NORM: (), MODEL_TENSOR.A_POST_NORM: ( "audio_tower.layer_norm", # ultravox "audio_tower.ln_post", # qwen2omni ), MODEL_TENSOR.A_ENC_ATTN_Q: ( "audio_tower.layers.{bid}.self_attn.q_proj", # ultravox ), MODEL_TENSOR.A_ENC_ATTN_K: ( "audio_tower.layers.{bid}.self_attn.k_proj", # ultravox ), MODEL_TENSOR.A_ENC_ATTN_V: ( "audio_tower.layers.{bid}.self_attn.v_proj", # ultravox ), MODEL_TENSOR.A_ENC_INPUT_NORM: ( "audio_tower.layers.{bid}.self_attn_layer_norm", # ultravox ), MODEL_TENSOR.A_ENC_OUTPUT: ( "audio_tower.layers.{bid}.self_attn.out_proj", # ultravox ), MODEL_TENSOR.A_ENC_OUTPUT_NORM: ( "audio_tower.layers.{bid}.final_layer_norm", # ultravox ), MODEL_TENSOR.A_ENC_FFN_UP: ( "audio_tower.layers.{bid}.fc1", # ultravox ), MODEL_TENSOR.A_ENC_FFN_GATE: (), MODEL_TENSOR.A_ENC_FFN_DOWN: ( "audio_tower.layers.{bid}.fc2", # ultravox ), # note: some tensors below has "audio." pseudo-prefix, to prevent conflicts with vision tensors # this prefix is added in the conversion code in modify_tensors() MODEL_TENSOR.A_MMPROJ: ( "audio.multi_modal_projector.linear_{bid}", # ultravox ), MODEL_TENSOR.A_MMPROJ_FC: ( "audio.multi_modal_projector.linear", # qwen2audio "audio_tower.proj", # qwen2omni ), MODEL_TENSOR.A_MM_NORM_PRE: ( "audio.multi_modal_projector.ln_pre", # ultravox ), MODEL_TENSOR.A_MM_NORM_MID: ( "audio.multi_modal_projector.ln_mid", # ultravox ), } # architecture-specific block mappings arch_block_mappings_cfg: dict[MODEL_ARCH, dict[MODEL_TENSOR, tuple[str, ...]]] = { MODEL_ARCH.ARCTIC: { MODEL_TENSOR.FFN_NORM: ( "model.layers.{bid}.residual_layernorm", ), MODEL_TENSOR.FFN_NORM_EXP: ( "model.layers.{bid}.post_attention_layernorm", ), }, } mapping: dict[str, tuple[MODEL_TENSOR, str]] def __init__(self, arch: MODEL_ARCH, n_blocks: int): self.mapping = {} for tensor, keys in self.mappings_cfg.items(): if tensor not in MODEL_TENSORS[arch]: continue tensor_name = TENSOR_NAMES[tensor] self.mapping[tensor_name] = (tensor, tensor_name) for key in keys: self.mapping[key] = (tensor, tensor_name) if arch in self.arch_block_mappings_cfg: self.block_mappings_cfg.update(self.arch_block_mappings_cfg[arch]) for bid in range(n_blocks): for tensor, keys in self.block_mappings_cfg.items(): if tensor not in MODEL_TENSORS[arch]: continue tensor_name = TENSOR_NAMES[tensor].format(bid = bid) self.mapping[tensor_name] = (tensor, tensor_name) for key in keys: key = key.format(bid = bid) self.mapping[key] = (tensor, tensor_name) def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None: result = self.mapping.get(key) if result is not None: return result for suffix in try_suffixes: if key.endswith(suffix): result = self.mapping.get(key[:-len(suffix)]) if result is not None: return result[0], result[1] + suffix return None def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None: result = self.get_type_and_name(key, try_suffixes = try_suffixes) if result is None: return None return result[1] def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None: result = self.get_type_and_name(key, try_suffixes = try_suffixes) if result is None: return None return result[0] def __getitem__(self, key: str) -> str: try: return self.mapping[key][1] except KeyError: raise KeyError(key) def __contains__(self, key: str) -> bool: return key in self.mapping def __repr__(self) -> str: return repr(self.mapping) def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: return TensorNameMap(arch, n_blocks)