113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
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# transformers调用本地模型
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from transformers import AutoModelForCausalLM, AutoTokenizer,GenerationConfig
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import torch
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from transformers import __version__ as tf_version
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import torch
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import triton
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import triton.language as tl
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "/data1/lingyun/model/QwenQwen3-32B-AWQ"
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# load the tokenizer and the model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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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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device_map="auto"
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)
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# prepare the model input
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prompt = "你好，2024年有什么大事件发生？"
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messages = [
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{"role": "user", "content": prompt}
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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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enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# conduct text completion
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=32768
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)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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# parsing thinking content
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try:
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# rindex finding 151668 (</think>)
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index = len(output_ids) - output_ids[::-1].index(151668)
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except ValueError:
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index = 0
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
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print("thinking content:", thinking_content)
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print("content:", content)
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# print("Triton 版本:", triton.__version__)
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# if hasattr(tl, 'interleave'):
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# print(" 当前 Triton 版本支持 tl.interleave")
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# else:
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# print("当前 Triton 版本不支持 tl.interleave")
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# # print(dir(triton.language))
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# print(f"Transformers: {tf_version}, PyTorch: {torch.__version__}")
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# model_path = r"/data1/lingyun/model/QwenQwen3-32B-AWQ"
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# def llm_answer(system_content, choose_model=model_path, user_content='', temperature=0.1, max_tokens=128, stream=False):
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# print(1111,system_content,"\n",2222,user_content)
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# tokenizer = AutoTokenizer.from_pretrained(
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# choose_model,
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# trust_remote_code=True # DeepSeek需要此参数
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# )
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# model = AutoModelForCausalLM.from_pretrained(
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# choose_model,
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# device_map="auto", # 自动选择GPU/CPU
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# torch_dtype="auto", # 自动选择精度
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# trust_remote_code=True # 允许执行模型自定义代码
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# )
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# # 3. 修复pad_token问题（关键步骤）
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# if tokenizer.pad_token is None:
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# tokenizer.pad_token = tokenizer.eos_token # 使用eos_token作为pad_token
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# model.config.pad_token_id = tokenizer.pad_token_id # 同步模型配置
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# messages=[{"role": "system", "content": "你好，2024年有什么大事件发生？"},
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# ]
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# inputs_ids = 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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# enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
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# )
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# model_inputs = tokenizer([inputs_ids], return_tensors="pt").to(model.device)
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# # conduct text completion
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# generated_ids = model.generate(
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# model_inputs,
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# max_new_tokens=32768
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# )
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# outputs = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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# i# parsing thinking content
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# try:
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# # rindex finding 151668 (</think>)
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# index = len(output_ids) - output_ids[::-1].index(151668)
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# except ValueError:
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# index = 0
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# thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
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# content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
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# return thinking_content,content
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# if __name__ == '__main__':
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# system_content = "你好，2024年有什么大事件发生？"
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# model_path = r"/data1/lingyun/model/QwenQwen3-32B-AWQ"
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# thinking_content,content = llm_answer(system_content,model_path)
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# print(thinking_content,"\n","\n")
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# print(content)
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