# Copyright (c) ModelScope Contributors. All rights reserved. from transformers import AutoTokenizer, PretrainedConfig, PreTrainedModel from transformers.dynamic_module_utils import get_class_from_dynamic_module from typing import Any, Dict from swift.template import TemplateType from swift.utils import Processor, safe_snapshot_download from ..constant import LLMModelType, MLLMModelType, RMModelType from ..model_arch import ModelArch from ..model_meta import Model, ModelGroup, ModelMeta from ..patcher import patch_output_clone, patch_output_to_input_device from ..register import ModelLoader, RewardModelLoader, register_model from ..utils import use_submodel_func from .qwen import Qwen2AudioLoader register_model( ModelMeta( LLMModelType.internlm, [ ModelGroup([ Model('Shanghai_AI_Laboratory/internlm-chat-7b', 'internlm/internlm-chat-7b'), Model('Shanghai_AI_Laboratory/internlm-7b', 'internlm/internlm-7b'), Model('Shanghai_AI_Laboratory/internlm-chat-7b-8k'), Model('Shanghai_AI_Laboratory/internlm-20b', 'internlm/internlm-20b'), Model('Shanghai_AI_Laboratory/internlm-chat-20b', 'internlm/internlm-chat-20b'), ]) ], template=TemplateType.internlm, architectures=['InternLMForCausalLM'], model_arch=ModelArch.llama, )) register_model( ModelMeta( LLMModelType.internlm2, [ ModelGroup([ Model('Shanghai_AI_Laboratory/internlm2-chat-1_8b', 'internlm/internlm2-chat-1_8b'), Model('Shanghai_AI_Laboratory/internlm2-1_8b', 'internlm/internlm2-1_8b'), Model('Shanghai_AI_Laboratory/internlm2-chat-1_8b-sft', 'internlm/internlm2-chat-1_8b-sft'), Model('Shanghai_AI_Laboratory/internlm2-base-7b', 'internlm/internlm2-base-7b'), Model('Shanghai_AI_Laboratory/internlm2-7b', 'internlm/internlm2-7b'), Model('Shanghai_AI_Laboratory/internlm2-chat-7b', 'internlm/internlm2-chat-7b'), Model('Shanghai_AI_Laboratory/internlm2-chat-7b-sft', 'internlm/internlm2-chat-7b-sft'), Model('Shanghai_AI_Laboratory/internlm2-base-20b', 'internlm/internlm2-base-20b'), Model('Shanghai_AI_Laboratory/internlm2-20b', 'internlm/internlm2-20b'), Model('Shanghai_AI_Laboratory/internlm2-chat-20b', 'internlm/internlm2-chat-20b'), Model('Shanghai_AI_Laboratory/internlm2-chat-20b-sft', 'internlm/internlm2-chat-20b-sft'), ]), ModelGroup([ Model('Shanghai_AI_Laboratory/internlm2-math-7b', 'internlm/internlm2-math-7b'), Model('Shanghai_AI_Laboratory/internlm2-math-base-7b', 'internlm/internlm2-math-base-7b'), Model('Shanghai_AI_Laboratory/internlm2-math-base-20b', 'internlm/internlm2-math-base-20b'), Model('Shanghai_AI_Laboratory/internlm2-math-20b', 'internlm/internlm2-math-20b'), ], tags=['math']), ModelGroup([ Model('Shanghai_AI_Laboratory/internlm2_5-1_8b-chat', 'internlm/internlm2_5-1_8b-chat'), Model('Shanghai_AI_Laboratory/internlm2_5-1_8b', 'internlm/internlm2_5-1_8b'), Model('Shanghai_AI_Laboratory/internlm2_5-7b', 'internlm/internlm2_5-7b'), Model('Shanghai_AI_Laboratory/internlm2_5-7b-chat', 