LLMTuner: Fine-Tune Llama, Whisper, and other LLMs with best practices like LoRA, QLoRA, through a sleek, scikit-learn-inspired interface.
This repository is tested on Python 3.7+
You should install Promptify using Pip command
pip3 install git+https://github.com/promptslab/LLMTuner.gitTo fine a Large models we provide the Tuner API.
from llmtuner import Tuner, Dataset, Model, Deployment
# Initialize the Whisper model with parameter-efficient fine-tuning
model = Model("openai/whisper-small", use_peft=True)
# Create a dataset instance for the audio files
dataset = Dataset('/path/to/audio_folder')
# Set up the tuner with the model and dataset for fine-tuning
tuner = Tuner(model, dataset)
# Fine-tune the model
trained_model = tuner.fit()
# Inference with Fine-tuned model
tuner.inference('sample.wav')
# Launch an interactive UI for the fine-tuned model
tuner.launch_ui('Model Demo UI')
# Set up deployment for the fine-tuned model
deploy = Deployment('aws') # Options: 'fastapi', 'aws', 'gcp', etc.
# Launch the model deployment
deploy.launch()- 🏋️♂️ Effortless Fine-Tuning: Finetune state-of-the-art LLMs like Whisper, Llama with minimal code
- ⚡️ Built-in utilities for techniques like LoRA and QLoRA
- ⚡️ Interactive UI: Launch webapp demos for your finetuned models with one click
- 🏎️ Simplified Inference: Fast inference without separate code
- 🌐 Deployment Readiness: (Coming Soon) Deploy your models with minimal effort to aws, gcp etc, ready to share with the world.
| Task Name | Colab Notebook | Status |
|---|---|---|
| Fine-Tune Whisper | Fine-Tune Whisper | ✅ |
| Fine-Tune Whisper Quantized | LoRA | ✅ |
| Fine-Tune Llama | Coming soon.. | ✅ |
If you are interested in Fine-tuning Open source LLMs, Building scalable Large models, Prompt-Engineering, and other latest research discussions, please consider joining PromptsLab
@misc{LLMtuner2023,
title = {LLMTuner: Fine-Tune Llama, Whisper, and other Large Models with best practices like LoRA, QLoRA, through a sleek, scikit-learn-inspired interface.},
author = {Pal, Ankit},
year = {2023},
howpublished = {\url{https://github.com/promptslab/LLMtuner}}
}
We welcome any contributions to our open source project, including new features, improvements to infrastructure, and more comprehensive documentation. Please see the contributing guidelines