model : BailingMoE3 Support (#26608)
* Adding support for bailingmoe3
* Adds speculative decoding support
* Make BailingMoE3 safe gate metadata optional
* bailingmoe3: apply trained SwiGLU clamps
* common: fix Bailing V3 tool argument parsing
* llama-model-saver, instantiate float vector metadata writer
* bailingmoe3: support Q-LoRA (Ling-3.0-tiny)
Ling-3.0-flash sets q_lora_rank: None and projects Q directly, so the current
implementation loads a single ATTN_Q tensor. Ling-3.0-tiny sets q_lora_rank: 256
and routes Q through a LoRA bottleneck instead:
q_a_proj -> q_a_layernorm -> q_b_proj
Conversion therefore failed with:
ValueError: Can not map tensor 'model.layers.3.attention.q_a_layernorm.weight'
Add the missing path, mirroring the existing deepseek2 MLA implementation:
* constants.py - add ATTN_Q_A / ATTN_Q_B / ATTN_Q_A_NORM to BAILINGMOE3
* tensor_mapping.py - map model.layers.{bid}.attention.q_{a,b}_proj and
q_a_layernorm
* conversion - emit attention.q_lora_rank when the config has it
* bailingmoe3.cpp - read n_lora_q; create the Q-LoRA tensors and build Q
through the bottleneck when q_lora_rank > 0
Everything is gated on q_lora_rank > 0. Ling-3.0-flash's config has no
q_lora_rank, the converter only emits the key when present, hparams.n_lora_q
defaults to 0, and get_key(..., required=false) leaves the target untouched when
the key is absent - so flash keeps taking the existing direct-Q branch.
The LoRA path produces the same shape as the direct projection, so the
nope/rope split, RoPE application and wk_b absorption downstream are unchanged.
* small mtp change
* bailingmoe3: support separate MTP GGUF and Q-LoRA MTP
* gguf: remove duplicate add_kda_gate_lower_bound definition
---------
Co-authored-by: bloomer <bloomer@booper.brushtail.me>
Co-authored-by: Dyluhn <dylanranejohnston1@gmail.com>
This commit is contained in:
Toby
@@ -255,6 +255,7 @@ 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 olmoe olmo2 phimoe
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"model.layers.{bid}.attention.q_proj", # bailingmoe3
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"layers.{bid}.self_attn.q_proj", # embeddinggemma
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"model.layers.{bid}.self_attn.q_proj_no_perm", # llama-custom
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"layers.{bid}.attention.wq", # llama-pth
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@@ -275,6 +276,7 @@ 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 olmoe olmo2 phimoe
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"model.layers.{bid}.attention.k_proj", # bailingmoe3
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"layers.{bid}.self_attn.k_proj", # embeddinggemma
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"model.layers.{bid}.self_attn.k_proj_no_perm", # llama-custom
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"layers.{bid}.attention.wk", # llama-pth
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@@ -296,6 +298,7 @@ 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 olmoe olmo2 phimoe
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"model.layers.{bid}.attention.v_proj", # bailingmoe3
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"layers.{bid}.self_attn.v_proj", # embeddinggemma
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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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@@ -321,6 +324,8 @@ class TensorNameMap:
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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 olmoe olmo2 phimoe
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"model.layers.{bid}.attention.o_proj", # bailingmoe3
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"model.layers.{bid}.attention.dense", # bailingmoe3 MLA
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"layers.{bid}.self_attn.o_proj", # embeddinggemma
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"model.layers.{bid}.self_attn.out_proj", # lfm2 minimax-01
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"model.layers.{bid}.self_attn.linear_attn", # deci
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@@ -834,6 +839,7 @@ class TensorNameMap:
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"model.layers.{bid}.linear_attn.dt_proj", # qwen3next
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"backbone.layers.{bid}.mixer.dt", # nemotron-h-moe
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"model.layers.{bid}.self_attn.dt_proj", # kimi
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"model.layers.{bid}.attention.dt_proj", # bailingmoe3
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),
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MODEL_TENSOR.SSM_DT_NORM: (
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@@ -848,6 +854,7 @@ class TensorNameMap:
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"model.layers.layers.{bid}.mixer.A_log", # plamo2
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"model.layers.{bid}.linear_attn.A_log", # qwen3next
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"model.layers.{bid}.self_attn.A_log", # kimi
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"model.layers.{bid}.attention.A_log", # bailingmoe3
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),
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MODEL_TENSOR.SSM_B_NORM: (
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@@ -874,6 +881,7 @@ class TensorNameMap:
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"model.layers.{bid}.linear_attn.norm", # qwen3next
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"backbone.layers.{bid}.mixer.norm", # mamba2
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"model.layers.{bid}.self_attn.o_norm", # kimi
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"model.layers.{bid}.attention.o_norm", # bailingmoe3
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),
