│ /usr/local/lib/python3.7/site-packages/peft/peft_model.py:166 in from_pretrained │ │ │ │ 163 │ │ │ model = cls(model, config, adapter_name) │ │ 164 │ │ else: │ │ 165 │ │ │ model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, ad │ ...
super().init(model, peft_config, adapter_name) File "/home/knut/transformers/lib/python3.9/site-packages/peft/peft_model.py", line 112, in init self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type]( File "/home/knut/transformers/lib/python3.9/site-packages/peft/tuners/lora...
trainer.configs import ModelInfoMapping print(ModelInfoMapping['ERNIE-xx']) 返回示例 Python short_name='xxx' base_model_type='ERNIE-Lite-8K-0922' support_peft_types=[<PeftType.ALL: 'ALL'>, <PeftType.LoRA: 'LoRA'>] common_params_limit=TrainLimit( batch_size_limit=(1, 4), max_seq_...
Configure data input channel to use Amazon FSx for Lustre Choosing an input mode and a storage unit Use attribute-based access control (ABAC) for multi-tenancy training Mapping of training storage paths Uncompressed model output Managing storage paths for different types of instance local storage Sag...
Configure data input channel to use Amazon FSx for Lustre Choosing an input mode and a storage unit Use attribute-based access control (ABAC) for multi-tenancy training Mapping of training storage paths Uncompressed model output Managing storage paths for different types of instance local storage Sag...
peft_config = LoraConfig(task_type="SEQ_CLS", inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.1) peft_model = get_peft_model(model, peft_config) print('PEFT Model') peft_model.print_trainable_parameters() peft_lora_finetuning_trainer = get_trainer(peft_model) ...
这里面又有个model_type_to_module_name方法 这个方法只是做了一下key的转化,之后是从transformers.models里去导入对应的module_name,但是这个name是来自config_mapping的,所以最后这个dict的key实际上是模型的配置文件类的列表,而value则是对应的模型类。所以一方面我们可以直接导入transformer里面已经预定义好的模型mapping...
in init_adapter model = get_peft_model(model, lora_config) File "/home/ma-user/.local/lib/python3.8/site-packages/peft/mapping.py", line 133, in get_peft_model return MODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config, adapter_name=adapter_name) File "/home/...
target_modules=peft_args.target_modules.split(",") if peft_args.target_modules else TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[model.config.model_type] ) model = get_peft_model(model, peft_config) and then we continue to write thecompute_metricsfunction for computing the metric of cho...
self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type]( File "/home/server/anaconda3/envs/pytorch/lib/python3.10/site-packages/peft/tuners/lora.py", line 154, ininit self.add_adapter(adapter_name, self.peft_config[adapter_name]) ...