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+
+---
+
+library_name: transformers
+tags: []
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/II-Medical-8B-GGUF
+This is quantized version of [Intelligent-Internet/II-Medical-8B](https://huggingface.co/Intelligent-Internet/II-Medical-8B) created using llama.cpp
+
+# Original Model Card
+
+
+# II-Medical-8B
+
+<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 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-7B-Preview](https://huggingface.co/Intelligent-Internet/II-Medical-7B-Preview), this new iteration significantly advances the capabilities of medical question answering, 
+
+## 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: 8.
+
+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
+
+Our II-Medical-8B model achieved a 40% score on [HealthBench](https://openai.com/index/healthbench/), a comprehensive open-source benchmark evaluating the performance and safety of large language models in healthcare. This performance is comparable to OpenAI's o1 reasoning model and GPT-4.5, OpenAI's largest and most advanced model to date. We provide a comparison to models available in ChatGPT below.
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61f2636488b9b5abbe184a8e/5r2O4MtzffVYfuUZJe5FO.jpeg)
+Detailed result for HealthBench can be found [here](https://huggingface.co/datasets/Intelligent-Internet/OpenAI-HealthBench-II-Medical-8B-GPT-4.1).
+
+![Model Benchmark](https://cdn-uploads.huggingface.co/production/uploads/6389496ff7d3b0df092095ed/uvporIhY4_WN5cGaGF1Cm.png)
+
+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   |
+|--------------------------|-------|-------|--------|--------|------|--------|--------|--------|------|-------|-------|
+| [HuatuoGPT-o1-72B](https://huggingface.co/FreedomIntelligence/HuatuoGPT-o1-72B)         | 76.76 | 88.85 | 79.90   | 80.46  | 64.36| 70.87   | 77.27  | 73.05  |23.53 |76.29  | 71.13 |
+| [QWQ 32B](https://huggingface.co/Qwen/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](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)            | 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](http://FreedomIntelligence/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](https://huggingface.co/UCSC-VLAA/MedReason-8B)               | 61.67 | 71.87 | 77.4   | 64.1   | 50.51| 59.7   | 60.06  | 54.22  |22.87 |66.8   | 59.92 |
+| [M1](https://huggingface.co/UCSC-VLAA/m1-7B-23K)                     | 62.54 | 75.81 | 75.80  | 65.86  | 53.08| 62.62  | 63.64  | 59.74  |19.59 |64.34  | 60.3  |
+| [II-Medical-8B-SFT](https://huggingface.co/II-Vietnam/II-Medical-8B-SFT)        | **71.92** | 86.57 | 77.4   | 77.26  | 65.64| 69.17  | 76.30  | 67.53  |23.79 |**73.80**  | 68.80  |
+| [II-Medical-8B](https://huggingface.co/Intelligent-Internet/II-Medical-8B)            | 71.57 | **87.82** | 78.2   | **80.46**  | **67.18**| **70.38**  | **78.25**  | **72.07**  |**25.26** |73.13  | **70.49**  |
+
+## 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](https://huggingface.co/datasets/GeneralReasoning/GeneralThought-430K): 40,544 samples
+- [Medical-R1-Distill-Data](https://huggingface.co/datasets/FreedomIntelligence/Medical-R1-Distill-Data): 22,000 samples
+- [Medical-R1-Distill-Data-Chinese](https://huggingface.co/datasets/FreedomIntelligence/Medical-R1-Distill-Data-Chinese): 17,000 samples
+- [UCSC-VLAA/m23k-tokenized](https://huggingface.co/datasets/UCSC-VLAA/m23k-tokenized): 23,487 samples
+
+### 2. Synthetic Medical QA Data with QwQ (225,700 samples)
+Generated from established medical datasets:
+- [MedMcQA](https://huggingface.co/datasets/openlifescienceai/medmcqa) (from openlifescienceai/medmcqa): 183,000 samples
+- [MedQA](https://huggingface.co/datasets/bigbio/med_qa): 10,000 samples
+- [MedReason](https://huggingface.co/datasets/UCSC-VLAA/MedReason): 32,700 samples
+
+### 3. Curated Medical R1 Traces (338,055 samples)
+
+First we gather all the public R1 traces from:
+
+- [PrimeIntellect/SYNTHETIC-1](https://huggingface.co/collections/PrimeIntellect/synthetic-1-67a2c399cfdd6c9f7fae0c37)
+- [GeneralReasoning/GeneralThought-430K](https://huggingface.co/datasets/GeneralReasoning/GeneralThought-430K)
+- [a-m-team/AM-DeepSeek-R1-Distilled-1.4M](https://arxiv.org/abs/2503.19633v1)
+- [open-thoughts/OpenThoughts2-1M](https://huggingface.co/datasets/open-thoughts/OpenThoughts2-1M)
+- [nvidia/Llama-Nemotron-Post-Training-Dataset](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset): Science subset only
+- Other resources: [cognitivecomputations/dolphin-r1](https://huggingface.co/datasets/cognitivecomputations/dolphin-r1), [ServiceNow-AI/R1-Distill-SFT](https://huggingface.co/datasets/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](https://arxiv.org/abs/2503.10460)
+- Purpose: Enhance general reasoning capabilities of the model
+
+### 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).
+
+
+### Data Decontamination
+
+We using two step decontamination:
+1. Following [open-r1](https://github.com/huggingface/open-r1) project: We decontaminate a dataset using 10-grams with the evaluation datasets.
+2. After that, we using the fuzzy decontamination from [`s1k`](https://arxiv.org/abs/2501.19393) 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](https://github.com/vllm-project/vllm):
+
+```bash
+vllm serve Intelligent-Internet/II-Medical-8B
+```
+
+You can also easily start a service using [SGLang](https://github.com/sgl-project/sglang):
+
+```bash
+python -m sglang.launch_server --model Intelligent-Internet/II-Medical-8B
+```
+
+## 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
+
+```bib
+@misc{2025II-Medical-8B,
+      title={II-Medical-8B: Medical Reasoning Model}, 
+      author={Intelligent Internet},
+      year={2025}
+}
+```