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Model: suhailult777/MedBrain-0.5B
Source: Original Platform

README.md

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