H2O LLM Studio provides a no-code GUI and framework for fine-tuning large language models, targeting users who want to customize LLMs without extensive coding. It simplifies the process of adapting state-of-the-art models using various hyperparameters and modern techniques like LoRA and 8-bit training, enabling efficient model customization and evaluation.

How It Works

The framework supports fine-tuning LLMs using techniques such as Low-Rank Adaptation (LoRA) and 8-bit quantization for reduced memory footprint. It also incorporates Reinforcement Learning (RL) alternatives like Direct Preference Optimization (DPO), Identity Preference Optimization (IPO), and KTO for model tuning based on preference data. The system offers advanced evaluation metrics, visual performance tracking, and integrations with Neptune and Weights & Biases.

Quick Start & Requirements

• Install: Recommended: pipenv install via make setup. Alternative: make setup-no-flash (disables Flash Attention 2). Docker installation is also available.
• Prerequisites: Ubuntu 16.04+, NVIDIA GPU (>= 470.57.02 drivers), Python 3.10 recommended. For larger models, 24GB+ GPU memory is advised. CUDA 12.1 or 12.4 is recommended for DeepSpeed.
• Resources: Setup involves installing Python, drivers, and dependencies. Docker requires NVIDIA Container Toolkit.
• Links: Documentation: https://docs.h2o.ai/h2o-llmstudio/

Highlighted Details

• Supports Causal Regression and Classification modeling.
• Integrates DeepSpeed for distributed training on multi-GPU setups (requires NVLink).
• Offers CLI for fine-tuning, interactive chat, and Hugging Face Hub publishing.
• Recent updates include DPO/IPO/KTO optimization and removal of RLHF.

Maintenance & Community

• Active development with recent PRs addressing new features and deprecations.
• Discord server available for community discussion.
• Model checkpoints and datasets are available on H2O.ai's Hugging Face page.

Licensing & Compatibility

• Licensed under Apache 2.0.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The project notes that due to rapid development, full backward compatibility is not guaranteed, and users are advised to pin versions. RLHF is being deprecated and will be fully removed in a future release.