README.md

---
license: mit
datasets:
- sflagg/Kaggle-Mental-Health-Survey-Data
language:
- en
base_model:
- TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-classification
library_name: transformers
tags:
- medical
---
# 🧠 CareMinds-AI — Medical AI Assistant

CareMinds-AI is a lightweight, offline-capable medical AI system built fine-tuned with domain-specific healthcare data. It combines:

- ✅ LLM (TinyLLaMA + LoRA fine-tuning)
- ✅ Structured Data Analytics (CSV + Pandas)
- ✅ RAG-style context retrieval (lightweight)
- ✅ Patient record lookup system

---

# 📌 Model Details

## 📖 Model Description

CareMinds-AI is a hybrid AI system designed to:

- Answer general medical questions  
- Analyze structured healthcare datasets  
- Retrieve patient-specific records  
- Perform real-time analytics using natural language  

It operates fully **offline**, without requiring external APIs.

---

### 👨‍💻 Developed by
Vedhamani Prabakar A

### 🧠 Model Type
- Base Model: CareMinds-AI (1.1B parameters)
- Fine-tuning: LoRA (PEFT)
- Architecture: Transformer-based causal language model

### 🌐 Language(s)
- English

---

## 🔗 Model Sources

- GitHub Repository:  
  https://github.com/VedhamaniprabakarA/CareMinds-AI.git

---

# 🚀 Uses

## ✅ Direct Use

CareMinds-AI can be used as:

- 🧠 Medical chatbot (offline)
- 📊 Healthcare data analyzer
- 🏥 Patient record retrieval system
- 🔍 Natural language query engine

---

## 🔄 Downstream Use

- Hospital management systems  
- Healthcare dashboards  
- Clinical data assistants  
- AI-powered analytics tools  

---

## 🧠 Recommendations

- Use alongside verified medical systems  
- Add RAG with trusted medical sources for production  
- Validate outputs before real-world use  

---

# ⚙️ How to Use the Model

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_path = "./CareMinds-AI"

model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

prompt = """### Instruction:
Explain about diabetes

### Response:
"""
inputs = tokenizer(prompt, return_tensors="pt").to(device)

outputs = model.generate(**inputs, max_new_tokens=150)

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


###test the model:
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("vedhamani/CareMinds-AI")
tokenizer = AutoTokenizer.from_pretrained("vedhamani/CareMinds-AI")

prompt = "### Instruction:\nExplain about diabetes\n\n### Response:\n"

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)

print(tokenizer.decode(outputs[0]))