'internlm/internlm2_5-7b-chat'), Model('Shanghai_AI_Laboratory/internlm2_5-7b-chat-1m', 'internlm/internlm2_5-7b-chat-1m'), Model('Shanghai_AI_Laboratory/internlm2_5-20b', 'internlm/internlm2_5-20b'), Model('Shanghai_AI_Laboratory/internlm2_5-20b-chat', 'internlm/internlm2_5-20b-chat'), ]) ], template=TemplateType.internlm2, requires=['transformers>=4.38'], architectures=['InternLM2ForCausalLM'], model_arch=ModelArch.internlm2, )) register_model( ModelMeta( LLMModelType.internlm3, [ ModelGroup([ Model('Shanghai_AI_Laboratory/internlm3-8b-instruct', 'internlm/internlm3-8b-instruct'), ]), ], template=TemplateType.internlm2, requires=['transformers>=4.48'], architectures=['InternLM3ForCausalLM'], model_arch=ModelArch.llama, )) class InternVLLoader(ModelLoader): def get_processor(self, model_dir: str, config: PretrainedConfig) -> Processor: self.auto_tokenizer_cls = AutoTokenizer return super().get_processor(model_dir, config) def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: model = super().get_model(model_dir, *args, **kwargs) if self.model_info.quant_method == 'bnb': # 'is_training' # patch: bnb backward shape mismatch bug if model is not None and model.language_model is not None: model.language_model.output.state.force_no_igemmlt = True use_submodel_func(model, 'language_model') patch_output_clone(model.language_model.get_input_embeddings()) return model register_model( ModelMeta( MLLMModelType.internvl_chat, [ ModelGroup([ Model('OpenGVLab/Mini-InternVL-Chat-2B-V1-5', 'OpenGVLab/Mini-InternVL-Chat-2B-V1-5'), Model('AI-ModelScope/InternVL-Chat-V1-5', 'OpenGVLab/InternVL-Chat-V1-5'), Model('AI-ModelScope/InternVL-Chat-V1-5-int8', 'OpenGVLab/InternVL-Chat-V1-5-int8'), ], template=TemplateType.internvl, requires=['transformers>=4.35', 'timm'], tags=['vision']), ModelGroup([ Model('OpenGVLab/Mini-InternVL-Chat-4B-V1-5', 'OpenGVLab/Mini-InternVL-Chat-4B-V1-5'), ], template=TemplateType.internvl_phi3, requires=['transformers>=4.35,<4.42', 'timm'], tags=['vision']), ModelGroup( [ Model('OpenGVLab/InternVL2-1B', 'OpenGVLab/InternVL2-1B'), Model('OpenGVLab/InternVL2-2B', 'OpenGVLab/InternVL2-2B'), Model('OpenGVLab/InternVL2-8B', 'OpenGVLab/InternVL2-8B'), Model('OpenGVLab/InternVL2-26B', 'OpenGVLab/InternVL2-26B'), Model('OpenGVLab/InternVL2-40B', 'OpenGVLab/InternVL2-40B'), Model('OpenGVLab/InternVL2-Llama3-76B', 'OpenGVLab/InternVL2-Llama3-76B'), # (infer use lmdeploy) Model('OpenGVLab/InternVL2-2B-AWQ', 'OpenGVLab/InternVL2-2B-AWQ'), Model('OpenGVLab/InternVL2-8B-AWQ', 'OpenGVLab/InternVL2-8B-AWQ'), Model('OpenGVLab/InternVL2-26B-AWQ', 'OpenGVLab/InternVL2-26B-AWQ'), Model('OpenGVLab/InternVL2-40B-AWQ', 'OpenGVLab/InternVL2-40B-AWQ'), Model('OpenGVLab/InternVL2-Llama3-76B-AWQ', 'OpenGVLab/InternVL2-Llama3-76B-AWQ'), # mpo Model('OpenGVLab/InternVL2-8B-MPO', 'OpenGVLab/InternVL2-8B-MPO'), # pretrain Model('OpenGVLab/InternVL2-Pretrain-Models:InternVL2-1B-Pretrain', 