Instructions to use Rajkumar57/CardioMed-LLaMA3.2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rajkumar57/CardioMed-LLaMA3.2-1B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Rajkumar57/CardioMed-LLaMA3.2-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rajkumar57/CardioMed-LLaMA3.2-1B")
model = AutoModelForCausalLM.from_pretrained("Rajkumar57/CardioMed-LLaMA3.2-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Rajkumar57/CardioMed-LLaMA3.2-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rajkumar57/CardioMed-LLaMA3.2-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rajkumar57/CardioMed-LLaMA3.2-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rajkumar57/CardioMed-LLaMA3.2-1B

SGLang

How to use Rajkumar57/CardioMed-LLaMA3.2-1B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Rajkumar57/CardioMed-LLaMA3.2-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rajkumar57/CardioMed-LLaMA3.2-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Rajkumar57/CardioMed-LLaMA3.2-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rajkumar57/CardioMed-LLaMA3.2-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Rajkumar57/CardioMed-LLaMA3.2-1B with Docker Model Runner:
```
docker model run hf.co/Rajkumar57/CardioMed-LLaMA3.2-1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

🫀 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.

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Safetensors

Model size

1B params

Tensor type

F16

Model tree for Rajkumar57/CardioMed-LLaMA3.2-1B

Base model

meta-llama/Llama-3.2-1B-Instruct

Finetuned

(1729)

this model

Merges

3 models

Quantizations

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