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Model: testUser/Qwen3-1.7b-Medical-R1-sft
Source: Original Platform

README.md

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+---
+frameworks:
+- Pytorch
+license: Apache License 2.0
+tasks:
+- text-generation
+
+#model-type:
+##如 gpt、phi、llama、chatglm、baichuan 等
+#- gpt
+
+#domain:
+##如 nlp、cv、audio、multi-modal
+#- nlp
+
+#language:
+##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
+#- cn 
+
+#metrics:
+##如 CIDEr、Blue、ROUGE 等
+#- CIDEr
+
+#tags:
+##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
+#- pretrained
+
+#tools:
+##如 vllm、fastchat、llamacpp、AdaSeq 等
+#- vllm
+---
+
+[Github](https://github.com/Zeyi-Lin/Qwen3-Medical-SFT)
+
+- **基础模型**：[Qwen3-1.7B](https://modelscope.cn/models/Qwen/Qwen3-1.7B/summary)
+- **微调后模型**：[Qwen3-1.7b-Medical-R1-sft](https://modelscope.cn/models/testUser/Qwen3-1.7b-Medical-R1-sft/summary)
+- **数据集**：[delicate_medical_r1_data](https://modelscope.cn/datasets/krisfu/delicate_medical_r1_data)
+- **SwanLab**：[qwen3-sft-medical](https://swanlab.cn/@ZeyiLin/qwen3-sft-medical/runs/agps0dkifth5l1xytcdyk/chart)
+- **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）](https://zhuanlan.zhihu.com/p/1903848838214705484)
+
+## 模型下载
+
+```bash
+#安装ModelScope
+pip install modelscope
+```
+```python
+#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
+```
+
+<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
+
+## 模型推理
+
+```bash
+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)
+```