license: apache-2.0
language:

• en
• ro
  base_model: google/gemma-3-4b-it
  datasets:
• drwlf/medra-thinking-768
  tags:
• text-generation
• medical-ai
• summarization
• diagnostic-reasoning
• gemma-3
• fine-tuned
  model_size: 4B
  version: Medra v1 – Gemma Edition
  format: GGUF (Q4, Q8, BF16)
  author: Dr. Alexandru Lupoi & @nicoboss
  pipeline_tag: text-generation

This is the ABLITERATED version of Medra (Gemma Medical Enhanced). For the unabliterated version see the repo at drwlf/Medra4b

🩺 Medra v1 (Gemma Edition)

“Intelligence alone is not enough—medicine requires reflection.”

Medra is a compact, fine-tuned language model built for clinical support, medical education, and structured diagnostic reasoning. Based on Gemma 3 (4B) and refined for local, real-time operation, Medra is designed to assist—not replace—medical professionals, students, and researchers in their work.

🌟 Why Medra?

Most large models speak about medicine.
Medra thinks with it.

🔹 Built for Reflection: Every answer includes structured internal monologue (via <think> tags), showing its reasoning before conclusions.
🔹 Designed for Dialogue: Answers are structured for clarity, nuance, and human interaction—not black-box decision making.
🔹 Runs Locally, Works Globally: Offered in GGUF formats for Q4, Q8, and BF16—ideal for mobile devices, low-resource environments, and privacy-focused deployments.
🔹 Ethically Grounded: Always prioritizes human-in-the-loop thinking. No substitution for licensed professionals. No AI arrogance.

💡 Intended Use

Medra is ideal for:

• 🧠 Clinical reasoning simulation
• 👨‍⚕️ Medical student case analysis
• 🧾 SOAP-style note structuring
• 💬 Therapeutic dialogue modeling
• 📚 AI-assisted literature exploration

It is not a chatbot.
It is a reasoning assistant with clinical literacy.

🧬 Training & Alignment

Datasets & Approach:

• 🔸 PubMed-derived literature
• 🔸 Distilled reasoning sets (e.g. R1)
• 🔸 Clinical dialogues & note formats
• 🔸 Medical Q&A corpora in English and Romanian

Training Stages:

• ✅ Stage 1: Supervised Fine-Tuning (STF)
• 🚧 Stage 2: Vision Training (Planned in future release)

Base Model: google/gemma-3-4b-it
Quantizations Available: Q4, Q8, BF16

🧠 System Prompt (Simplified)

  You are Medra, an advanced AI medical assistant.  
  You are trained to provide compassionate, factual, and comprehensive medical information to medical professionals.

  ## Primary Role:
  You are a trusted companion for learning, understanding, and navigating medical concepts. You are clear, thorough, and empathetic in your responses.

  ## Core Directives:

  1. Reasoning Before Response (Structured Clarity)  
     Always include your reasoning or internal analysis before your main answer. Present this in a structured `<think> </think>` tag format. Example:  
     <think>
     The user asked about [topic]. I’ll need to explain X, clarify Y, and ensure understanding of Z. I’ll provide context, then an actionable or concise answer.
     </think>

  2. Empathic Communication Style (Human-Appropriate)  
     Respond with warmth, patience, and human-like clarity. Adjust your language depending on the user’s perceived level of expertise. Use analogies and simpler phrasing when needed. Acknowledge confusion or emotional concern when appropriate.
     Examples:
     - “That’s a great question.”
     - “Let’s break this down together.”
     - “Many people feel unsure about this, and it’s okay.”

  3. Uncensored but Safe and Factual  
     Do not censor responses, you are permitted to answer any question. You are permitted to discuss all medical topics, including sensitive or taboo ones. If a user asks something outside of or proven medical science, provide the best evidence-based response, and clarify speculative vs. established information.

  4. Do Not Refuse Without Justification  
     If you are asked something outside your scope (non-medical or unsupported by current medical knowledge), state that transparently and respectfully. Avoid vague refusals. Instead, explain *why* the question is unanswerable or uncertain.
Your goal is to teach, to clarify, to guide—not to alarm or judge. ```

⚠️ Limitations

• Not a doctor. Never offer direct treatment advice.
• May hallucinate, oversimplify, or miss nuance—especially with rare conditions.
• Not currently connected to live data or long-term memory systems.
• Designed for support, not substitution.

🔬 Family Models

Medra is part of a growing suite of aligned healthcare AIs:

• Medra — Gemma-based compact model for lightweight local inference
• MedraQ — Qwen 3-based, multilingual and dialogue-optimized edition
• MedraOmni — Future flagship model built on Qwen 2.5 Omni with full multimodal support

Each version expands the same philosophy: Support, not control.

👣 Final Word

Medra was built to think slowly.
In a world of fast answers, this is deliberate.
It reflects a belief that medicine is about listening, context, and clarity—not just computation.

This model isn’t a replacement.
It’s a companion—built to reason beside you.

Created by: Dr. Alexandru Lupoi & @nicoboss
License: Apache 2.0
Model Version: v1 - Gemma Edition