70 lines
2.6 KiB
Python
Executable File
70 lines
2.6 KiB
Python
Executable File
import json
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import torch
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from tqdm import tqdm
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from transformers import AutoModelForCausalLM, AutoTokenizer
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def execute_model_prediction(model_path: str, input_file: str, output_file: str):
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# ... existing imports ...
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# 初始化模型和分词器
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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trust_remote_code=True
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).to("cuda", dtype=torch.bfloat16)
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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tokenizer.pad_token = tokenizer.unk_token
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tokenizer.pad_token_id = tokenizer.unk_token_id
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tokenizer.add_eos_token = False
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def generate_responses(prompts: list[str]) -> list[str]:
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"""处理一批文本并生成回复"""
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inputs = tokenizer(prompts, return_tensors="pt", padding=True).to(model.device)
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outputs = model.generate(inputs['input_ids'], max_new_tokens=256, pad_token_id=0)
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return tokenizer.batch_decode(outputs, skip_special_tokens=True)
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# 读取数据
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samples = []
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with open(input_file, 'r') as f:
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for line in f:
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samples.append(json.loads(line))
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BATCH_SIZE = 16
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for batch_start in tqdm(range(0, len(samples), BATCH_SIZE)):
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batch = samples[batch_start:batch_start + BATCH_SIZE]
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# 构建输入提示
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prompts = []
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for sample in batch:
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if 'choices' in sample:
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prompt = f"<用户>{sample['question']}\n{''.join(sample['choices'])}<AI>"
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else:
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prefix = "<用户>" if sample['question'].strip().startswith("Write") else ""
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suffix = "<AI> def" if sample['question'].strip().startswith("Write") else "\n "
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prompt = f"{prefix}{sample['question']}{suffix}"
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prompts.append(prompt)
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# 生成回复并处理
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responses = generate_responses(prompts)
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processed_outputs = []
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for idx, response in enumerate(responses):
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output = response.split('<AI>')[1].strip()
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processed_outputs.append(output)
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# 更新结果
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for idx, output in enumerate(processed_outputs):
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samples[batch_start + idx]['raw_outputs'] = [output]
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# 保存结果
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with open(output_file, 'w', encoding='utf-8') as f:
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for sample in samples:
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json.dump(sample, f, ensure_ascii=False)
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f.write('\n')
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if __name__ == '__main__':
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MODEL_PATH = 'checkpoints/js/checkpoint-7200'
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INPUT_FILE = 'test_set/军事知识问答_round4.jsonl'
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OUTPUT_FILE = 'test_set/军事知识问答_round4_output.jsonl'
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execute_model_prediction(MODEL_PATH, INPUT_FILE, OUTPUT_FILE) |