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Model: tdlhl/MedSSR-Qwen3-8B-Base Source: Original Platform
2026-04-21 21:26:42 +08:00
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+---
+base_model:
+- Qwen/Qwen3-8B-Base
+tags:
+- medical
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# MedSSR-Qwen3-8B-Base
+
+This is the model for our ACL 2026 Findings paper, "[Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach](https://huggingface.co/papers/2604.11547)". `MedSSR-Qwen3-8B-Base` is a medical reasoning-focused LLM built from `Qwen/Qwen3-8B-Base`.
+
+## Model Summary
+
+- **Base model**: `Qwen/Qwen3-8B-Base`
+- **Model name**: `MedSSR-Qwen3-8B-Base`
+- **Training framework**: [verl](https://github.com/verl-project/verl)
+- **Paper link**: [https://arxiv.org/pdf/2604.11547](https://huggingface.co/papers/2604.11547)
+- **Github repo**: [https://github.com/tdlhl/MedSSR](https://github.com/tdlhl/MedSSR)
+- **Hugging Face training dataset**: [tdlhl/MedSSR-Synthetic-43K](https://huggingface.co/datasets/tdlhl/MedSSR-Synthetic-43K)
+- **Hugging Face test dataset**: [tdlhl/RareDis-Sub](https://huggingface.co/datasets/tdlhl/RareDis-Sub)
+
+## Quick Start (Transformers)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "tdlhl/MedSSR-Qwen3-8B-Base"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": (
+            "A 67-year-old man develops crushing substernal chest pain for 40 minutes. "
+            "ECG shows ST-segment elevation in leads II, III, and aVF. "
+            "Which coronary artery is most likely occluded?
+"
+            "A. Left anterior descending artery
+"
+            "B. Left circumflex artery
+"
+            "C. Right coronary artery
+"
+            "D. Posterior descending artery"
+        ),
+    }
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+)
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=1024,
+        temperature=0.6,
+        top_p=0.95,
+    )
+
+new_tokens = outputs[0][inputs["input_ids"].shape[-1]:]
+print(tokenizer.decode(new_tokens, skip_special_tokens=True))
+```
+
+## Quick Start (vLLM)
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(
+    model="tdlhl/MedSSR-Qwen3-8B-Base",
+    trust_remote_code=True,
+)
+
+sampling = SamplingParams(
+    temperature=0.6,
+    top_p=0.95,
+    max_tokens=1024,
+)
+
+prompt = (
+    "A 24-year-old woman presents with fatigue, weight gain, constipation, and cold intolerance. "
+    "Which of the following laboratory findings is most consistent with primary hypothyroidism?
+"
+    "A. Low TSH, low free T4
+"
+    "B. High TSH, low free T4
+"
+    "C. High TSH, high free T4
+"
+    "D. Low TSH, high free T4"
+)
+
+outputs = llm.generate([prompt], sampling_params=sampling)
+print(outputs[0].outputs[0].text)
+```
+
+## Suggested Decoding Setup
+
+For evaluation settings similar to our paper, we follow the recommended setting of Qwen:
+
+```text
+temperature=0.6
+top_p=0.95
+top_k=20
+max_tokens=2048
+```
+
+## Notes
+
+- This model is intended for research use.
+- The model may produce incorrect or unverifiable medical reasoning.
+- Outputs should not be used as a substitute for professional medical judgment.
+- For benchmark-style evaluation, please follow the released evaluation script in our repository.
+
+## Citation
+
+If you find our model useful, please cite our paper:
+
+```bibtex
+@article{li2025eliciting,
+  title={Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach},
+  author={Haolin Li, Shuyang Jiang, Ruipeng Zhang, Jiangchao Yao, Ya Zhang, Yanfeng Wang},
+  journal={arXiv preprint arXiv:2604.11547},
+  year={2026}
+}
+```