Instructions to use calebking/llama-3.2-3b-instruct-medical-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use calebking/llama-3.2-3b-instruct-medical-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "calebking/llama-3.2-3b-instruct-medical-lora")

Transformers

How to use calebking/llama-3.2-3b-instruct-medical-lora with Transformers:

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

pipe = pipeline("text-generation", model="calebking/llama-3.2-3b-instruct-medical-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("calebking/llama-3.2-3b-instruct-medical-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use calebking/llama-3.2-3b-instruct-medical-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "calebking/llama-3.2-3b-instruct-medical-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "calebking/llama-3.2-3b-instruct-medical-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/calebking/llama-3.2-3b-instruct-medical-lora

SGLang

How to use calebking/llama-3.2-3b-instruct-medical-lora 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 "calebking/llama-3.2-3b-instruct-medical-lora" \
    --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": "calebking/llama-3.2-3b-instruct-medical-lora",
		"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 "calebking/llama-3.2-3b-instruct-medical-lora" \
        --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": "calebking/llama-3.2-3b-instruct-medical-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use calebking/llama-3.2-3b-instruct-medical-lora with Docker Model Runner:
```
docker model run hf.co/calebking/llama-3.2-3b-instruct-medical-lora
```

LLaMA-3.2-3B-Instruct Medical LoRA

Built with Llama

LoRA adapter for LLaMA-3.2-3B-Instruct fine-tuned on medical instruction datasets to improve medical quantitative reasoning.

Model Details

Base model: meta-llama/Llama-3.2-3B-Instruct
Developed by: Caleb King
Type: Text generation / Instruction fine-tuned language model
License: llama3.2
Language: English

Training

Dataset: MedInstruct (combined from BioInstruct and AlpaCare-MedInstruct-52k)
Method: LoRA (Low-Rank Adaptation)
Framework: PEFT 0.18.1

Repository

https://github.com/CalebKingPortfolio/MQR-LLM-Thesis

License

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Model tree for calebking/llama-3.2-3b-instruct-medical-lora

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