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:

python

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:

python

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:

bibtex

@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.