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model: support youtu-vl model (#18479)

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* Support Youtu-VL Model

* merge code

* fix bug

* revert qwen2 code & support rsplit in minja.hpp

* update warm info

* fix annotation

* u

* revert minja.hpp

* fix

* Do not write routed_scaling_factor to gguf when routed_scaling_factor is None

* fix expert_weights_scale

* LGTM after whitespace fixes

* fix

* fix

* fix

* layers to layer_index

* enum fix

---------

Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit is contained in:
tt
2026-01-02 02:25:54 +08:00
committed by GitHub
parent 3ccccc83f7
commit ced765be44
17 changed files with 473 additions and 36 deletions
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convert_hf_to_gguf.py
+92 -7
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@@ -1233,6 +1233,9 @@ class TextModel(ModelBase):
if chkhsh == "4a2e2abae11ca2b86d570fc5b44be4d5eb5e72cc8f22dd136a94b37da83ab665":
# ref: https://huggingface.co/KORMo-Team/KORMo-tokenizer
res = "kormo"
if chkhsh == "9d70134b369a70e5735009b6de918f7581b5211f7c074d1f89f753aea8248af1":
# ref: https://huggingface.co/tencent/Youtu-LLM-2B
res = "youtu"
if chkhsh == "16389f0a1f51ee53e562ffd51c371dc508639ab0e4261502071836e50e223e91":
# ref: https://huggingface.co/upstage/Solar-Open-100B
res = "solar-open"
@@ -7189,6 +7192,7 @@ class DeepseekModel(TextModel):
"DeepseekV2ForCausalLM",
"DeepseekV3ForCausalLM",
"KimiVLForConditionalGeneration",
"YoutuForCausalLM",
)
class DeepseekV2Model(TextModel):
model_arch = gguf.MODEL_ARCH.DEEPSEEK2
@@ -7255,7 +7259,15 @@ class DeepseekV2Model(TextModel):
super().set_gguf_parameters()
hparams = self.hparams
self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
# first_k_dense_replace: number of leading layers using dense FFN instead of MoE
# For non-MoE models (like Youtu), set to n_layer to use dense FFN for all layers
# For MoE models (like DeepSeek-V2), this is the number of leading non-MoE layers
has_moe = hparams.get("n_routed_experts") is not None
first_k_dense_replace = hparams.get("first_k_dense_replace")
if first_k_dense_replace is None:
# Default: if no MoE, all layers are dense; if MoE, none are dense
first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
self.gguf_writer.add_vocab_size(hparams["vocab_size"])
if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
@@ -7267,11 +7279,24 @@ class DeepseekV2Model(TextModel):
self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
# MoE parameters (required by C++ code for DEEPSEEK2 arch)
# For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
moe_intermediate_size = self.find_hparam(["moe_intermediate_size", "intermediate_size"], optional=False)
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
if (n_routed_experts := hparams.get("n_routed_experts")) is not None:
self.gguf_writer.add_expert_count(n_routed_experts)
# expert_shared_count is required by C++ code, default to 0 for non-MoE models
n_shared_experts = hparams.get("n_shared_experts", 0)
self.gguf_writer.add_expert_shared_count(n_shared_experts)
# When not set, C++ code will use scale_w = false to skip the no-op scaling
if (routed_scaling_factor := hparams.get("routed_scaling_factor")) is not None:
self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
if (norm_topk_prob := hparams.get("norm_topk_prob")) is not None and norm_topk_prob:
self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
@@ -7287,10 +7312,17 @@ class DeepseekV2Model(TextModel):
# skip vision tensors and remove "language_model." for Kimi-VL
if "vision_tower" in name or "multi_modal_projector" in name:
return []
if name.startswith("siglip2.") or name.startswith("merger."):
return []
if name.startswith("language_model."):
name = name.replace("language_model.", "")
# skip lm_head.weight if tie_word_embeddings is True
if self.hparams.get("tie_word_embeddings", False):
if name == "lm_head.weight" or name == "model.lm_head.weight":
logger.info("Skipping tied output layer 'lm_head.weight' (will use token_embd.weight)")
return []
# rename e_score_correction_bias tensors
if name.endswith("e_score_correction_bias"):
name = name.replace("e_score_correction_bias", "e_score_correction.bias")
@@ -10625,6 +10657,59 @@ class JanusProVisionModel(MmprojModel):
return []
@ModelBase.register("YOUTUVLForConditionalGeneration", "YOUTUVLForCausalLM")
class YOUTUVLVisionModel(MmprojModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
assert self.hparams_vision is not None
self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.YOUTUVL)
self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
# Handle activation function
hidden_act = str(self.hparams.get("hidden_act", "gelu_pytorch_tanh")).lower()
if hidden_act in ("gelu", "gelu_pytorch_tanh", "gelu_fast", "gelu_new", "gelu_accurate"):
self.gguf_writer.add_vision_use_gelu(True)
elif hidden_act == "silu":
self.gguf_writer.add_vision_use_silu(True)
else:
raise ValueError(f"Unsupported activation function for YOUTUVL: {hidden_act}")
self.gguf_writer.add_vision_spatial_merge_size(self.hparams.get("spatial_merge_size", 2))
window_size = self.hparams.get("window_size")
if window_size is not None:
self.gguf_writer.add_vision_window_size(window_size)
# fullatt_block_indexes contains explicit layer indices that use full attention
# e.g., [2, 5, 8, 11] means layers 2, 5, 8, 11 use full attention
# All other layers use window attention
fullatt_block_indexes = self.hparams.get("fullatt_block_indexes")
assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for youtuvl"
# Store the explicit layer indices for YoutuVL (irregular pattern approach)
self.gguf_writer.add_vision_wa_layer_indexes(layers=fullatt_block_indexes)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
# Skip language model tensors
skip_prefixes = ('lm_head.', 'model.layers.', 'model.embed_tokens.', 'model.norm.')
if name.startswith(skip_prefixes):
return []
# Try to map the tensor using TensorNameMap (handles vision encoder and projector)
try:
new_name = self.map_tensor_name(name)
return [(new_name, data_torch)]
except ValueError:
# If mapping fails, log warning and skip
logger.warning(f"Cannot map tensor: {name}")
return []
@ModelBase.register("SolarOpenForCausalLM")
class SolarOpenModel(Glm4MoeModel):
model_arch = gguf.MODEL_ARCH.GLM4_MOE