self.config = config …… @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): config = kwargs.pop("config", None) state_dict = kwargs.pop("state_dict", None) cache_dir =
model = cls(config, *model_args, **model_kwargs) 然后加载预训练的参数: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 if state_dict is None and not from_tf: try: state_dict = torch.load(resolved_archive_file, map_location="cpu") except Exception: raise OSError( f"Unable to load...
model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **dict(model_args, **kwargs)) return model model_args把我这个模型的独特参数和传进来的**kwargs一起打包成参数字典,传给_create_vision_transformer函数 _create_vision_transformer函数内部发生了什么 函数之中,会再调用bu...
local_files_only =True# Load config if we don't provide a configurationifnotisinstance(config, PretrainedConfig): config_path = configifconfigisnotNoneelsepretrained_model_name_or_path config, model_kwargs = cls.config_class.from_pretrained( config_path, *model_args, cache_dir=cache_dir, ret...
return func(*args, **kwargs) File "/home/lAnimateDiff/scripts/animate.py", line 242, in main unet = UNet3DConditionModel.from_pretrained_2d(args.pretrained_model_path, subfolder="unet", unet_additional_kwargs=OmegaConf.to_container(inference_config.unet_additional_kwargs)).cuda() ...
**model_kwargs, ): """Load Pytorch pretrained weights and return the loaded model.""" model = cls(**model_kwargs) if os.path.isdir(model_id): print("Loading weights from local directory") model_file = os.path.join(model_id, PYTORCH_WEIGHTS_NAME) model_file = os.path.join(model_...
(bert.config)self.c_head.apply(self.lm._init_weights)self.cross_entropy = nn.CrossEntropyLoss()self.model_args = model_argsdef gradient_checkpointing_enable(self, **kwargs):self.lm.gradient_checkpointing_enable(**kwargs)def forward(s...
(args.local_rank) # 指定工作目录 tmp_dir = "/tmp" # 配置参数 kwargs = dict( model=model_id, train_dataset=train_dataset, eval_dataset=eval_dataset, work_dir=tmp_dir, launcher='pytorch' # 分布式启动方式 ) # 实例化trainer对象 trainer = build_trainer(default_args=kwargs) # 调用train...
def __init__(self, config, *model_args, **model_kwargs): super().__init__(config, *model_args, **model_kwargs) self.model = BaichuanModel(config) self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False) if hasattr(config, "quantization_config") and isinst...
建议多多的看报错日志，自己循着日志找自己的配置错误。此回答整理自钉群 “魔搭ModelScope开发者联盟群...