CardioMed-LLaMA3.2-1B is a domain-adapted, instruction-tuned language model fine-tuned specifically on heart disease–related medical prompts using LoRA on top of meta-llama/Llama-3.2-1B-Instruct.

This model is designed to generate structured medical abstracts and awareness information about cardiovascular diseases such as stroke, myocardial infarction, hypertension, etc.

✨ Example Usage

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("rajkumar/CardioMed-LLaMA3.2-1B", torch_dtype=torch.float16).cuda()
tokenizer = AutoTokenizer.from_pretrained("rajkumar/CardioMed-LLaMA3.2-1B")

prompt = """### Instruction:
Provide an abstract and awareness information for the following disease: Myocardial Infarction

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🧠 Use Cases

Patient education for cardiovascular conditions
Early awareness chatbots
Clinical NLP augmentation
Health-tech research assistants

🔧 Fine-tuning Details

Base model: meta-llama/Llama-3.2-1B-Instruct
Fine-tuning method: PEFT (LoRA)
LoRA target modules: q_proj, v_proj
Dataset size: 3,209 instruction-response pairs (custom medical JSONL)
Instruction format: Alpaca-style (### Instruction / ### Response)
Max sequence length: 512 tokens
Framework: Hugging Face Transformers + PEFT

🧪 Prompt Format

text

### Instruction:
Provide an abstract and awareness information for the following disease: Stroke

### Response:

Model will generate:

✅ Abstract
✅ Awareness & prevention guidelines
✅ Structured medical info

📄 License

This model is licensed under the MIT License and intended for educational and research purposes only.

Rajkumar57/CardioMed-LLaMA3.2-1B

🫀 CardioMed-LLaMA3.2-1B

✨ Example Usage

🧠 Use Cases

🔧 Fine-tuning Details

🧪 Prompt Format

📄 License

Rajkumar57/
CardioMed-LLaMA3.2-1B