Instructions to use bootscoder/Llama-3-Medical-8B-SFT-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bootscoder/Llama-3-Medical-8B-SFT-LoRA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/home/haoxuan004/LLaMA-8B-DeepSpeed/output/pretrained-full")
model = PeftModel.from_pretrained(base_model, "bootscoder/Llama-3-Medical-8B-SFT-LoRA")

Transformers

How to use bootscoder/Llama-3-Medical-8B-SFT-LoRA with Transformers:

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

pipe = pipeline("text-generation", model="bootscoder/Llama-3-Medical-8B-SFT-LoRA")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bootscoder/Llama-3-Medical-8B-SFT-LoRA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bootscoder/Llama-3-Medical-8B-SFT-LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bootscoder/Llama-3-Medical-8B-SFT-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bootscoder/Llama-3-Medical-8B-SFT-LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bootscoder/Llama-3-Medical-8B-SFT-LoRA

SGLang

How to use bootscoder/Llama-3-Medical-8B-SFT-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 "bootscoder/Llama-3-Medical-8B-SFT-LoRA" \
    --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": "bootscoder/Llama-3-Medical-8B-SFT-LoRA",
		"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 "bootscoder/Llama-3-Medical-8B-SFT-LoRA" \
        --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": "bootscoder/Llama-3-Medical-8B-SFT-LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bootscoder/Llama-3-Medical-8B-SFT-LoRA with Docker Model Runner:
```
docker model run hf.co/bootscoder/Llama-3-Medical-8B-SFT-LoRA
```

Llama-3-Medical-8B-SFT-LoRA

This is a LoRA adapter for Meta-Llama-3-8B, fine-tuned on medical domain data using Supervised Fine-Tuning (SFT).

Model Details

Base Model: Meta-Llama-3-8B
Training Method: QLoRA (Quantized Low-Rank Adaptation)
Training Framework: TRL + DeepSpeed + PEFT
Domain: Medical
Languages: English and Chinese
License: Llama 3 License

Training Details

This LoRA adapter was trained using:

Method: Supervised Fine-Tuning (SFT) with QLoRA
Framework: Hugging Face TRL, PEFT, DeepSpeed
Data: Medical domain datasets including medical Q&A, clinical notes, and medical knowledge
LoRA Rank: Check adapter_config.json for details
Training Precision: Mixed precision (bf16/fp16)

Usage

To use this LoRA adapter, you need to:

Install required packages:

pip install transformers peft torch

Load the model with LoRA adapter:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = "meta-llama/Meta-Llama-3-8B"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, "bootscoder/Llama-3-Medical-8B-SFT-LoRA")

# Generate text
inputs = tokenizer("What is diabetes?", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Or merge LoRA with base model:

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = "meta-llama/Meta-Llama-3-8B"
model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto")
model = PeftModel.from_pretrained(model, "bootscoder/Llama-3-Medical-8B-SFT-LoRA")

# Merge and save
merged_model = model.merge_and_unload()
merged_model.save_pretrained("./merged_model")

Intended Use

This model is intended for:

Medical question answering
Medical text generation
Research and educational purposes in healthcare domain

Limitations

This model is for research purposes only
Should not be used for clinical decision-making without professional medical oversight
May generate inaccurate or hallucinated medical information
Requires careful validation before any real-world application

Training Infrastructure

Hardware: NVIDIA GPUs with DeepSpeed optimization
Software: PyTorch, Transformers, PEFT 0.17.1, TRL, DeepSpeed

Citation

If you use this model, please cite:

@misc{llama3-medical-8b-sft-lora,
  author = {bootscoder},
  title = {Llama-3-Medical-8B-SFT-LoRA},
  year = {2024},
  publisher = {HuggingFace},
  url = {https://huggingface.co/bootscoder/Llama-3-Medical-8B-SFT-LoRA}
}

Disclaimer

This model is provided as-is for research and educational purposes. The outputs should not be used as medical advice. Always consult with qualified healthcare professionals for medical decisions.

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