| --- |
| library_name: transformers |
| license: apache-2.0 |
| license_link: https://huggingface.co/UbiquantAI/Fleming-R1-7B/blob/main/LICENSE |
| pipeline_tag: text-generation |
| --- |
| |
| # Fleming-R1-7B |
| <p align="center" style="margin: 0;"> |
| <a href="https://github.com/UbiquantAI/Fleming-R1" aria-label="GitHub Repository" style="text-decoration:none;"> |
| <span style="display:inline-flex;align-items:center;gap:.35em;"> |
| <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 16 16" |
| width="16" height="16" aria-hidden="true" |
| style="vertical-align:text-bottom;fill:currentColor;"> |
| <path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0016 8c0-4.42-3.58-8-8-8Z"/> |
| </svg> |
| <span>GitHub</span> |
| </span> |
| </a> |
| <span style="margin:0 .75em;opacity:.6;">•</span> |
| <a href="https://arxiv.org/abs/2509.15279" aria-label="Paper">📑 Paper</a> |
| </p> |
| |
| ## Highlights |
|
|
| ## 📖 Model Overview |
|
|
| Fleming-R1 is a reasoning model for medical scenarios that can perform step-by-step analysis of complex problems and produce reliable answers. The model follows a training paradigm of “chain-of-thought cold start” plus large-scale reinforcement learning. On multiple medical benchmarks, the 7B version achieves SOTA among models of a similar size; the 32B version performs close to the much larger GPT-OSS-120B and shows stronger results on Chinese tasks. |
|
|
| **Model Features:** |
|
|
| * **Reasoning-oriented data strategy** Combines public medical datasets with knowledge graphs to improve coverage of rare diseases, medications, and multi-hop reasoning chains; |
| * **Chain-of-thought cold start** Uses high-quality reasoning traces distilled from teacher models to guide the model in learning basic reasoning patterns; |
| * **Two-stage reinforcement learning** Employs adaptive hard-negative mining to strengthen the model’s reasoning when facing difficult problems. |
|
|
| ## 📦 Releases |
|
|
| - **Fleming-R1-7B** —— Trained on Qwen2.5-7B |
| 🤗 [`UbiquantAI/Fleming-R1-7B`](https://huggingface.co/UbiquantAI/Fleming-R1-7B) |
| - **Fleming-R1-32B** —— Trained on Qwen3-32B |
| 🤗 [`UbiquantAI/Fleming-R1-32B`](https://huggingface.co/UbiquantAI/Fleming-R1-32B) |
|
|
| ## 📊 Performance |
|
|
| ### Main Benchmark Results |
|
|
| <div align="center"> |
| <img src="images/exp_result.png" alt="Benchmark Results" width="60%"> |
| </div> |
|
|
| ### Reasoning Ability Comparison |
|
|
| On the MedXpertQA benchmark, which evaluates medical reasoning ability, Fleming-R1 surpasses models of similar—and even larger—sizes, and is on par with certain closed-source models. |
|
|
| <div align="center"> |
| <img src="images/size_compare.png" alt="Size comparison" width="60%"> |
| </div> |
|
|
| ## 🔧 Quick Start |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_name = "UbiquantAI/Fleming-R1-7B" |
| |
| # load the tokenizer and the model |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| |
| # prepare the model input |
| prompt = "What should I do if I suddenly develop a fever?" |
| messages = [ |
| {"role": "user", "content": prompt} |
| ] |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True, |
| ) |
| |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| |
| # conduct text completion |
| generated_ids = model.generate( |
| **model_inputs, |
| max_new_tokens=32768 |
| ) |
| output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
| |
| # parsing thinking content |
| output = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n") |
| thinking_content = output.split("<think>")[-1].split("</think>")[0] |
| content = output.split("</think>")[-1] |
| |
| print("####thinking content:\n", thinking_content) |
| print("\n") |
| print("####answer:\n", content) |
| ``` |
|
|
| ## ⚠️ Safety Statement |
|
|
| This project is for research and non-clinical reference only; it must not be used for actual diagnosis or treatment decisions. |
| The generated reasoning traces are an auditable intermediate process and do not constitute medical advice. |
| In medical scenarios, results must be reviewed and approved by qualified professionals, and all applicable laws, regulations, and privacy compliance requirements in your region must be followed. |
|
|
| ## 📚 Citation |
|
|
| ```bibtex |
| @misc{flemingr1, |
| title={Fleming-R1: Toward Expert-Level Medical Reasoning via Reinforcement Learning}, |
| author={Chi Liu and Derek Li and Yan Shu and Robin Chen and Derek Duan and Teng Fang and Bryan Dai}, |
| year={2025}, |
| eprint={2509.15279}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/2509.15279}, |
| } |
| ``` |
|
|
