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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from transformers.generation.utils import GenerationConfig |
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from peft import PeftModel, PeftConfig |
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import json |
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import csv |
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model_path = "/root/lanyun-tmp/ZhipuAI/chatglm3-6b" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_path, use_fast=False, trust_remote_code=True, |
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) |
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filename = '/root/lanyun-tmp/Dataset/val_triviaqa.csv' |
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data = [] |
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with open(filename, newline='',encoding="utf-8") as csvfile: |
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reader = csv.DictReader(csvfile) |
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files = 'TriviaQA_ChatGLM3_Nlora.csv' |
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with open(files, 'w', newline='',encoding='utf-8') as csvfile: |
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writer = csv.writer(csvfile) |
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for index,row in enumerate(reader): |
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if index<=1000: |
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context = row['context'] |
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question = row['question'] |
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messages = str([{'role': 'system', 'content': 'Don t output "[" !!!, As a reading comprehension expert, you will receive context and question. Please understand the given Context first and then output the answer of the question based on the Context'}, {'role': 'user', 'content': '{\'context\': \'[DOC] [TLE] richard marx had an 80s No 1 hit with Hold On To The Nights? \', \'question\': \'Who had an 80s No 1 hit with Hold On To The Nights?\'}'}, {'role': 'assistant', 'content': "richard marx"}]) |
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response = model.chat(tokenizer, messages) |
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answer = response[0][0] |
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print(answer) |
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writer.writerow(answer) |
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