'OpenGVLab/InternVL2-Pretrain-Models:InternVL2-1B-Pretrain'), Model('OpenGVLab/InternVL2-Pretrain-Models:InternVL2-2B-Pretrain', 'OpenGVLab/InternVL2-Pretrain-Models:InternVL2-2B-Pretrain'), Model('OpenGVLab/InternVL2-Pretrain-Models:InternVL2-4B-Pretrain', 'OpenGVLab/InternVL2-Pretrain-Models:InternVL2-4B-Pretrain'), Model('OpenGVLab/InternVL2-Pretrain-Models:InternVL2-8B-Pretrain', 'OpenGVLab/InternVL2-Pretrain-Models:InternVL2-8B-Pretrain'), Model('OpenGVLab/InternVL2-Pretrain-Models:InternVL2-26B-Pretrain', 'OpenGVLab/InternVL2-Pretrain-Models:InternVL2-26B-Pretrain'), Model('OpenGVLab/InternVL2-Pretrain-Models:InternVL2-40B-Pretrain', 'OpenGVLab/InternVL2-Pretrain-Models:InternVL2-40B-Pretrain'), Model('OpenGVLab/InternVL2-Pretrain-Models:InternVL2-Llama3-76B-Pretrain', 'OpenGVLab/InternVL2-Pretrain-Models:InternVL2-Llama3-76B-Pretrain'), ], template=TemplateType.internvl2, requires=['transformers>=4.36', 'timm'], tags=['vision', 'video'], ), ModelGroup( [ Model('OpenGVLab/InternVL2-4B', 'OpenGVLab/InternVL2-4B'), ], template=TemplateType.internvl2_phi3, requires=['transformers>=4.36,<4.42', 'timm'], tags=['vision', 'video'], ), ModelGroup( [ Model('OpenGVLab/InternVL2_5-1B', 'OpenGVLab/InternVL2_5-1B'), Model('OpenGVLab/InternVL2_5-2B', 'OpenGVLab/InternVL2_5-2B'), Model('OpenGVLab/InternVL2_5-4B', 'OpenGVLab/InternVL2_5-4B'), Model('OpenGVLab/InternVL2_5-8B', 'OpenGVLab/InternVL2_5-8B'), Model('OpenGVLab/InternVL2_5-26B', 'OpenGVLab/InternVL2_5-26B'), Model('OpenGVLab/InternVL2_5-38B', 'OpenGVLab/InternVL2_5-38B'), Model('OpenGVLab/InternVL2_5-78B', 'OpenGVLab/InternVL2_5-78B'), # quant (infer use lmdeploy) Model('OpenGVLab/InternVL2_5-4B-AWQ', 'OpenGVLab/InternVL2_5-4B-AWQ'), Model('OpenGVLab/InternVL2_5-8B-AWQ', 'OpenGVLab/InternVL2_5-8B-AWQ'), Model('OpenGVLab/InternVL2_5-26B-AWQ', 'OpenGVLab/InternVL2_5-26B-AWQ'), Model('OpenGVLab/InternVL2_5-38B-AWQ', 'OpenGVLab/InternVL2_5-38B-AWQ'), Model('OpenGVLab/InternVL2_5-78B-AWQ', 'OpenGVLab/InternVL2_5-78B-AWQ'), # mpo Model('OpenGVLab/InternVL2_5-1B-MPO', 'OpenGVLab/InternVL2_5-1B-MPO'), Model('OpenGVLab/InternVL2_5-2B-MPO', 'OpenGVLab/InternVL2_5-2B-MPO'), Model('OpenGVLab/InternVL2_5-4B-MPO', 'OpenGVLab/InternVL2_5-4B-MPO'), Model('OpenGVLab/InternVL2_5-8B-MPO', 'OpenGVLab/InternVL2_5-8B-MPO'), Model('OpenGVLab/InternVL2_5-26B-MPO', 'OpenGVLab/InternVL2_5-26B-MPO'), Model('OpenGVLab/InternVL2_5-38B-MPO', 'OpenGVLab/InternVL2_5-38B-MPO'), Model('OpenGVLab/InternVL2_5-78B-MPO', 'OpenGVLab/InternVL2_5-78B-MPO'), ], template=TemplateType.internvl2_5, requires=['transformers>=4.36', 'timm'], tags=['vision', 'video'], ), ModelGroup( [ # pretrain Model('OpenGVLab/InternVL3-1B-Pretrained', 'OpenGVLab/InternVL3-1B-Pretrained'), Model('OpenGVLab/InternVL3-2B-Pretrained', 'OpenGVLab/InternVL3-2B-Pretrained'), Model('OpenGVLab/InternVL3-8B-Pretrained', 'OpenGVLab/InternVL3-8B-Pretrained'), Model('OpenGVLab/InternVL3-9B-Pretrained', 'OpenGVLab/InternVL3-9B-Pretrained'), Model('OpenGVLab/InternVL3-14B-Pretrained', 