$ show h2oai/h2o-llmstudio
H2O LLM Studio is a web-based application for fine-tuning large language models (LLMs) without requiring coding experience. It provides a GUI built on H2O Wave and CLI tools for dataset import, experiment configuration, model training, evaluation, and export to Hugging Face Hub. The system supports multiple problem types including causal language modeling, classification, regression, sequence-to-sequence, and Direct Preference Optimization (DPO). The architecture centers around a Wave server handling both UI and backend logic, with distributed training capabilities via DeepSpeed. Key components include a configuration system using dataclasses, utilities for conversation chain handling, model loading abstractions, and factory patterns for training components.
The LLM identified 6 files worth highlighting for reuse:
Utility for grouping dataset rows into conversation chains using parent_id. Isolated and reusable in any project handling multi-turn dialogue data.
Base configuration class using dataclasses with support for UI generation (visibility, dependencies). Demonstrates a pattern for creating configurable systems with automatic UI.
PatchedAttribute context manager to temporarily patch an attribute and restore it. Useful for testing and mocking in a safe, isolated manner.
create_nlp_backbone function loads Hugging Face models with support for quantization, LoRA, and other PEFT methods. Encapsulates complex model loading logic into a reusable utility.
Custom Cache class providing a lightweight persistent key-value store. Reusable for storing application state without external dependencies.
Optimizer factory class that instantiates optimizers based on configuration. Demonstrates a clean factory pattern for decoupling configuration from instantiation, reusable across training pipelines.