"`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format( self.__class__.__name__, self.__class__.__name__ ) ) # Save config in model self.config = config …… @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): config = kwargs....
field_names.add(field.attname) non_model_fields = update_fields.difference(field_names) ifnon_model_fields: raiseValueError("The following fields do not exist in this " "model or are m2m fields: %s" %', '.join(non_model_fields)) # If saving to the same database, and this model is...
dtype=args.dtype,stage=args.zero_stage,enable_tensorboard=args.enable_tensorboard,tb_path=args.tensorboard_path,tb_name="step2_model")ds_config['train_micro_batch_size_per_gpu']=args.per_device_train_batch_sizeds_config['train_batch_size']=args.per_device_train_batch_size*torch....
Using GPU in script?: Using distributed or parallel set-up in script?: Others 我在这里打印了下,args为None,好像参数没传进来? LLaMA-Factory/src/llmtuner/train/tuner.py Line 27 in 8e09e20 model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)...
} public ModelArgs siteId(@Nullable Long siteId) { if (siteId != null) { queryMap.put("EQ_siteId_Int", siteId); } return this; } 上述2处有关 siteId的类型值,应该设置为 Long , 目前设置为Int时,会有报错。 查了一下,还有其他一些java文件,有 EQ_siteId_Int 的配置,也需要修改。
vllm_engine_args:选填,vllm engine加载参数,用于更改vllm engine加载的配置,详见https://github.com/vllm-project/vllm/blob/main/vllm/engine/arg_utils.py。 其中"model", "served_model_name", "tokenizer", "download_dir", "trust_remote_code", "tensor_parallel_size"这些参数固定为平台默认配置关于...
(name='nlp-base-trainer', default_args=kwargs) # 开始训练 print('===') print('pre-trained model loaded, training started:') print('===') trainer.train() print('===')
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, trust_remote_code=True) File "/root/miniconda3/lib/python3.8/site-packages/transformers/models/auto/tokenization_auto.py", line 679, in from_pretrained return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *...
name= models.CharField(max_length=32)def__str__(self):returnself.nameclassTags(models.Model): name= models.CharField(max_length=32)def__str__(self):returnself.nameclassNews(models.Model): title= models.CharField(max_length=32) type= models.ForeignKey(User_Type,on_delete=models.CASCADE,blan...
path.join(temp_dir_name, 'saved_model') model.save(save_path, include_optimizer=False, save_format='tf') converter = lite.TFLiteConverter.from_saved_model(save_path) else: converter = lite.TFLiteConverter.from_keras_model(model) if quantization_config: converter = quantization_config....