medictron-7B

BioMistral-7B Fine-Tuned on Indian Medical Data

A domain-adapted clinical LLM fine-tuned on synthetic Indian medical Q&A records using QLoRA (4-bit quantization) with Unsloth 2x speedup. Built to power the conversational AI layer.

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Model Details

• Developed by: B. Nikita Reddy
• Model type: Causal LLM — BioMistral-7B + LoRA adapter (PEFT)
• Languages: English, Hindi
• License: MIT
• Base model: BioMistral/BioMistral-7B

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What It Does

• Generates structured treatment recommendations — Allopathy, Homeopathy, Home Remedy
• Supports voice input via Whisper ASR and returns spoken responses via TTS
• Feeds patient vitals and symptom data into an XGBoost-based risk analyzer that scores patient risk from 0–100 (Low / Moderate / High / Critical) with SHAP explainability

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Training Details

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Loss Curve (per checkpoint)

Checkpoints saved at steps 600, 800, and 873.

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How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("BioMistral/BioMistral-7B")
model = PeftModel.from_pretrained(base_model, "YOUR_HF_USERNAME/biomistral-7b-indian-medical")
tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B")

prompt = "Patient reports bukhar (fever) for 2 days. Suggest treatment."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Preview

Limitations

• Not a replacement for licensed medical advice
• Optimised for Indian medical terminology — may underperform in other clinical contexts
• Hindi coverage may not extend to all regional dialects
• synthetic training samples — rare conditions may be underrepresented

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Contact

B. Nikita Reddy

Framework Versions

• PEFT 0.18.1
• Unsloth (latest at training time)
• Transformers (latest at training time)