II-Medical-8B-1706-GGUF

QuantFactory/II-Medical-8B-1706-GGUF

This is quantized version of Intelligent-Internet/II-Medical-8B-1706 created using llama.cpp

Original Model Card

II-Medical-8B-1706

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I. Model Overview

II-Medical-8B-1706 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-8B, this new iteration significantly advances the capabilities of medical question answering,

We also provide the static quants versions of II-Medical-8B-1706 here

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: 6.

For the Reinforcement Learning (RL) stage, we designed a two-stage training process. The first stage focuses on enhancing the model's reasoning capabilities for complex medical questions. The second stage ensures that the model's responses prioritize safety and helpfulness. Both stages utilize the following configuration:

• 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-1706 model achieved a 46.8% score on HealthBench, a comprehensive open-source benchmark evaluating the performance and safety of large language models in healthcare. This performance is comparable to MedGemma-27B from Google. We provide a comparison to models available in ChatGPT below.

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Detailed result for HealthBench can be found here.

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We also evaluate on nine other medical QA benchmarks include MedMCQA, MedQA, PubMedQA, HealthBench, medical related questions from MMLU-Pro, 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 2.3M samples from the following sources:

1. Public Medical Reasoning Datasets

• General Medical Reasoning
• Medical-R1-Distill-Data
• Medical-R1-Distill-Data-Chinese
• UCSC-VLAA/m23k-tokenized

2. Synthetic Medical QA Data with Qwen3-235B-A22B

Generated from established medical datasets:

• MedMcQA
• MedQA
• MedReason

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. Other

• 269k prompts from Text-Book-QA-subset
• 162k prompts from Text-Patient-QA-subset
• 112k prompts from Chat-Doctor-QA
• Subset Instruction-following from a-m-team/AM-Qwen3-Distilled

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).

3. Response Deduplicate

   • Ngram: 4
   • Jacard Threshold: 0.7

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-1706

You can also easily start a service using SGLang:

python -m sglang.launch_server --model Intelligent-Internet/II-Medical-8B-1706

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-1706,
      title={II-Medical-8B: Medical Reasoning Model}, 
      author={Intelligent Internet},
      year={2025}
}