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

<span style="color: #7FFF7F;">II-Medical-8B-1706 GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit `aa0ef5c5`.

<span style="color: #7FFF7F;">Quantization Beyond the IMatrix</span>

I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides.

In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the --tensor-type option in llama.cpp to manually "bump" important layers to higher precision. You can see the implementation here: 👉 Layer bumping with llama.cpp

While this does increase model file size, it significantly improves precision for a given quantization level.

I'd love your feedback—have you tried this? How does it perform for you?

<a href="https://readyforquantum.com/huggingfaceggufselection_guide.html" style="color: #7FFF7F;"> Click here to get info on choosing the right GGUF model format </a>

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II-Medical-8B-1706

<div style="display: flex; justify-content: center;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/6389496ff7d3b0df092095ed/73Y-oDmehp0eJ2HWrfn3V.jpeg" width="800"> </div>

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.

<!-- --> 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. 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. 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. 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.Wait Token Filter: Removed traces with has more than 47 occurrences of "Wait" (97th percentile threshold).
4. 4.Response Deduplicate
5. 5.Ngram: 4
6. 6.Jacard Threshold: 0.7

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-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}
}

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<span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test: Choose an AI assistant type:

• —TurboLLM (GPT-4.1-mini)
• —HugLLM (Hugginface Open-source models)
• —TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• —Function calling against live network services
• —How small can a model go while still handling:
• —Automated Nmap security scans
• —Quantum-readiness checks
• —Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• —✅ Zero-configuration setup
• —⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• —🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• —**It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• —Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• —Real-time network diagnostics and monitoring
• —Security Audits
• —Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• —🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. 1."Give me info on my websites SSL certificate"
2. 2."Check if my server is using quantum safe encyption for communication"
3. 3."Run a comprehensive security audit on my server"
4. 4.'"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