Spaces:
Sleeping
title: SLM Instruction Tuning using Unsloth
emoji: 🐨
colorFrom: yellow
colorTo: indigo
sdk: gradio
sdk_version: 4.38.1
app_file: app.py
pinned: false
license: apache-2.0
LoRA Instruction Tuning using Unsloth
What?
This Gradio app is a simple interface to access unsloth AI's fine-tuning methods hosted on Huggingface Spaces. This outputs of this fine-tuning will be instruction tuned LoRA weights that will be uploaded into your personal huggingface models repository. This is more for personal use since the A100 GPUs only last for a stated runtime (~60 seconds or more if specified).
Why?
The goal of this demo is to show how you can tune your own language models leveraging industry standard compute and fine tuning methods using a simple point-and-click UI.
This is a demo and not a production application and is hosted here simply to . This application is subject a demand queue.
How?
Just start by following the guide below:
- Navigate through the tabs at the top from left to right.
- Basic Setup: Populate your username, repository, token, and model details.
- Upload data: Either from transformers or your local jsonl file. Please view this guide for best practices.
- Fine-tune Model: Eat a snack and wait as you train the model for your use case.
For GPU runtimes longer than a minute, remove the imports to huggingface spaces and decorators and run on your local GPU or migrate this work to a workspace like lightning AI.
Coming soon!
- More models and added flexibility with guardrails on hyperparameter tuning.
- Downloads for a WandB logger for training monitoring.
Other resources.
- Unsloth's notebooks to look at what is going on under the hood.