'OpenGVLab/InternVL3-14B-Pretrained'), Model('OpenGVLab/InternVL3-38B-Pretrained', 'OpenGVLab/InternVL3-38B-Pretrained'), Model('OpenGVLab/InternVL3-78B-Pretrained', 'OpenGVLab/InternVL3-78B-Pretrained'), # instruct Model('OpenGVLab/InternVL3-1B-Instruct', 'OpenGVLab/InternVL3-1B-Instruct'), Model('OpenGVLab/InternVL3-2B-Instruct', 'OpenGVLab/InternVL3-2B-Instruct'), Model('OpenGVLab/InternVL3-8B-Instruct', 'OpenGVLab/InternVL3-8B-Instruct'), Model('OpenGVLab/InternVL3-9B-Instruct', 'OpenGVLab/InternVL3-9B-Instruct'), Model('OpenGVLab/InternVL3-14B-Instruct', 'OpenGVLab/InternVL3-14B-Instruct'), Model('OpenGVLab/InternVL3-38B-Instruct', 'OpenGVLab/InternVL3-38B-Instruct'), Model('OpenGVLab/InternVL3-78B-Instruct', 'OpenGVLab/InternVL3-78B-Instruct'), # mpo Model('OpenGVLab/InternVL3-1B', 'OpenGVLab/InternVL3-1B'), Model('OpenGVLab/InternVL3-2B', 'OpenGVLab/InternVL3-2B'), Model('OpenGVLab/InternVL3-8B', 'OpenGVLab/InternVL3-8B'), Model('OpenGVLab/InternVL3-9B', 'OpenGVLab/InternVL3-9B'), Model('OpenGVLab/InternVL3-14B', 'OpenGVLab/InternVL3-14B'), Model('OpenGVLab/InternVL3-38B', 'OpenGVLab/InternVL3-38B'), Model('OpenGVLab/InternVL3-78B', 'OpenGVLab/InternVL3-78B'), # awq (Use lmdeploy for inference.) Model('OpenGVLab/InternVL3-1B-AWQ', 'OpenGVLab/InternVL3-1B-AWQ'), Model('OpenGVLab/InternVL3-2B-AWQ', 'OpenGVLab/InternVL3-2B-AWQ'), Model('OpenGVLab/InternVL3-8B-AWQ', 'OpenGVLab/InternVL3-8B-AWQ'), Model('OpenGVLab/InternVL3-9B-AWQ', 'OpenGVLab/InternVL3-9B-AWQ'), Model('OpenGVLab/InternVL3-14B-AWQ', 'OpenGVLab/InternVL3-14B-AWQ'), Model('OpenGVLab/InternVL3-38B-AWQ', 'OpenGVLab/InternVL3-38B-AWQ'), Model('OpenGVLab/InternVL3-78B-AWQ', 'OpenGVLab/InternVL3-78B-AWQ'), # SenseNova-SI Model('SenseNova/SenseNova-SI-InternVL3-2B', 'sensenova/SenseNova-SI-InternVL3-2B'), Model('SenseNova/SenseNova-SI-InternVL3-8B', 'sensenova/SenseNova-SI-InternVL3-8B'), Model('SenseNova/SenseNova-SI-1.1-InternVL3-2B', 'sensenova/SenseNova-SI-1.1-InternVL3-2B'), Model('SenseNova/SenseNova-SI-1.1-InternVL3-8B', 'sensenova/SenseNova-SI-1.1-InternVL3-8B'), ], template=TemplateType.internvl2_5, requires=['transformers>=4.37.2', 'timm'], tags=['vision', 'video'], ), ModelGroup( [ # pretrain Model('OpenGVLab/InternVL3_5-1B-Pretrained', 'OpenGVLab/InternVL3_5-1B-Pretrained'), Model('OpenGVLab/InternVL3_5-2B-Pretrained', 'OpenGVLab/InternVL3_5-2B-Pretrained'), Model('OpenGVLab/InternVL3_5-4B-Pretrained', 'OpenGVLab/InternVL3_5-4B-Pretrained'), Model('OpenGVLab/InternVL3_5-8B-Pretrained', 'OpenGVLab/InternVL3_5-8B-Pretrained'), Model('OpenGVLab/InternVL3_5-14B-Pretrained', 'OpenGVLab/InternVL3_5-14B-Pretrained'), Model('OpenGVLab/InternVL3_5-38B-Pretrained', 'OpenGVLab/InternVL3_5-38B-Pretrained'), Model('OpenGVLab/InternVL3_5-30B-A3B-Pretrained', 'OpenGVLab/InternVL3_5-30B-A3B-Pretrained'), Model('OpenGVLab/InternVL3_5-241B-A28B-Pretrained', 'OpenGVLab/InternVL3_5-241B-A28B-Pretrained'), # Instruct