metadata
language:
- en
tags:
- medical
- llama
- heart-disease
- healthcare
- instruction-tuned
- awareness
- causal-lm
model_name: CardioMed-LLaMA3.2-1B
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets:
- custom
library_name: transformers
pipeline_tag: text-generation
🫀 CardioMed-LLaMA3.2-1B
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
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
### 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.