language, license, tags, datasets, pipeline_tag

language	license	tags	datasets	pipeline_tag
en	apache-2.0	medical healthcare text-generation jax pytorch	Mohammed-Altaf/medical-instruction-100k	text-generation

🩺 MedBrain-0.5B

MedBrain-0.5B is a highly efficient, custom-trained medical language model designed to provide accurate, structured, and context-aware responses to healthcare inquiries, clinical handoffs, and patient education instructions.

Originally trained meticulously in a pure Google JAX / Flax environment for extreme performance, the weights have been seamlessly merged and optimized into a standard PyTorch parameter format for universal compatibility and instant deployment.

🚀 Quick Start

You can run this model instantly using standard Hugging Face transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "suhailult777/MedBrain-0.5B"

# Load Tokenizer and Model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32)

# Format your medical prompt
prompt = "A patient presents with sudden shortness of breath and left-sided chest pain. What are the immediate triage steps?"
formatted_prompt = f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"

# Run inference
inputs = tokenizer(formatted_prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)

print(tokenizer.decode(outputs[0], skip_special_tokens=False))

🧠 Architecture & Methodology

• Parameter Count: ~0.5 Billion parameters
• Optimization Strategy: Low-Rank Adaptation (LoRA) - Rank 16, Alpha 16
• Training Infrastructure: Custom JAX native dynamic loop utilizing Optax schedulers.
• Base Architecture Mapping: Transformer-based Causal LM.
• Dataset: Fine-tuned on the structured Mohammed-Altaf/medical-instruction-100k corpus, which provides vast arrays of physician-patient interactions.

🛠️ Intended Use

1. Medical Triage Assistance: Assisting clinicians in organizing thoughts around symptoms.
2. Clinical Handoff Generators: Structuring patient handoff notes quickly.
3. Patient Education: Formatting complex medical jargon into easy-to-understand explanations.

⚠️ Limitations & Clinical Warning

This model is built as an experimental research artifact. It should never be used for clinical decision-making, raw diagnostic purposes, or serving as a replacement for a licensed healthcare professional. LLMs can hallucinate confidently. Always consult a certified physician for medical advice.