Model('OpenGVLab/InternVL3_5-1B-Instruct', 'OpenGVLab/InternVL3_5-1B-Instruct'), Model('OpenGVLab/InternVL3_5-2B-Instruct', 'OpenGVLab/InternVL3_5-2B-Instruct'), Model('OpenGVLab/InternVL3_5-4B-Instruct', 'OpenGVLab/InternVL3_5-4B-Instruct'), Model('OpenGVLab/InternVL3_5-8B-Instruct', 'OpenGVLab/InternVL3_5-8B-Instruct'), Model('OpenGVLab/InternVL3_5-14B-Instruct', 'OpenGVLab/InternVL3_5-14B-Instruct'), Model('OpenGVLab/InternVL3_5-38B-Instruct', 'OpenGVLab/InternVL3_5-38B-Instruct'), Model('OpenGVLab/InternVL3_5-30B-A3B-Instruct', 'OpenGVLab/InternVL3_5-30B-A3B-Instruct'), Model('OpenGVLab/InternVL3_5-241B-A28B-Instruct', 'OpenGVLab/InternVL3_5-241B-A28B-Instruct'), # MPO Model('OpenGVLab/InternVL3_5-1B-MPO', 'OpenGVLab/InternVL3_5-1B-MPO'), Model('OpenGVLab/InternVL3_5-2B-MPO', 'OpenGVLab/InternVL3_5-2B-MPO'), Model('OpenGVLab/InternVL3_5-4B-MPO', 'OpenGVLab/InternVL3_5-4B-MPO'), Model('OpenGVLab/InternVL3_5-8B-MPO', 'OpenGVLab/InternVL3_5-8B-MPO'), Model('OpenGVLab/InternVL3_5-14B-MPO', 'OpenGVLab/InternVL3_5-14B-MPO'), Model('OpenGVLab/InternVL3_5-38B-MPO', 'OpenGVLab/InternVL3_5-38B-MPO'), Model('OpenGVLab/InternVL3_5-30B-A3B-MPO', 'OpenGVLab/InternVL3_5-30B-A3B-MPO'), Model('OpenGVLab/InternVL3_5-241B-A28B-MPO', 'OpenGVLab/InternVL3_5-241B-A28B-MPO'), # Model('OpenGVLab/InternVL3_5-1B', 'OpenGVLab/InternVL3_5-1B'), Model('OpenGVLab/InternVL3_5-2B', 'OpenGVLab/InternVL3_5-2B'), Model('OpenGVLab/InternVL3_5-4B', 'OpenGVLab/InternVL3_5-4B'), Model('OpenGVLab/InternVL3_5-8B', 'OpenGVLab/InternVL3_5-8B'), Model('OpenGVLab/InternVL3_5-14B', 'OpenGVLab/InternVL3_5-14B'), Model('OpenGVLab/InternVL3_5-38B', 'OpenGVLab/InternVL3_5-38B'), Model('OpenGVLab/InternVL3_5-30B-A3B', 'OpenGVLab/InternVL3_5-30B-A3B'), Model('OpenGVLab/InternVL3_5-241B-A28B', 'OpenGVLab/InternVL3_5-241B-A28B'), ], template=TemplateType.internvl3_5, requires=['transformers>=4.37.2', 'timm'], tags=['vision', 'video'], ), ModelGroup( [ Model('OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview', 'OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview'), ], template=TemplateType.internvl3_5_gpt, requires=['transformers>=4.37.2', 'timm'], tags=['vision', 'video'], ), ], InternVLLoader, architectures=['InternVLChatModel'], model_arch=ModelArch.internvl, )) class Interns1Loader(ModelLoader): def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: from transformers.modeling_utils import PreTrainedModel model = super().get_model(model_dir, *args, **kwargs) if not hasattr(PreTrainedModel, '_old_enable_input_require_grads'): old_enable_input_require_grads = PreTrainedModel.enable_input_require_grads def patched_enable_input_require_grads(self): def make_inputs_require_grads(module, input, output): if isinstance(output, tuple): output[0].requires_grad_(True) else: output.requires_grad_(True) self._require_grads_hook = self.get_input_embeddings().register_forward_hook(make_inputs_require_grads) PreTrainedModel.enable_input_require_grads = patched_enable_input_require_grads PreTrainedModel._old_enable_input_require_grads = old_enable_input_require_grads return model class InternVLHfLoader(Interns1Loader): def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: from transformers import AutoModelForImageTextToText self.auto_model_cls = self.auto_model_cls or AutoModelForImageTextToText return super().get_model(model_dir, *args, **kwargs) register_model( ModelMeta( MLLMModelType.internvl, [ ModelGroup([ Model('OpenGVLab/InternVL3-1B-hf', 'OpenGVLab/InternVL3-1B-hf'), Model('OpenGVLab/InternVL3-2B-hf', 'OpenGVLab/InternVL3-2B-hf'), Model('OpenGVLab/InternVL3-8B-hf', 'OpenGVLab/InternVL3-8B-hf'), Model('OpenGVLab/InternVL3-9B-hf', 'OpenGVLab/InternVL3-9B-hf'), Model('OpenGVLab/InternVL3-14B-hf', 'OpenGVLab/InternVL3-14B-hf'), Model('OpenGVLab/InternVL3-38B-hf', 'OpenGVLab/InternVL3-38B-hf'), Model('OpenGVLab/InternVL3-78B-hf', 'OpenGVLab/InternVL3-78B-hf'), ], template=TemplateType.internvl_hf, requires=['transformers>=4.52.1', 'timm']), ModelGroup([ Model('OpenGVLab/InternVL3_5-1B-HF', 'OpenGVLab/InternVL3_5-1B-HF'), Model('OpenGVLab/InternVL3_5-2B-HF', 'OpenGVLab/InternVL3_5-2B-HF'), Model('OpenGVLab/InternVL3_5-4B-HF', 'OpenGVLab/InternVL3_5-4B-HF'), Model('OpenGVLab/InternVL3_5-8B-HF', 'OpenGVLab/InternVL3_5-8B-HF'), Model('OpenGVLab/InternVL3_5-14B-HF', 'OpenGVLab/InternVL3_5-14B-HF'), Model('OpenGVLab/InternVL3_5-38B-HF', 'OpenGVLab/InternVL3_5-38B-HF'), Model('OpenGVLab/InternVL3_5-30B-A3B-HF', 'OpenGVLab/InternVL3_5-30B-A3B-HF'), Model('OpenGVLab/InternVL3_5-241B-A28B-HF', 'OpenGVLab/InternVL3_5-241B-A28B-HF'), ], template=TemplateType.internvl_hf, requires=['transformers>=4.52.1', 'timm']), ModelGroup([ Model('OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF', 'OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF'), ], template=TemplateType.internvl_hf, requires=['transformers>=4.55.0', 'timm']), ], InternVLHfLoader, architectures=['InternVLForConditionalGeneration'], model_arch=ModelArch.llava_hf, tags=['vision', 'video'], )) register_model( ModelMeta( MLLMModelType.interns1, [ ModelGroup([ Model('Shanghai_AI_Laboratory/Intern-S1-mini', 'internlm/Intern-S1-mini'), Model('Shanghai_AI_Laboratory/Intern-S1', 'internlm/Intern-S1'), Model('Shanghai_AI_Laboratory/Intern-S1-mini-FP8', 'internlm/Intern-S1-mini-FP8'), Model('Shanghai_AI_Laboratory/Intern-S1-FP8', 'internlm/Intern-S1-FP8'), ]), ], Interns1Loader, template=TemplateType.interns1, architectures=['InternS1ForConditionalGeneration'], model_arch=ModelArch.interns1, requires=['transformers>=4.55.2,<4.56'], tags=['vision', 'video'], )) class Xcomposer2Loader(ModelLoader): version = 'v2' def get_model(self, model_dir: str, *args, **kwargs) -> PreTrainedModel: if self.version == 'v2-4khd': from transformers import CLIPVisionModel def load_model(self): self.vision_tower_name = safe_snapshot_download( 