ericrisco/gpt-oss-20b-medical-rescue

gpt-oss-20b–medical-rescue

A ~20B parameter language model (base: gpt-oss-20b) fine-tuned with Unsloth on a curated 80k+ Q&A dataset focused on first aid, emergency medicine, and clinical reasoning.

⚠️ IMPORTANT DISCLAIMER: This model is for educational and research purposes only. It must never be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult qualified healthcare professionals.

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Model Summary

• Primary Task: Medical and rescue Q&A, procedural first aid guidance, and basic clinical reasoning.

• Training Dataset: ericrisco/medical-training-dataset (80k+ examples, CC-BY-4.0).

• Fine-tuning Method: Unsloth (PEFT).

• Data Structure:

  • input: medical question or scenario
  • context: additional information (optional)
  • output: detailed answer
  • source: original dataset or document reference

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Intended Use

Suitable for:

• Medical education and training simulations.
• First aid and emergency response learning tools.
• Research in medical NLP safety and reasoning.

Not suitable for:

• Real-time medical decision-making.
• Diagnosing conditions or prescribing treatments.
• Emergency triage or operational deployment in healthcare systems.

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Training Data

Dataset: ericrisco/medical-training-dataset (CC-BY-4.0). Size: 80,000+ Q&A pairs.

Sources:

• 11 public medical datasets (e.g., medical-o1-reasoning-SFT, medqa, medicationqa, symptom_to_diagnosis).
• 14 official medical & emergency PDFs from WHO, IFRC, FEMA, and governmental emergency guides.
• Synthetic Q&A generation using Ollama + Gemma3N with a RAG pipeline powered by FAISS, Cohere embeddings, and strict medical relevance filtering.

Processing Pipeline:

1. PDF extraction & chunking with LangChain (512 tokens per chunk, 128 overlap).
2. Semantic vectorization via Cohere embed-v4.0.
3. RAG-based question generation from authoritative sources.
4. Answer synthesis with medical accuracy validation.
5. Data standardization into a unified schema.

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Fine-Tuning Details

• Base model: gpt-oss-20b
• Framework: Unsloth (LoRA/QLoRA PEFT).
• Training objective: Supervised fine-tuning (SFT) on high-quality Q&A format.
• Safety filters: Removal of irrelevant, administrative, or unsafe content; enforced presence of key medical indicators (e.g., patient assessment, vital signs, treatment steps).
• Dataset balance: Shuffled and deduplicated to avoid overfitting.

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License

• Model weights: Same license as gpt-oss-20b.
• Dataset: CC-BY-4.0 — Medical Training Dataset: Comprehensive Medical Q&A for AI Training.
• Pipeline & scripts: GitHub – gemma3n-impact-challenge.

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Citation

If you use this dataset or model, please cite:

@dataset{medical_training_dataset_2025,
  title={Medical Training Dataset: Comprehensive Medical Q&A for AI Training},
  author={Eric Risco},
  year={2025},
  url={https://huggingface.co/datasets/ericrisco/medical-training-dataset},
  repository={https://github.com/ericrisco/gemma3n-impact-challenge}
}

@misc{gpt_oss_20b_medical_rescue_2025,
  title={gpt-oss-20b–medical-rescue},
  author={Eric Risco},
  year={2025},
  howpublished={\url{https://huggingface.co/TU_USERNAME/gpt-oss-20b–medical-rescue}}
}