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II-Medical-8B-SFT

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library_name: transformers
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II-Medical-8B-SFT

I. Model Overview

II-Medical-8B-SFT is the newest advanced large language model developed by Intelligent Internet, specifically engineered to enhance AI-driven medical reasoning. Following the positive reception of our previous II-Medical-7B-Preview, this new iteration significantly advances the capabilities of medical question answering,

II. Training Methodology

We collected and generated a comprehensive set of reasoning datasets for the medical domain and performed SFT fine-tuning on the Qwen/Qwen3-8B model. Following this, we further optimized the SFT model by training DAPO on a hard-reasoning dataset to boost performance.

For SFT stage we using the hyperparameters:

  • Max Length: 16378.
  • Batch Size: 128.
  • Learning-Rate: 5e-5.
  • Number Of Epoch: 8.

III. Evaluation Results

Model Benchmark

We evaluate on ten medical QA benchmarks include MedMCQA, MedQA, PubMedQA, medical related questions from MMLU-Pro and GPQA, small QA sets from Lancet and the New England Journal of Medicine, 4 Options and 5 Options splits from the MedBullets platform and MedXpertQA.

Model MedMC MedQA PubMed MMLU-P GPQA Lancet MedB-4 MedB-5 MedX NEJM Avg
HuatuoGPT-o1-72B 76.76 88.85 79.90 80.46 64.36 70.87 77.27 73.05 23.53 76.29 71.13
QWQ 32B 69.73 87.03 88.5 79.86 69.17 71.3 72.07 69.01 24.98 75.12 70.68
Qwen2.5-7B-IT 56.56 61.51 71.3 61.17 42.56 61.17 46.75 40.58 13.26 59.04 51.39
HuatuoGPT-o1-8B 63.97 74.78 80.10 63.71 55.38 64.32 58.44 51.95 15.79 64.84 59.32
Med-reason 61.67 71.87 77.4 64.1 50.51 59.7 60.06 54.22 22.87 66.8 59.92
M1 62.54 75.81 75.80 65.86 53.08 62.62 63.64 59.74 19.59 64.34 60.3
II-Medical-8B-SFT 71.92 86.57 77.4 77.26 65.64 69.17 76.30 67.53 23.79 73.80 68.80
II-Medical-8B 71.57 87.82 78.2 80.46 67.18 70.38 78.25 72.07 25.26 73.13 70.49

Our II-Medical-8B model also achieved a 40% score on HealthBench, an open-source benchmark evaluating the performance and safety of large language models in healthcare. This performance is comparable to OpenAI's o1 reasoning model and GPT-4.5, OpenAI's largest and most advanced model to date. image/png. Details result for HealthBench, you can find here.

IV. Dataset Curation

The training dataset comprises 555,000 samples from the following sources:

1. Public Medical Reasoning Datasets (103,031 samples)

2. Synthetic Medical QA Data with QwQ (225,700 samples)

Generated from established medical datasets:

  • MedMcQA (from openlifescienceai/medmcqa): 183,000 samples
  • MedQA: 10,000 samples
  • MedReason: 32,700 samples

3. Curated Medical R1 Traces (338,055 samples)

First we gather all the public R1 traces from:

All R1 reasoning traces were processed through a domain-specific pipeline as follows:

  1. Embedding Generation: Prompts are embedded using sentence-transformers/all-MiniLM-L6-v2.

  2. Clustering: Perform K-means clustering with 50,000 clusters.

  3. Domain Classification:

    • For each cluster, select the 10 prompts nearest to the cluster center.
    • Classify the domain of each selected prompt using Qwen2.5-32b-Instruct.
    • Assign the cluster's domain based on majority voting among the classified prompts.

  4. Domain Filtering: Keep only clusters labeled as Medical or Biology for the final dataset.

4. Supplementary Math Dataset

  • Added 15,000 samples of reasoning traces from light-r1
  • Purpose: Enhance general reasoning capabilities of the model

Preprocessing Data

  1. Filtering for Complete Generation

    • Retained only traces with complete generation outputs

  2. Length-based Filtering

    • Minimum threshold: Keep only the prompt with more than 3 words.
    • Wait Token Filter: Removed traces with has more than 47 occurrences of "Wait" (97th percentile threshold).

Data Decontamination

We using two step decontamination:

  1. Following open-r1 project: We decontaminate a dataset using 10-grams with the evaluation datasets.
  2. After that, we using the fuzzy decontamination from s1k method with threshold 90%.

Our pipeline is carefully decontaminated with the evaluation datasets.

V. How To Use

Our model can be utilized in the same manner as Qwen or Deepseek-R1-Distill models.

For instance, you can easily start a service using vLLM:

vllm serve Intelligent-Internet/II-Medical-8B

You can also easily start a service using SGLang:

python -m sglang.launch_server --model Intelligent-Internet/II-Medical-8B

VI. Usage Guidelines

  • Recommended Sampling Parameters: temperature = 0.6, top_p = 0.9
  • When using, explicitly request step-by-step reasoning and format the final answer within \boxed{} (e.g., "Please reason step-by-step, and put your final answer within \boxed{}.").

VII. Limitations and Considerations

  • Dataset may contain inherent biases from source materials
  • Medical knowledge requires regular updates
  • Please note that It’s not suitable for medical use.

VIII. Citation 

@misc{2025II-Medical-8B,
      title={II-Medical-8B: Medical Reasoning Model}, 
      author={Intelligent Internet},
      year={2025}
}