II-Medical-8B 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.

For RL stage we setup training with:

Max prompt length: 2048 tokens.
Max response length: 12288 tokens.
Overlong buffer: Enabled, 4096 tokens, penalty factor 1.0.
Clip ratios: Low 0.2, High 0.28.
Batch sizes: Train prompt 512, Generation prompt 1536, Mini-batch 32.
Responses per prompt: 16.
Temperature: 1.0, Top-p: 1.0, Top-k: -1 (vLLM rollout).
Learning rate: 1e-6, Warmup steps: 10, Weight decay: 0.1.
Loss aggregation: Token-mean.
Gradient clipping: 1.0.
Entropy coefficient: 0.

III. Evaluation Results

Our II-Medical-8B model achieved a 40% score on HealthBench, a comprehensive 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. We provide a comparison to models available in ChatGPT below.

Detailed result for HealthBench can be found here.

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

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:

Embedding Generation: Prompts are embedded using sentence-transformers/all-MiniLM-L6-v2.
Clustering: Perform K-means clustering with 50,000 clusters.
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.
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

Filtering for Complete Generation
- Retained only traces with complete generation outputs
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:

Following open-r1 project: We decontaminate a dataset using 10-grams with the evaluation datasets.
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

bib

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