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README.md

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+---
+base_model: meta-llama/Meta-Llama-3-8B
+library_name: peft
+license: llama3
+language:
+- en
+- zh
+tags:
+- lora
+- sft
+- medical
+- llama3
+- transformers
+- trl
+pipeline_tag: text-generation
+---
+
+# 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:
+
+1. Install required packages:
+```bash
+pip install transformers peft torch
+```
+
+2. 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))
+```
+
+3. 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.