初始化项目，由ModelHub XC社区提供模型

Model: XformAI-india/Qwen3-4B-medicaldataset
Source: Original Platform

README.md

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+---
+license: mit
+datasets:
+- FreedomIntelligence/medical-o1-reasoning-SFT
+base_model:
+- Qwen/Qwen3-4B
+tags:
+- medical
+---
+
+# 🧠 Qwen3-4B-MedicalDataset
+
+A fine-tuned version of [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) on a curated **medical dataset**, designed to assist with medical Q&A, clinical documentation, and healthcare-related reasoning. Developed by **XformAI-India**.
+
+---
+
+## 📌 Model Details
+
+- **Base Model**: Qwen3-4B (from Qwen by Alibaba)
+- **Architecture**: Transformer Decoder (GPT-like)
+- **Parameters**: 4 Billion
+- **Precision**: bfloat16 / float16
+- **Fine-tuning method**: Supervised fine-tuning (SFT)
+- **Dataset**: FreedomIntelligence/medical-o1-reasoning-SFT
+---
+
+## 🧪 Intended Use
+
+This model is intended for **research** and **educational purposes** in the following domains:
+
+- Medical question answering
+- Summarizing patient records
+- Medical reasoning and triage
+- Chatbot integration for healthcare support (non-diagnostic)
+
+---
+
+## 🚫 Limitations & Disclaimer
+
+> ⚠️ **Not for clinical use.** This model is not a replacement for professional medical advice, diagnosis, or treatment.
+
+- May hallucinate or produce incorrect medical information.
+- Trained only on publicly available or synthetic datasets — not real patient data.
+- Should not be used in emergency or high-stakes settings.
+
+---
+
+## 💡 Example Usage
+
+\`\`\`python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("XformAI-india/Qwen3-4B-medicaldataset", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("XformAI-india/Qwen3-4B-medicaldataset", trust_remote_code=True)
+
+prompt = "Patient presents with chest pain and shortness of breath. What are possible differential diagnoses?"
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+\`\`\`
+
+---
+
+## 🏗 Training Configuration
+
+- **Epochs**: 3
+- **Batch Size**: 8
+- **Optimizer**: AdamW
+- **Learning Rate**: 2e-5
+- **Gradient Accumulation**: 4
+- **Framework**: PyTorch, Hugging Face Transformers
+
+---
+
+
+## 🧠 Citation
+
+If you use this model, please cite:
+
+
+```bibtex
+@misc{qwen3medical2025,
+  title={Qwen3-4B-MedicalDataset: A Fine-Tuned Transformer for Medical AI Research},
+  author={XformAI-India},
+  year={2025},
+  url={https://huggingface.co/XformAI-india/Qwen3-4B-medicaldataset}
+}
+```
+
+---