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[model] support Internvl3_5 (#9028)
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@ -280,7 +280,7 @@ Choose your path:
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| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
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| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
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| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
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| [InternVL 2.5-3](https://huggingface.co/OpenGVLab) | 1B/2B/8B/14B/38B/78B | intern_vl |
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| [InternVL 2.5-3.5](https://huggingface.co/OpenGVLab) | 1B/2B/4B/8B/14B/30B/38B/78B/241B | intern_vl |
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| [InternLM/Intern-S1-mini](https://huggingface.co/internlm/) | 8B | intern_s1 |
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| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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@ -282,7 +282,7 @@ https://github.com/user-attachments/assets/43b700c6-a178-41db-b1f8-8190a5d3fcfc
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| [Hunyuan](https://huggingface.co/tencent/) | 7B | hunyuan |
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| [Index](https://huggingface.co/IndexTeam) | 1.9B | index |
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| [InternLM 2-3](https://huggingface.co/internlm) | 7B/8B/20B | intern2 |
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| [InternVL 2.5-3](https://huggingface.co/OpenGVLab) | 1B/2B/8B/14B/38B/78B | intern_vl |
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| [InternVL 2.5-3.5](https://huggingface.co/OpenGVLab) | 1B/2B/4B/8B/14B/30B/38B/78B/241B | intern_vl |
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| [InternLM/Intern-S1-mini](https://huggingface.co/internlm/) | 8B | intern_s1 |
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| [Kimi-VL](https://huggingface.co/moonshotai) | 16B | kimi_vl |
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| [Llama](https://github.com/facebookresearch/llama) | 7B/13B/33B/65B | - |
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@ -1260,6 +1260,18 @@ register_model_group(
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DownloadSource.DEFAULT: "OpenGVLab/InternVL3-78B-hf",
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DownloadSource.MODELSCOPE: "OpenGVLab/InternVL3-78B-hf",
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},
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"InternVL3_5-1B-hf": {
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DownloadSource.DEFAULT: "llamafactory/InternVL3_5-1B-hf",
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},
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"InternVL3_5-2B-hf": {
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DownloadSource.DEFAULT: "llamafactory/InternVL3_5-2B-hf",
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},
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"InternVL3_5-4B-hf": {
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DownloadSource.DEFAULT: "llamafactory/InternVL3_5-4B-hf",
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},
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"InternVL3_5-8B-hf": {
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DownloadSource.DEFAULT: "llamafactory/InternVL3_5-8B-hf",
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},
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},
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template="intern_vl",
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multimodal=True,
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@ -39,6 +39,9 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
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return
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model_type = getattr(model.config, "model_type", None)
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text_config = getattr(model.config, "text_config", None)
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text_architectures = getattr(text_config, "architectures", None)
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if model_type == "dbrx":
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from transformers.models.dbrx.modeling_dbrx import DbrxFFN
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@ -102,7 +105,7 @@ def add_z3_leaf_module(model: "PreTrainedModel") -> None:
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_set_z3_leaf_modules(model, [Qwen2MoeSparseMoeBlock])
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if model_type == "qwen3_moe":
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if model_type == "qwen3_moe" or text_architectures == "Qwen3MoeForCausalLM": # for internvl_3_5
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from transformers.models.qwen3_moe.modeling_qwen3_moe import Qwen3MoeSparseMoeBlock
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_set_z3_leaf_modules(model, [Qwen3MoeSparseMoeBlock])
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@ -113,6 +116,8 @@ def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_t
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return
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model_type = getattr(config, "model_type", None)
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text_config = getattr(config, "text_config", None) # for multimodal model
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if model_type in [
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"dbrx",
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"granitemoe",
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@ -127,9 +132,18 @@ def configure_moe(config: "PretrainedConfig", model_args: "ModelArguments", is_t
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]:
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setattr(config, "output_router_logits", True)
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if text_config and getattr(text_config, "model_type", None) in [
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"glm4v_moe_text", # glmv4_5
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"qwen3_moe", # internvl_3_5
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]:
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setattr(text_config, "output_router_logits", True)
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if model_type in ["granitemoe", "jamba", "llama4", "mixtral", "olmoe", "phimoe", "qwen2_moe", "qwen3_moe"]:
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setattr(config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
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elif text_config and getattr(text_config, "model_type", None) in ["qwen3_moe"]:
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setattr(text_config, "router_aux_loss_coef", model_args.moe_aux_loss_coef)
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elif model_type == "deepseek":
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setattr(config, "aux_loss_alpha", model_args.moe_aux_loss_coef)
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