Update README.md

README.md

@@ -18,3 +18,33 @@ license: apache-2.0
 - By finetuning with proposed [MedReason dataset](https://huggingface.co/datasets/UCSC-VLAA/MedReason), our best model [MedReason-8B](https://huggingface.co/UCSC-VLAA/MedReason-8B), achieves *state-of-the-art* performance.
 
 We open-sourced our model here.
+
+## 👨‍⚕️ Model
+
+- **Model Access**
+
+| Model             | Base Model                                                   | Link                                                       |
+| ----------------- | ------------------------------------------------------------ | ---------------------------------------------------------- |
+| MedReason-8B      | [HuatuoGPT-o1-8B](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-8B) | [Link](https://huggingface.co/UCSC-VLAA/MedReason-8B)      |
+| MedReason-Llama   | [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) | [Link](https://huggingface.co/UCSC-VLAA/MedReason-Llama)   |
+| MedReason-Mistral | [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | [Link](https://huggingface.co/UCSC-VLAA/MedReason-Mistral) |
+
+- **Deploy**: we provide a example code for direct inference with MedReason-8B. 
+
+  Also, MedReason-8B can be deployed with tools like [vllm](https://github.com/vllm-project/vllm) or [Sglang](https://github.com/sgl-project/sglang), we provide code for model deployment using Sglang in `./src/evaluation/eval.py`
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained('UCSC-VLAA/MedReason-8B',torch_dtype="auto",device_map="auto", use_safetensors= True)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained('UCSC-VLAA/MedReason-8B', trust_remote_code=True, padding_side='left')
+
+input_text = "How to stop a cough?"
+messages = [{"role": "user", "content": input_text}]
+
+inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False,add_generation_prompt=True), return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=2048)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```