num_virtual_tokens=20)model=AutoModelForSequenceClassification.from_pretrained(model_name_or_path,retu...
base_model = AutoModelForSequenceClassification.from_pretrained( base_model_path, num_labels=1, load_in_8bit=False, torch_dtype=torch.float32,trust_remote_code=True, device_map="auto", ) else: logger.info("Loading LoRA for causal language model") base_model = model_class.from_pretrained( ...
创建PEFT方法对应的配置 peft_config = LoraConfig( task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1)通过调用 get_peft_model 包装基础 🤗 Transformer 模型 model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)+ model = get_peft_...
model = openmind.AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=training_args.cache_dir, trust_remote_code=True ) ### 请在此处添加代码 ### 代码为LoRA、AdaLoRA或IA3相关配置代码 1. 2. 3. 4. 5. 6. 7. 8. ● LoRA LoRA是一种用于高效训练大型语言模型的...
部署在项目框架中,请使用 export_model.py 将微调后的权重合并到 ChatGLM-6B 模型中并导出完整模型。 python src/export_model.py \ --checkpoint_dir cognition \ --output_dir path_to_save_model 通过类似如下代码的调用方式,您可以在任何项目中独立部署微调后的模型。
model_name_or_path="yahma/llama-7b-hf"model=transformers.AutoModelForCausalLM.from_pretrained(model_name_or_path,torch_dtype=torch.bfloat16,device_map="cuda")# Wrap the modelwithrank-1constant reFT reft_config=ReftConfig(representations={"layer":19,"component":"block_output","intervention"...
base_model_name_or_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) # model = PeftModel.from_pretrained(model, FLAGS.ckpt_path, is_trainable=False) model = PeftModel.from_pretrained(model, ckpt_path, is_trainable=False) model = model.to(accelerator.device) print...
(task_type=TaskType.SEQ_2_SEQ_LM,inference_mode=False,r=8,lora_alpha=32,lora_dropout=0.1)model=AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)model=get_peft_model(model,peft_config)model.print_trainable_parameters()"trainable params: 2359296 || all params: 1231940608 || trainable...
6b"tokenizer=AutoTokenizer.from_pretrained(model_id,trust_remote_code=True)#model = AutoModel.from_pretrained(model_id, trust_remote_code=True).half().cuda()model=AutoModel.from_pretrained(model_id,trust_remote_code=True,device='cuda')model=model.eval()response,history=model.chat(tokenizer,"...
print(f"Loading base model: {args.base_model_name_or_path}") base_model = AutoModelForCausalLM.from_pretrained( args.base_model_name_or_path, return_dict=True, torch_dtype=torch.float16, trust_remote_code=args.trust_remote_code,