若需使用QLoRA微调LLaMA 2，请遵循以下步骤进行操作：安装所需环境：```bash pip install transformers datasets peft accelerate bitsandbytes safetensors ```导入必要的库：```python import os, sys import torch import datasets from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig,...
model=model, torch_dtype=torch.float16, device_map="auto",)sequences = pipeline('I liked "Breaking Bad" and "Band of Brothers". Do you have any recommendations of other shows
在训练完成后，我们想要运行和测试模型。我们会使用 peft 和 transformers 将 LoRA 适配器加载到模型中。if use_flash_attention:# 停止 flash attentionfrom utils.llama_patch import unplace_flash_attn_with_attn unplace_flash_attn_with_attn()import torchfrom peft import AutoPeftModelForCausalLMfrom tra...
load_in_8bit=False, # 加载8位 torch_dtype=torch.float16, # float16 device_map={"": "cpu"}, # cpu ) # 步骤2:遍历LoRA模型 for lora_index, lora_model_path in enumerate(lora_model_paths): # 步骤3:根据base model和lora model来初始化PEFT模型 lora_model = PeftModel.from_pretrained( b...
from peft import AutoPeftModelForCausalLMmodel = AutoPeftModelForCausalLM.from_pretrained( args.output_dir, low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map=device_map, )# Merge LoRA and base modelmerged_model = model.merge_and_unload()#...
model low_cpu_mem_usage=True, torch_dtype=torch.float16, load_in_4bit=True, is_trainable=True,)model_ref = AutoPeftModelForCausalLM.from_pretrained( script_args.model_name_or_path, # same model as the main one low_cpu_mem_usage=True, torch_dtype=torch.float16, ...
compute\_dtype = getattr\(torch, bnb\_4bit\_compute\_dtype\) \# BitsAndBytesConfig int-4 config bnb\_config = BitsAndBytesConfig\( load\_in\_4bit=use\_4bit, bnb\_4bit\_use\_double\_quant=use\_double\_nested\_quant, bnb\_4bit\_quant\_type=bnb\_4bit\_quant\_type, ...
pretrained_model_name_or_path =r'...'model = AutoModelForCausalLM.from_pretrained(Path(f'{pretrained_model_name_or_path}'), device_map='auto', torch_dtype=torch.float16, load_in_8bit=True)#加载模型model = model.eval()#切换到eval模式tokenizer = AutoTokenizer.from_pretrained(Path(f'{pr...
compute_dtype = getattr(torch, bnb_4bit_compute_dtype) # BitsAndBytesConfig int-4 config bnb_config = BitsAndBytesConfig( load_in_4bit=use_4bit, bnb_4bit_use_double_quant=use_double_nested_quant, bnb_4bit_quant_type=bnb_4bit_quant_type, ...
torch_dtype=torch.float16,device_map="auto", ) 运行pipeline 任务 在定义了 pipeline 任务后,还需要提供一些文本提示,作为 pipeline 任务运行时生成响应(序列)的输入。下面示例中的 pipeline 任务将 do_sample 设置为 True,这样就可以指定解码策略,从整个词汇表的概率分布中选择下一个 token。本文示例脚本使用的...