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model : support Qwen3-Embedding (#15023)

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Douglas Hanley 2025-08-02 03:44:50 -05:00 • committed by GitHub
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3 changed files with 18 additions and 2 deletions
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3
convert_hf_to_gguf.py
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@ -849,6 +849,9 @@ class TextModel(ModelBase):
if chkhsh == "2085e1638f6c377a0aa4ead21b27bb4cb941bf800df86ed391011769c1758dfb":
# ref: https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B
res = "exaone4"
if chkhsh == "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c":
# ref: https://huggingface.co/Qwen/Qwen3-Embedding-8B
res = "qwen2"
if res is None:
logger.warning("\n")

16
gguf-py/gguf/tensor_mapping.py
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@ -33,6 +33,7 @@ class TensorNameMap:
"language_model.model.embed_tokens", # llama4
"encoder", # neobert
"model.transformer.wte", # llada
"embed_tokens", # qwen3-embedding
),
# Token type embeddings
@ -143,6 +144,7 @@ class TensorNameMap:
"transformer_encoder.{bid}.attention_norm", # neobert
"model.layers.{bid}.operator_norm", # lfm2
"model.transformer.blocks.{bid}.attn_norm", # llada
"layers.{bid}.input_layernorm", # qwen3-embedding
),
# Attention norm 2
@ -188,6 +190,7 @@ class TensorNameMap:
"transformer.h.{bid}.attn.attention.q_proj", # exaone
"model.layers.{bid}.self_attn.q_proj", # llama4
"model.transformer.blocks.{bid}.q_proj", # llada
"layers.{bid}.self_attn.q_proj", # qwen3-embedding
),
# Attention key
@ -205,6 +208,7 @@ class TensorNameMap:
"transformer.h.{bid}.attn.attention.k_proj", # exaone
"model.layers.{bid}.self_attn.k_proj", # llama4
"model.transformer.blocks.{bid}.k_proj", # llada
"layers.{bid}.self_attn.k_proj", # qwen3-embedding
),
# Attention value
@ -221,6 +225,7 @@ class TensorNameMap:
"transformer.h.{bid}.attn.attention.v_proj", # exaone
"model.layers.{bid}.self_attn.v_proj", # llama4
"model.transformer.blocks.{bid}.v_proj", # llada
"layers.{bid}.self_attn.v_proj", # qwen3-embedding
),
# Attention output
@ -254,6 +259,7 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.o_proj", # llama4
"transformer_encoder.{bid}.wo", # neobert
"model.transformer.blocks.{bid}.attn_out", # llada
"layers.{bid}.self_attn.o_proj", # qwen3-embedding
),
# Attention output norm
@ -300,6 +306,7 @@ class TensorNameMap:
"transformer_encoder.{bid}.ffn_norm", # neobert
"model.layers.layers.{bid}.pre_mlp_norm", # plamo2
"model.transformer.blocks.{bid}.ff_norm", # llada
"layers.{bid}.post_attention_layernorm", # qwen3-embedding
),
# Post feed-forward norm
@ -373,7 +380,8 @@ class TensorNameMap:
"model.layers.{bid}.feed_forward.up_proj", # llama4 jamba granite-hybrid
"transformer_encoder.{bid}.ffn.w12", # neobert
"model.layers.{bid}.block_sparse_moe.up", # smallthinker
"model.transformer.blocks.{bid}.up_proj", # llada
"model.transformer.blocks.{bid}.up_proj", # llada
"layers.{bid}.mlp.up_proj", # qwen3-embedding
),
MODEL_TENSOR.FFN_UP_EXP: (
@ -416,6 +424,7 @@ class TensorNameMap:
"model.layers.{bid}.feed_forward.gate_proj", # llama4 jamba granite-hybrid
"model.layers.{bid}.block_sparse_moe.gate", # smallthinker
"model.transformer.blocks.{bid}.ff_proj", # llada
"layers.{bid}.mlp.gate_proj", # qwen3-embedding
),
MODEL_TENSOR.FFN_GATE_EXP: (
@ -465,7 +474,8 @@ class TensorNameMap:
"model.layers.{bid}.feed_forward.down_proj", # llama4 jamba granite-hybrid
"transformer_encoder.{bid}.ffn.w3", # neobert
"model.layers.{bid}.block_sparse_moe.down", # smallthinker
"model.transformer.blocks.{bid}.ff_out", # llada
"model.transformer.blocks.{bid}.ff_out", # llada
"layers.{bid}.mlp.down_proj", # qwen3-embedding
),
MODEL_TENSOR.FFN_DOWN_EXP: (
@ -497,6 +507,7 @@ class TensorNameMap:
"encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2
"transformer.layers.{bid}.attn.q_norm", # openelm
"model.layers.layers.{bid}.mixer.q", # plamo2
"layers.{bid}.self_attn.q_norm", # qwen3-embedding
),
MODEL_TENSOR.ATTN_K_NORM: (
@ -508,6 +519,7 @@ class TensorNameMap:
"encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2
"transformer.layers.{bid}.attn.k_norm", # openelm
"model.layers.layers.{bid}.mixer.k", # plamo2
"layers.{bid}.self_attn.k_norm", # qwen3-embedding
),
MODEL_TENSOR.ROPE_FREQS: (

1
src/llama-model.cpp
View file

@ -899,6 +899,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
} break;
case LLM_ARCH_QWEN3:
{
ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type, false);
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
switch (hparams.n_layer) {
case 28: type = hparams.n_embd == 1024 ? LLM_TYPE_0_6B : LLM_TYPE_1_7B; break;