外延能力LangChain集成
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外延能力LangChain集成
外延能力LangChain集成
飞书用户44432023年12月8日修改
本文介绍了Llama2模型集成LangChain框架的具体实现，这样可更方便地基于Llama2开发文档检索、问答机器人和智能体应用等。
1.调用Llama2类
针对LangChain[1]框架封装的Llama2 LLM类见examples/llama2_for_langchain.py，调用代码如下所示：
代码块
from llama2_for_langchain import Llama2
# 这里以调用4bit量化压缩的Llama2-Chinese参数FlagAlpha/Llama2-Chinese-13b-Chat-4bit为例
llm = Llama2(model_name_or_path='FlagAlpha/Llama2-Chinese-13b-Chat-4bit', bit4=True)
while True:
human_input = input("Human: ")
response = llm(human_input)
print(f"Llama2: {response}")
2.Llama2 LLM类具体实现
主要是def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str函数实现。LangChain八股文也不难实现，如下所示：
代码块
from langchain.llms.base import LLM
from typing import Dict, List, Any, Optional
import torch,sys,os
from transformers import AutoTokenizer
class Llama2(LLM): # LLM是一个抽象类，需要实现_call方法
max_token: int = 2048 # 最大token数
temperature: float = 0.1 # 生成温度
top_p: float = 0.95 # 生成概率
tokenizer: Any # 分词器
model: Any # 模型
def __init__(self, model_name_or_path, bit4=True):
super().__init__()
self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path,use_fast=False)
self.tokenizer.pad_token = self.tokenizer.eos_token
if bit4==False: # 32bit
from transformers import AutoModelForCausalLM
self.model = AutoModelForCausalLM.from_pretrained(model_name_or_path,device_map='auto',torch_dtype=torch.float16,load_in_8bit=True)
self.model.eval()
else: # 4bit
from auto_gptq import AutoGPTQForCausalLM
self.model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,low_cpu_mem_usage=True, device="cuda:0", use_triton=False,inject_fused_attention=False,inject_fused_mlp=False)
if torch.__version__ >= "2" and sys.platform != "win32":
self.model = torch.compile(self.model)
@property # @property装饰器将方法转换为属性
def _llm_type(self) -> str:
return "Llama2"
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
print('prompt:',prompt)
input_ids = self.tokenizer(prompt, return_tensors="pt",add_special_tokens=False).input_ids.to('cuda')
generate_input = {
"input_ids":input_ids,
"max_new_tokens":1024,
"do_sample":True,
"top_k":50,
"top_p":self.top_p,
"temperature":self.temperature,
"repetition_penalty":1.2,
"eos_token_id":self.tokenizer.eos_token_id,
"bos_token_id":self.tokenizer.bos_token_id,
"pad_token_id":self.tokenizer.pad_token_id
}
generate_ids = self.model.generate(**generate_input)
generate_ids = [item[len(input_ids[0]):-1] for item in generate_ids]
result_message = self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
return result_message # 返回生成的文本