The PretrainedConfig class has a method for loading configs from_pretrained(). As the documentation states, the input can be either a string or a path to a config file: pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`): This can be either: - a string, the `model ...
config, model_kwargs = cls.config_class.from_pretrained( File "E:\stable-diffusion-webui\venv\lib\site-packages\transformers\models\clip\configuration_clip.py", line 126, in from_pretrained config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) ...
然后是跑cls._get_tokenizer_class_from_config(pretrained_model_name_or_path, resolved_vocab_file, use_fast),找到tokenizer_config.json配置,里面指明了tokenizer_class叫做QWenVLTokenizer,此时,如果是AutoTokenizer的话,就会跑paddlenlp中AutoTokenizer的_get_tokenizer_class_from_config,不过qwen这里是 AutoTokenize...
其中,model_type作为标识,from_pretrained方法通过这个model_type(pretrained_model_name_or_path)来加载对应的模型,这里定义为 "baichuan"。 keys_to_ignore_at_inference 表示在进行推理时可以忽略的一些键值。 这两个类内属性会覆盖相应的 PretrainedConfig 的 class 属性 PretrainedConfig有如下几个类内属性,可以由...
class MyClass: def __init__(self, config): self.config = config # 其他初始化代码 # 正确的实例化方式 my_instance = MyClass(config={'key': 'value'}) # 错误的实例化方式,缺少 config 参数 # my_instance = MyClass() # 这将引发 TypeError: __init__() missing 1 required posit...
[0.1, 0.1, 0.2, 0.2]), reg_class_agnostic=True, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), dict( type='Shared2FCBBoxHead', in_channels=256, fc_out_channels=1024, roi_feat_size=...
具体来说,错误信息 ValueError: Unrecognized configuration class <class 'transformers.models.llava.configuration_llava.LlavaConfig'> for this kind of AutoModel: AutoModelForCausalLM 指出LlavaConfig 配置类不被 AutoModelForCausalLM 所识别。这可能是因为以下几个原因: 配置类名称错误:确保你使用的配...
from torch.optim.lr_scheduler import LambdaLR class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() # 定义模型结构 self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1) self.relu = nn.ReLU(inplace=True) ...
fromtypingimportList classLMConfig(PretrainedConfig): model_type="minimind" def__init__( self, dim:int=512, n_layers:int=8, n_heads:int=8, n_kv_heads:int=2, vocab_size:int=6400, hidden_dim:int=None, multiple_of:int=64,
"""def__init__(self,backbone,neck=None,keypoint_head=None,train_cfg=None,test_cfg=None,pretrained=None,loss_pose=None):... DeepposeRegressionHead初始化 #DeepposeRegressionHeadclassDeepposeRegressionHead(nn.Module):"""Deeppose regression head with fully connected layers. ...