Update README.md

README.md

@@ -10,4 +10,60 @@ base_model:
 library_name: transformers
 tags:
 - medical
----
+---
+
+<div align="center">
+<h1>
+  FineMedLM
+</h1>
+</div>
+
+<div align="center">
+<a href="https://github.com/hongzhouyu/FineMed" target="_blank">GitHub</a> | <a href="https://arxiv.org/abs/2501.09213" target="_blank">Paper</a>
+</div>
+
+# <span>Introduction</span>
+**FineMedLM** is a medical chat LLM trained via SFT on meticulously crafted synthetic data. By further applying DPO, the model acquires enhanced deep reasoning capabilities, culminating in the development of [FineMedLM-o1](https://huggingface.co/hongzhouyu/FineMedLM-o1).
+
+For more information, visit our GitHub repository.
+
+# <span>Usage</span>
+You can use FineMedLM in the same way as `Llama-3.1-8B-Instruct`:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("hongzhouyu/FineMedLM")
+tokenizer = AutoTokenizer.from_pretrained("hongzhouyu/FineMedLM")
+
+prompt = "How do the interactions between neuronal activity, gonadal hormones, and neurotrophins influence axon regeneration post-injury, and what are the potential therapeutic implications of this research? Please think step by step."
+messages = [
+    {"role": "system", "content": "You are a helpful professional doctor."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+model_inputs = tokenizer([text], return_tensors="pt")
+
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=4096
+)
+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]
+
+print(response)
+```
+
+# <span>Citation</span>
+```
+@misc{yu2025finemedlmo1enhancingmedicalreasoning,
+    title={FineMedLM-o1: Enhancing the Medical Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training}, 
+    author={Hongzhou Yu and Tianhao Cheng and Ying Cheng and Rui Feng},
+    year={2025},
+    eprint={2501.09213},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL},
+    url={https://arxiv.org/abs/2501.09213}, 
+}
+```