Intelligent-Internet/II-Medical-7B-Preview

II-Medical-7B-Preview

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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.

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.

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

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

1. 1.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.

1. 1.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. 1.Filtering for Complete Generation
2. 2.Retained only traces with complete generation outputs

1. 1.Length-based Filtering
2. 2.Minimum threshold: Keep only the prompt with more than 3 words.
3. 3.Maximum threshold: Keep only the traces with less than 7,143 words.
4. 4.Wait Token Filter: Removed traces with has more than 47 occurrences of "Wait" (97th percentile threshold).

Data Decontamination

We using two step decontamination:

1. 1.Following open-r1 project: We decontaminate a dataset using 10-grams with the evaluation datasets.
2. 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-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
• —Please note that It’s not suitable for medical use.

VIII. Citation

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