90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
# pip install bitsandbytes
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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from transformers import BitsAndBytesConfig
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from api.doubao_client import DoubaoClient
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from core.ocr_service import OCRService
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import torch
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import threading
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_doubao_client = DoubaoClient()
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_ocr_service = OCRService(_doubao_client)
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image_path = r'c:\math1.jpg'
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type="nf4"
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)
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ocr_result = _ocr_service.get_ocr(image_path)
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# model_name = "netease-youdao/Confucius3-Math"
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model_name = r"D:\Confucius3-Math\netease-youdao\Confucius3-Math"
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SYSTEM_PROMPT_TEMPLATE="""
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# 数学解题助手规则（必须严格遵守）
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1. 【核心要求】必须使用初中生知识范围内可以理解的方法解题，绝对禁止使用向量、微积分、复数等高中及以上数学知识。
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2. 如果是动点问题要注意可能有多个解。
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3. 使用简单易懂的语言，将复杂问题分解成简单的步骤。
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4. 引导学生思考，而不是直接给出答案。
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# 角色信息
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- language: 中文
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- description: 数学解题助手是一个专门为初中生提供数学问题解答的角色。
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- expertise: 初中数学解题、数学教育
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- target_audience: 初中生
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"""
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USER_PROMPT_TEMPLATE = """{question}"""
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# question = "1+1=?"
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question = ocr_result
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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# torch_dtype="auto",
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quantization_config=bnb_config,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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messages = [
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{'role': 'system', 'content': SYSTEM_PROMPT_TEMPLATE},
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{'role': 'user', 'content': USER_PROMPT_TEMPLATE.format(question=question)},
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# 创建流式输出器
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streamer = TextIteratorStreamer(tokenizer, skip_special_tokens=True, timeout=10.0)
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# 设置生成参数，添加streamer
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generation_kwargs = {
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**model_inputs,
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"streamer": streamer,
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"max_new_tokens": 32768
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}
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# 创建线程来处理流式输出
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thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
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thread.start()
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# 流式输出结果
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print("流式输出开始：")
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for chunk in streamer:
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if chunk:
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print(chunk, end="", flush=True)
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thread.join()
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print("\n流式输出结束") |