adapter_name='default'.ipdb>adapter_name'default'ipdb>prompt_encoderPrefixEncoder((embedding):Embedding(30,49152)) 增加30个虚拟tokens: >/usr/local/lib/python3.8/dist-packages/peft/peft_model.py(217)_setup_prompt_encoder()216self.prompt_tokens[adapter_name]=torch.arange(-->217config.num_virtual...
add_adapter(adapter_name, peft_config) 106 ipdb> adapter_name 'default' 进来之后,是设置prompt encoder: > /usr/local/lib/python3.8/dist-packages/peft/peft_model.py(340)add_adapter() 339 if isinstance(peft_config, PromptLearningConfig): --> 340 self._setup_prompt_encoder(adapter_name) ...
peft_config, PromptLearningConfig ):returnPeftModel(model, peft_config, adapter_name=adapter_name)ifisinstance(peft_config, PromptLearningConfig): peft_config = _prepare_prompt_learning_config(peft_config, model_config)returnMODEL_TYPE_TO_PEFT_MODEL_MAPPING[peft_config.task_type](model, peft_config...
"value"]) model = AutoModelForSequenceClassification.from_pretrained(model_name_or_path, ...
model_id, adapter_name, is_trainable, torch_dtype=torch.bfloat16 ) def test_peft_whisper(self): model_id = "peft-internal-testing/tiny_WhisperForConditionalGeneration-lora" model = AutoPeftModel.from_pretrained(model_id) self.assertTrue(isinstance(model, PeftModel)) with tempfile.TemporaryDirec...
adapter_name] = lora_alpha / math.sqrt(r) else: self.scaling[adapter_name] = lora_...
adapter_name: str = "default", is_trainable: bool = False, config: Optional[PeftConfig] = None, **kwargs, ): r""" A wrapper around all the preprocessing steps a user needs to perform in order to load a PEFT model. The kwargs are passed along to `PeftConfig` that automatically tak...
这只会保存经过训练的增量 PEFT 权重。例如,您可以在此处的twitter_complaintsraft 数据集上找到使用 LoRA 调整的bigscience/T0_3B: smangrul/twitter_complaints_bigscience_T0_3B_LORA_SEQ_2_SEQ_LM。请注意,它只包含 2 个文件: adapter_config.json 和 adapter_model.bin,后者只有 19MB。
当前,已经出现了很多lora作为adapter的微调模型,如Alpaca LoRA,Chinese-LLaMA-Alpaca等,其在公开时会注明:中文LLaMA/Alpaca LoRA模型无法单独使用,需要搭配原版LLaMA模型,发布的是LoRA权重,可以理解为原LLaMA模型上的一个“补丁”,两者进行合并即可获得完整版权重。
final_model_path, safe_serialization=True, max_shard_size="5GB") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--final_model_path", type=str) parser.add_argument("--adapter_config_path", type=str) parser.add_argument("--base_mode...