🩺 Yukt-Med (Phi-2 Medical Assistant)

Your Compact, Specialized Medical Knowledge Companion.

Fine-tuned on 86,000+ curated medical interactions to provide concise, accurate, and non-diagnostic healthcare information.

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🌟 Overview

Yukt-Med is a lightweight, state-of-the-art language model designed for the medical and healthcare domain. It is fine-tuned using LoRA (Low-Rank Adaptation) on a diverse collection of healthcare datasets.

What makes Yukt-Med unique is its balance of performance and efficiency. While powerful, it has been quantized to 4-bit GGUF, making it runnable on commodity hardware, mobile devices, and in offline environments.

💡 Perfect for: Rapid medical information retrieval, symptom analysis support, and educational purposes. Not for diagnosis.

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🚀 Key Features

Feature	Description
🧠 Specialized Brain	Trained on ChatDoctor, MedQuad, and curated drug databases.
⚡ Ultra-Efficient	GGUF version runs smoothly on 4GB RAM (CPU/Mobile).
Instruction-Following	Responds accurately to instructions using a specific prompt template.
🌐 Production-Ready	Available in Standard Safetensors and Compact GGUF formats.

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📊 Training Data

The model's knowledge comes from over 86,800 cleaned and structured examples:

1. ChatDoctor Dataset: Real-world patient-doctor dialogues for conversational medical advice.
2. MedQuad Dataset: Large-scale Medical Question-Answering pairs.
3. Drugs & Side Effects: Detailed information on pharmaceuticals.
4. Symptom-Disease Mapping: Patterns for common medical conditions.

(Note: Data was filtered to ensure high-quality, safe, and factual content.)

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🧪 Evaluation Examples

Compare Yukt-Med's focused medical responses:

Instruction (Prompt)	Yukt-Med Response (Generation)
### Instruction: What are the common symptoms of a common cold? ### Response:	The common symptoms of a common cold include a runny or stuffy nose, sneezing, sore throat, cough, and mild body aches or fatigue.
### Instruction: Tell me about the side effects of Aspirin. ### Response:	The most frequent side effects are bleeding and stomach upset.

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🛠️ Usage Instructions

1. Using Standard Transformers (Python/GPU)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "ayuag/yukt-med"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)

def ask_medical_bot(question):
    prompt = f"### Instruction: {question}\n### Response:"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(ask_medical_bot("What are symptoms of Malaria?"))