34 lines
1.7 KiB
Python
34 lines
1.7 KiB
Python
from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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from config import Config,Default,Dir
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def get_model_train_dataset_json_file():
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model_train_json_file = Dir.MODEL_DATASET_DIR + "/" + Default.TRAIN_JSONL_NEW_FILE
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model_test_json_file = Dir.MODEL_DATASET_DIR + "/" + Default.TEST_JSONL_NEW_FILE
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return model_train_json_file, model_test_json_file
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def predict(messages, model, tokenizer):
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device = "cuda"
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=2048)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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# 加载原下载路径的tokenizer和model
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tokenizer = AutoTokenizer.from_pretrained("./Qwen3-0.6B/checkpoint-1084", use_fast=False, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("./Qwen3-0.6B/checkpoint-1084", device_map="auto", torch_dtype=torch.bfloat16)
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test_texts = {
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'instruction': "你是一个医学专家，你需要根据用户的问题，给出带有思考的回答。",
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'input': "医生，我在研究内耳的前庭部分时，发现了一些特殊的结构，比如前庭嵴。请问前庭内还有哪些特殊的结构，它们的作用是什么？"
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}
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instruction = test_texts['instruction']
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input_value = test_texts['input']
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messages = [
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{"role": "system", "content": f"{instruction}"},
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{"role": "user", "content": f"{input_value}"}
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]
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response = predict(messages, model, tokenizer)
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print(response) |