II-Vietnam/Medical-SFT-Qwen2.5-7B-Instruct-24-april-RS-v0

II-Medical-7B-Preview

I. Model Overview

II-Medical-7B-Preview is a medical reasoning model trained on a comprehensive dataset of medical knowledge. The model is designed to enhance AI capabilities in medical reasoning.

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/Qwen2.5-7B-Instruct 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: 4.

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

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
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-7B-Preview-Wo-RL	69.13	84.05	77.5	73.49	55.12	67.71	69.48	64.28	19.51	70.64	65.1
II-Medical-7B-Preview-RL	69.42	85.15	77.9	77.26	55.90	65.29	72.72	68.50	22.97	68.66	66.4

IV. Dataset Curation

The training dataset comprises 581,204 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 Data (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
   • Removed incomplete or truncated samples

2. Length-based Filtering

   • Minimum threshold: Keep only the prompt with more than three words.
   • Maximum threshold: Keep only the traces with less than 7,143 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.

Finally, we open sources our dataset at Our Medical Reasoning SFT dataset.

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-7B-Preview

You can also easily start a service using SGLang:

python -m sglang.launch_server --model Intelligent-Internet/II-Medical-7B-Preview

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
• Classification accuracy depends on embedding model and clustering parameters
• Math reasoning traces may introduce domain-mixing effects
• Please note that It’s not suitable for medical use.

VII. Citation

@misc{2025II-Medical-7B,
      title={II-Medical-7B-Preview : Medical Reasoning Model}, 
      author={Intelligent Internet},
      year={2025}
}