h2o-llmstudio

Welcome to H2O LLM Studio, a framework and no-code GUI designed for
fine-tuning state-of-the-art large language models (LLMs).

Jump to

• With H2O LLM Studio, you can
• Quickstart
• What's New
• Setup
  • Recommended Install
  • Virtual Environments
• Run H2O LLM Studio GUI
• Run H2O LLM Studio GUI using Docker
• Run H2O LLM Studio with command line interface (CLI)
• Troubleshooting
• Data format and example data
• Training your model
• Example: Run on OASST data via CLI
• Model checkpoints
• Documentation
• Contributing
• License

With H2O LLM Studio, you can

• easily and effectively fine-tune LLMs without the need for any coding experience.
• use a graphical user interface (GUI) specially designed for large language models.
• fine-tune any LLM using a large variety of hyperparameters.
• use recent fine-tuning techniques such as Low-Rank Adaptation (LoRA) and 8-bit model training with a low memory footprint.
• use Reinforcement Learning (RL) to fine-tune your model (experimental).
• use advanced evaluation metrics to judge generated answers by the model.
• track and compare your model performance visually. In addition, W&B integration can be used.
• chat with your model and get instant feedback on your model performance.
• easily export your model to the Hugging Face Hub and share it with the community.

Quickstart

For questions, discussing, or just hanging out, come and join our Discord!

Use cloud-based runpod.io instance to run the latest version of H2O LLM Studio with GUI.

Using CLI for fine-tuning LLMs:

What's New

• PR 788 New problem type for Causal Regression Modeling allows to train single target regression data using LLMs.
• PR 747 Fully removed RLHF in favor of DPO/IPO/KTO optimization.
• PR 741 Removing separate max length settings for prompt and answer in favor of a single max_length settings better resembling chat_template functionality from transformers.
• PR 592 Added KTOPairLoss for DPO modeling allowing