|
import torch |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
from transformers.generation.utils import GenerationConfig |
|
from peft import PeftModel, PeftConfig |
|
import json |
|
import csv |
|
|
|
lora_path = "/root/lanyun-tmp/output/MiniCPM/checkpoint-9000/" |
|
model_path = '/root/lanyun-tmp/OpenBMB/MiniCPM-2B-sft-fp32' |
|
model = AutoModelForCausalLM.from_pretrained( |
|
model_path, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True |
|
) |
|
model.generation_config = GenerationConfig.from_pretrained(model_path) |
|
tokenizer = AutoTokenizer.from_pretrained( |
|
model_path, use_fast=False, trust_remote_code=True, |
|
) |
|
model = PeftModel.from_pretrained(model, lora_path |
|
) |
|
|
|
|
|
|
|
|
|
|
|
filename = '/root/lanyun-tmp/Dataset/test.jsonl' |
|
data = [] |
|
with open(filename, 'r') as f: |
|
for line in f: |
|
item = json.loads(line) |
|
data.append(item) |
|
|
|
|
|
|
|
files = 'MiniCPM2B-ZH-_answers.csv' |
|
with open(files, 'w', newline='') as csvfile: |
|
writer = csv.writer(csvfile) |
|
|
|
for item in data: |
|
context = item['context'] |
|
question = item['question'] |
|
answer0 = item['answer0'] |
|
answer1 = item['answer1'] |
|
answer2 = item['answer2'] |
|
answer3 = item['answer3'] |
|
|
|
messages = str([ |
|
{"role": "system", "content": "作为阅读理解专家，你将收到上下文，问题和四个选项，请先理解下面给出的上下文，然后根据上下文输出正确选项的标签作为问题的答案}"}, |
|
{"role": "user", "content": str({'context':{context},'question':{question},"answer0":{answer0},"answer1":{answer1},"answer2":{answer2},"answer3":{answer3}})}, |
|
]) |
|
response = model.chat(tokenizer, messages) |
|
|
|
answer = response[0][0] |
|
print(answer) |
|
writer.writerow(answer) |
|
|
|
|
