Instructions to use YBXL/Med-LLaMA3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YBXL/Med-LLaMA3-8B with Transformers:

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

pipe = pipeline("text-generation", model="YBXL/Med-LLaMA3-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YBXL/Med-LLaMA3-8B")
model = AutoModelForCausalLM.from_pretrained("YBXL/Med-LLaMA3-8B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use YBXL/Med-LLaMA3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YBXL/Med-LLaMA3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YBXL/Med-LLaMA3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/YBXL/Med-LLaMA3-8B

SGLang

How to use YBXL/Med-LLaMA3-8B 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 "YBXL/Med-LLaMA3-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YBXL/Med-LLaMA3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "YBXL/Med-LLaMA3-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YBXL/Med-LLaMA3-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use YBXL/Med-LLaMA3-8B with Docker Model Runner:
```
docker model run hf.co/YBXL/Med-LLaMA3-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Med-LLaMA3-8B

Model Details

Model Description

Med-LLaMA3-8B is an 8-billion parameter medical language model that has undergone continual pre-training on LLaMA3-8B architecture using large-scale open-sourced medical data.

Training Details

Med-LLaMA3-8B is trained on a large-scale dataset comprising: medical books, medical literature, clinical guidelines and a small portion of general domain data It is a study extension based on our previous Me-LLaMA paper: https://arxiv.org/pdf/2402.12749

If you use the model, please cite the following papers:

@misc{xie2024llama,
      title={Me LLaMA: Foundation Large Language Models for Medical Applications}, 
      author={Qianqian Xie and Qingyu Chen and Aokun Chen and Cheng Peng and Yan Hu and Fongci Lin and Xueqing Peng and Jimin Huang and Jeffrey Zhang and Vipina Keloth and Huan He and Lucila Ohno-Machido and Yonghui Wu and Hua Xu and Jiang Bian},
      year={2024},
      eprint={2402.12749},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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Safetensors

Model size

8B params

Tensor type

F32

Model tree for YBXL/Med-LLaMA3-8B

Finetunes

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Quantizations

2 models

Paper for YBXL/Med-LLaMA3-8B

Me LLaMA: Foundation Large Language Models for Medical Applications

Paper • 2402.12749 • Published Feb 20, 2024 • 2