Llama-MedX v3.2

A medical-domain language model built on Meta Llama 3.1 8B, fine-tuned for clinical reasoning and medical question answering. Quantized to GGUF for efficient local inference via Ollama.

🚀 Overview

llama-medx_v32 is an Ollama packaging of skumar9/Llama-medx_v3.2, a medical large language model derived from Meta Llama 3.1 8B. The base model was adapted to the medical domain using Supervised Fine-Tuning (SFT) followed by Odds Ratio Preference Optimization (ORPO), with hyperparameter strategies designed to align it with medical terminology and reasoning while mitigating catastrophic forgetting. It was further fine-tuned for answering medical multiple-choice questions.

This build is not an abliterated or uncensored model, it retains the safety behavior of its base. It is intended for medical research and education only.

• Base model: skumar9/Llama-medx_v3.2 (on Meta Llama 3.1 8B, ~8B parameters)
• Context length: 128K tokens (published)
• Method: Domain SFT + ORPO, plus MCQ fine-tuning
• License: Llama 3.1 Community License

🎯 Key Features

• Medical domain alignment, fine-tuned to follow medical terminology, clinical reasoning, and exam-style question answering.
• Built on Llama 3.1 8B, inherits the strong general reasoning and instruction-following of Meta’s Llama 3.1 base.
• MCQ-tuned, additional fine-tuning targeted at multiple-choice medical questions.
• Long context, published with a 128K token context window.
• Compact local deployment, a ~2.0 GB GGUF quant runs locally with a single ollama run command, even on modest hardware.

🏷️ Available Versions

Only the latest tag is published (model weights blob = 2,019,377,376 bytes ≈ 2.0 GB). For an 8B-parameter Llama 3.1 model this size corresponds to an aggressive ~2-bit (Q2_K-class) quantization, chosen to keep the footprint small. Expect some quality trade-off versus the full-precision base; for higher fidelity, run the original skumar9/Llama-medx_v3.2 weights or a higher-bit quant.

💻 Quick Start

CLI

ollama run richardyoung/llama-medx_v32

cURL

curl http://localhost:11434/api/chat -d '{
  "model": "richardyoung/llama-medx_v32",
  "messages": [
    { "role": "user", "content": "Explain the pathophysiology of type 2 diabetes." }
  ]
}'

Python

import ollama

response = ollama.chat(
    model="richardyoung/llama-medx_v32",
    messages=[{"role": "user", "content": "Explain the pathophysiology of type 2 diabetes."}],
)
print(response["message"]["content"])

JavaScript

import ollama from "ollama";

const response = await ollama.chat({
  model: "richardyoung/llama-medx_v32",
  messages: [
    { role: "user", content: "Explain the pathophysiology of type 2 diabetes." },
  ],
});
console.log(response.message.content);

🛠️ Use Cases

• Medical education, study aid for students working through clinical concepts and exam-style questions.
• Medical NLP research, a domain-adapted base for experiments in clinical text understanding and generation.
• MCQ exploration, drafting and explaining multiple-choice medical questions and their reasoning.
• Terminology & summarization, explaining medical terminology and summarizing medical literature for review.
• Prototyping, a lightweight, locally runnable medical model for non-clinical development and evaluation.

All use cases are for research and educational purposes only. See the Medical Disclaimer below.

📋 System Requirements

CPU-only inference is possible thanks to the small quant size; roughly 4–6 GB of free system RAM is recommended.

🔧 Technical Details

• Base Model: skumar9/Llama-medx_v3.2
• Underlying base: meta-llama/Llama-3.1-8B
• Parameters: ~8B
• Architecture: Auto-regressive Llama transformer (32 layers, hidden size 4096) with Grouped-Query Attention (GQA, 8 KV heads)
• Context Length: 128K tokens (published)
• Quantization: Low-bit GGUF (~2 bits per weight, Q2_K-class); model weights ≈ 2.0 GB
• Fine-tuning: Supervised Fine-Tuning (SFT) + Odds Ratio Preference Optimization (ORPO), plus MCQ fine-tuning
• License: Llama 3.1 Community License

⚠️ Medical Disclaimer

This model is provided for research and educational purposes only.

• It is NOT a medical device and has not been reviewed, cleared, or approved by the FDA or any other regulatory authority.
• It does NOT provide medical advice and must NOT be used for diagnosis, treatment, or any clinical decision-making.
• Its outputs may be inaccurate, incomplete, outdated, or harmful, and must never be relied upon for real patient care.
• Nothing produced by this model establishes a doctor–patient relationship or substitutes for professional medical judgment.
• Always consult a qualified healthcare professional for any medical concern, diagnosis, or treatment decision. In an emergency, contact your local emergency services immediately.

By using this model you accept full responsibility for how its outputs are used and agree that the maintainers and base-model authors bear no liability for any outcome.

🙏 Acknowledgments

• Base model: skumar9, Llama-medx_v3.2, the medical fine-tune.
• Foundation model: Meta, Llama 3.1 8B, the underlying base.
• Quantization: llama.cpp.

Built & maintained by Richard Young · DeepNeuro