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Model: fgewfskjfsd/II-Medical-7B-Preview Source: Original Platform
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README.md
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README.md
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library_name: transformers
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tags: []
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---
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# II-Medical-7B-Preview
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<div style="display: flex; justify-content: center;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6389496ff7d3b0df092095ed/73Y-oDmehp0eJ2HWrfn3V.jpeg" width="800">
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</div>
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## I. Model Overview
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II-Medical-7B-Preview is a medical reasoning model trained on a [comprehensive dataset](https://huggingface.co/datasets/Intelligent-Internet/II-Medical-Reasoning-SFT-V0) of medical knowledge. The model is designed to enhance AI capabilities in medical.
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## II. Training Methodology
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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.
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For SFT stage we using the hyperparameters:
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- Max Length: 16378.
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- Batch Size: 128.
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- Learning-Rate: 5e-5.
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- Number Of Epoch: 4.
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For RL stage we setup training with:
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- Max prompt length: 2048 tokens.
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- Max response length: 12288 tokens.
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- Overlong buffer: Enabled, 4096 tokens, penalty factor 1.0.
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- Clip ratios: Low 0.2, High 0.28.
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- Batch sizes: Train prompt 512, Generation prompt 1536, Mini-batch 32.
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- Responses per prompt: 16.
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- Temperature: 1.0, Top-p: 1.0, Top-k: -1 (vLLM rollout).
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- Learning rate: 1e-6, Warmup steps: 10, Weight decay: 0.1.
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- Loss aggregation: Token-mean.
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- Gradient clipping: 1.0.
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- Entropy coefficient: 0.
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## III. Evaluation Results
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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
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Journal of Medicine, 4 Options and 5 Options splits from the MedBullets platform and MedXpertQA.
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| Model | MedMC | MedQA | PubMed | MMLU-P | GPQA | Lancet | MedB-4 | MedB-5 | MedX | NEJM | Avg |
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|--------------------------|-------|-------|--------|--------|------|--------|--------|--------|------|-------|-------|
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| QWQ 32B | 69.73 | 87.03 | 88.5 | 79.86 | 69.17| 71.3 | 72.07 | 69.01 |24.98 |75.12 | 70.68 |
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| 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 |
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| 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 |
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| Med-reason | 61.67 | 71.87 | 77.4 | 64.1 | 50.51| 59.7 | 60.06 | 54.22 |22.87 |66.8 | 59.92 |
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| M1 | 62.54 | 75.81 | 75.80 | 65.86 | 53.08| 62.62 | 63.64 | 59.74 |19.59 |64.34 | 60.3 |
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| 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 |
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| II-Medical-7B-Preview | **69.42** | **85.15** | 77.9 | **77.26** | **55.90**| 65.29 | **72.72** | **68.50** |**22.97** |68.66 | **66.4** |
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## IV. Dataset Curation
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The training dataset comprises 555,000 samples from the following sources:
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### 1. Public Medical Reasoning Datasets (103,031 samples)
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- General Medical Reasoning: 40,544 samples
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- Medical-R1-Distill-Data: 22,000 samples
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- Medical-R1-Distill-Data-Chinese: 17,000 samples
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- UCSC-VLAA/m23k-tokenized: 23,487 samples
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### 2. Synthetic Medical QA Data with QwQ (225,700 samples)
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Generated from established medical datasets:
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- MedMcQA (from openlifescienceai/medmcqa): 183,000 samples
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- MedQA: 10,000 samples
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- MedReason: 32,700 samples
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### 3. Curated Medical R1 Traces (338,055 samples)
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First we gather all the public R1 traces from:
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- PrimeIntellect/SYNTHETIC-1
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- GeneralReasoning/GeneralThought-430K
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- a-m-team/AM-DeepSeek-R1-Distilled-1.4M
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- open-thoughts/OpenThoughts2-1M
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- nvidia/Llama-Nemotron-Post-Training-Dataset: Science subset only
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- Other resources: cognitivecomputations/dolphin-r1, ServiceNow-AI/R1-Distill-SFT,...
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All R1 reasoning traces were processed through a domain-specific pipeline as follows:
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1. Embedding Generation: Prompts are embedded using sentence-transformers/all-MiniLM-L6-v2.
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2. Clustering: Perform K-means clustering with 50,000 clusters.
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3. Domain Classification:
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- For each cluster, select the 10 prompts nearest to the cluster center.
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- Classify the domain of each selected prompt using Qwen2.5-32b-Instruct.
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- Assign the cluster's domain based on majority voting among the classified prompts.
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4. Domain Filtering: Keep only clusters labeled as Medical or Biology for the final dataset.
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### 4. Supplementary Math Dataset
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- Added 15,000 samples of reasoning traces from light-r1
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- Purpose: Enhance general reasoning capabilities of the model
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### Preprocessing Data
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1. Filtering for Complete Generation
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- Retained only traces with complete generation outputs
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2. Length-based Filtering
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- Minimum threshold: Keep only the prompt with more than 3 words.
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- Maximum threshold: Keep only the traces with less than 7,143 words.
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- Wait Token Filter: Removed traces with has more than 47 occurrences of "Wait" (97th percentile threshold).
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### Data Decontamination
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We using two step decontamination:
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1. Following open-r1 project: We decontaminate a dataset using 10-grams with the evaluation datasets.
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2. After that, we using the fuzzy decontamination from `s1k` method with threshold 90%.
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**Our pipeline is carefully decontaminated with the evaluation datasets.**
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## V. How To Use
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Our model can be utilized in the same manner as Qwen or Deepseek-R1-Distill models.
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For instance, you can easily start a service using [vLLM](https://github.com/vllm-project/vllm):
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```bash
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vllm serve Intelligent-Internet/II-Medical-7B-Preview
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```
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You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang):
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```bash
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python -m sglang.launch_server --model Intelligent-Internet/II-Medical-7B-Preview
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```
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## VI. Usage Guidelines
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- Recommended Sampling Parameters: temperature = 0.6, top_p = 0.9
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- 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{}.").
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## VII. Limitations and Considerations
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- Dataset may contain inherent biases from source materials
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- Medical knowledge requires regular updates
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- Please note that **It’s not suitable for medical use.**
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## VIII. Citation
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```bib
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@misc{2025II-Medical-7B-Preview,
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title={II-Medical-7B-Preview: Medical Reasoning Model},
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author={Intelligent Internet},
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year={2025}
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}
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```
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