'AI-ModelScope/clip-vit-large-patch14-336', check_local=True) self.vision_tower = CLIPVisionModel.from_pretrained(self.vision_tower_name) self.vision_tower.requires_grad_(False) self.is_loaded = True CLIPVisionTower = get_class_from_dynamic_module('build_mlp.CLIPVisionTower', model_dir) CLIPVisionTower.load_model = load_model model = super().get_model(model_dir, *args, **kwargs) model.vit.vision_tower.gradient_checkpointing_enable() if self.version == 'v2': # fix AttributeError: no attribute 'attention_dropout' model.model.layers[0].attention.__class__.attention_dropout = 0. if self.version == 'v2.5': patch_output_to_input_device(model.vit) patch_output_to_input_device(model.vision_proj) register_model( ModelMeta( MLLMModelType.xcomposer2, [ ModelGroup([ Model('Shanghai_AI_Laboratory/internlm-xcomposer2-7b', 'internlm/internlm-xcomposer2-7b'), ], ), ], Xcomposer2Loader, template=TemplateType.xcomposer2, architectures=['InternLMXComposer2ForCausalLM'], model_arch=ModelArch.xcomposer, tags=['vision'], )) class Xcomposer2_4khdLoader(Xcomposer2Loader): version = 'v2-4khd' register_model( ModelMeta( MLLMModelType.xcomposer2_4khd, [ ModelGroup([ Model('Shanghai_AI_Laboratory/internlm-xcomposer2-4khd-7b', 'internlm/internlm-xcomposer2-4khd-7b'), ], ), ], Xcomposer2_4khdLoader, template=TemplateType.xcomposer2, architectures=['InternLM2ForCausalLM', 'InternLMXComposer2ForCausalLM'], model_arch=ModelArch.xcomposer, tags=['vision'], )) class Xcomposer2_5Loader(Xcomposer2Loader): version = 'v2.5' register_model( ModelMeta( MLLMModelType.xcomposer2_5, [ ModelGroup([ Model('Shanghai_AI_Laboratory/internlm-xcomposer2d5-7b', 'internlm/internlm-xcomposer2d5-7b'), Model('Shanghai_AI_Laboratory/internlm-xcomposer2d5-ol-7b:base', 'internlm/internlm-xcomposer2d5-ol-7b:base') ]), ], Xcomposer2_5Loader, template=TemplateType.xcomposer2_5, architectures=['InternLMXComposer2ForCausalLM'], model_arch=ModelArch.xcomposer, tags=['vision'], requires=['decord'], # target_modules: attention.wqkv attention.wo feed_forward.w1 feed_forward.w2 feed_forward.w3 )) register_model( ModelMeta( MLLMModelType.xcomposer2_5_ol_audio, [ ModelGroup([ Model('Shanghai_AI_Laboratory/internlm-xcomposer2d5-ol-7b:audio', 'internlm/internlm-xcomposer2d5-ol-7b:audio'), ]), ], Qwen2AudioLoader, template=TemplateType.qwen2_audio, requires=['transformers>=4.45'], architectures=['Qwen2AudioForConditionalGeneration'], model_arch=ModelArch.qwen2_audio, tags=['audio'], )) register_model( ModelMeta( RMModelType.internlm2_reward, [ ModelGroup([ Model('Shanghai_AI_Laboratory/internlm2-1_8b-reward', 'internlm/internlm2-1_8b-reward'), Model('Shanghai_AI_Laboratory/internlm2-7b-reward', 'internlm/internlm2-7b-reward'), Model('Shanghai_AI_Laboratory/internlm2-20b-reward', 'internlm/internlm2-20b-reward'), ]), ], RewardModelLoader, template=TemplateType.internlm2_reward, is_reward=True, requires=['transformers>=4.38'], architectures=['InternLM2ForRewardModel'], ))