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MODEL_TENSOR.SSM_OUT: (
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@@ -895,12 +903,15 @@ class TensorNameMap:
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# Kimi Linear KDA (using SSM_ prefix for consistency)
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MODEL_TENSOR.SSM_CONV1D_Q: (
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"model.layers.{bid}.self_attn.q_conv1d",
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"model.layers.{bid}.attention.q_conv1d",
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),
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MODEL_TENSOR.SSM_CONV1D_K: (
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"model.layers.{bid}.self_attn.k_conv1d",
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"model.layers.{bid}.attention.k_conv1d",
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),
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MODEL_TENSOR.SSM_CONV1D_V: (
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"model.layers.{bid}.self_attn.v_conv1d",
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"model.layers.{bid}.attention.v_conv1d",
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),
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MODEL_TENSOR.SSM_F_A: (
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"model.layers.{bid}.self_attn.f_a_proj",
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@@ -911,6 +922,7 @@ class TensorNameMap:
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MODEL_TENSOR.SSM_BETA: (
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"model.layers.{bid}.linear_attn.in_proj_b", # qwen3.5
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"model.layers.{bid}.self_attn.b_proj", # Kimi Linear
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"model.layers.{bid}.attention.b_proj", # bailingmoe3
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),
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# Kimi K3 latent MoE: routed experts operate in a down-projected space
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MODEL_TENSOR.FFN_ROUTED_DOWN: (
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@@ -1103,40 +1115,48 @@ class TensorNameMap:
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MODEL_TENSOR.ATTN_Q_A: (
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"model.layers.{bid}.self_attn.q_a_proj", # deepseek2
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"model.layers.{bid}.attention.q_a_proj", # bailingmoe3 (Ling-3.0-tiny)
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"layers.{bid}.attention.wq_a", # mistral-large
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),
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MODEL_TENSOR.ATTN_Q_B: (
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"model.layers.{bid}.self_attn.q_b_proj", # deepseek2
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"model.layers.{bid}.attention.q_b_proj", # bailingmoe3 (Ling-3.0-tiny)
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"layers.{bid}.attention.wq_b", # mistral-large
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),
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MODEL_TENSOR.ATTN_KV_A_MQA: (
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"model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2
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"model.layers.{bid}.attention.kv_a_proj_with_mqa", # bailingmoe3
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"layers.{bid}.attention.wkv_a_with_mqa", # mistral-large
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),
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MODEL_TENSOR.ATTN_KV_B: (
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"model.layers.{bid}.self_attn.kv_b_proj", # deepseek2
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"model.layers.{bid}.attention.kv_b_proj", # bailingmoe3
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),
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MODEL_TENSOR.ATTN_K_B: (
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"model.layers.{bid}.self_attn.k_b_proj", # deepseek2
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"model.layers.{bid}.attention.k_b_proj", # bailingmoe3
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"layers.{bid}.attention.k_b_proj", # mistral-large
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),
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MODEL_TENSOR.ATTN_V_B: (
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"model.layers.{bid}.self_attn.v_b_proj", # deepseek2
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"model.layers.{bid}.attention.v_b_proj", # bailingmoe3
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"layers.{bid}.attention.v_b_proj", # mistral-large
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),
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MODEL_TENSOR.ATTN_Q_A_NORM: (
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"model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2
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"model.layers.{bid}.attention.q_a_layernorm", # bailingmoe3 (Ling-3.0-tiny)
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"layers.{bid}.attention.q_a_norm", # mistral-large
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),
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MODEL_TENSOR.ATTN_KV_A_NORM: (
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"model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2
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"model.layers.{bid}.attention.kv_a_layernorm", # bailingmoe3
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"layers.{bid}.attention.kv_a_norm", # mistral-large
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),
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