frameworks, license, tasks

frameworks	license	tasks
Pytorch	Apache License 2.0	text-generation

Github

• 基础模型：Qwen3-1.7B
• 微调后模型：Qwen3-1.7b-Medical-R1-sft
• 数据集：delicate_medical_r1_data
• SwanLab：qwen3-sft-medical
• 微调方式：全参数微调、LoRA微调
• 推理风格：R1推理风格
• 算力要求：
  • 全参数微调：32GB显存
  • LoRA微调：28GB显存
• 图文教程：Qwen3大模型微调入门实战（完整代码）

模型下载

#安装ModelScope
pip install modelscope

#SDK模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('testUser/Qwen3-1.7b-Medical-R1-sft')

Git下载

#Git模型下载
git clone https://www.modelscope.cn/testUser/Qwen3-1.7b-Medical-R1-sft.git

如果您是本模型的贡献者，我们邀请您根据模型贡献文档，及时完善模型卡片内容。

模型推理

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

def predict(messages, model, tokenizer):
    if torch.backends.mps.is_available():
        device = "mps"
    elif torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"

    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    model_inputs = tokenizer([text], return_tensors="pt").to(device)

    generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=2048)
    generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

    return response


# 加载原下载路径的tokenizer和model
tokenizer = AutoTokenizer.from_pretrained("./Qwen3-1.7b-Medical-R1-sft", use_fast=False, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("./Qwen3-1.7b-Medical-R1-sft", device_map="auto", torch_dtype=torch.bfloat16)

test_texts = {
    '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。",
    'input': "医生，我最近被诊断为糖尿病，听说碳水化合物的选择很重要，我应该选择什么样的碳水化合物呢？"
}

instruction = test_texts['instruction']
input_value = test_texts['input']

messages = [
    {"role": "system", "content": f"{instruction}"},
    {"role": "user", "content": f"{input_value}"}
]

response = predict(messages, model, tokenizer)
print(response)