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

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. . 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)

• General Medical Reasoning: 40,544 samples
• Medical-R1-Distill-Data: 22,000 samples
• Medical-R1-Distill-Data-Chinese: 17,000 samples
• UCSC-VLAA/m23k-tokenized: 23,487 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:

• PrimeIntellect/SYNTHETIC-1
• GeneralReasoning/GeneralThought-430K
• a-m-team/AM-DeepSeek-R1-Distilled-1.4M
• open-thoughts/OpenThoughts2-1M
• nvidia/Llama-Nemotron-Post-Training-Dataset: Science subset only
• Other resources: cognitivecomputations/dolphin-r1, ServiceNow-AI/R1-Distill-SFT,...

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}
}