Fine-Tune LLMs Locally With No Code Using AutoTrain Configs
AI Summary
Summary: Fine-Tuning AI Models with Auto Train
- Fine-Tuning AI Models
- Adjust weights and biases of a pre-trained model.
- Requires less data and computational resources.
- Customizes the model for specific tasks or datasets.
- Auto Train Tool by Hugging Face
- Provides a GUI and CLI for local model fine-tuning.
- Simplifies the fine-tuning process.
- Suitable for users with minimal machine learning knowledge.
- Installation and Setup
- Use an Ubuntu system with adequate VRAM and memory.
- Create a virtual environment using Conda.
- Install Auto Train and its prerequisites.
- Clone the Auto Train repository.
- Install PyTorch, TorchVision, and CUDA compiler.
- Auto Train Configs
- Easy-to-understand configurations for training models.
- Include settings for block size, learning rate, batch size, etc.
- Support for quantization and mixed precision training.
- Allow pushing trained models to Hugging Face with user tokens.
- ORPO Technique
- Combines supervised fine-tuning and preference alignment.
- Reduces computational resources and time.
- Simplifies the multi-stage fine-tuning process.
- Running Auto Train
- Execute Auto Train with a configuration file.
- Automatically downloads and fine-tunes the model.
- Uploads the trained model to Hugging Face.
- Conclusion
- Auto Train Advanced is a user-friendly tool for fine-tuning large language models.
- No coding required for the entire process.
- The tool is accessible and beneficial for non-experts in machine learning.