h2o-llmstudio vs LlamaFactory
H2O's no-code GUI and framework for fine-tuning LLMs — LoRA, 8-bit, DPO and experiment tracking behind a web UI, with CLI and Docker paths for the same configs. — versus — The unified fine-tuning framework: 100+ LLMs and VLMs via LoRA/QLoRA/full-parameter, config-driven or through the LlamaBoard GUI. ACL 2024, 1000+ citations, 73k stars.
Both put a GUI on fine-tuning; LLM Studio optimizes for the no-code experience, LlamaFactory for maximum model/method coverage with the GUI as one of several front doors.
| h2o-llmstudio | LlamaFactory | |
|---|---|---|
| Stars | 5.0k | 73k |
| Forks | 538 | 9.0k |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 3 days ago | 2 days ago |
| Topics | training | training |
| Curated connections | 3 | 4 |
h2o-llmstudio — the curator's take
Fine-tuning for teams where not everyone writes training loops: pick a base model, upload data, tune LoRA/quantization/DPO hyperparameters in a web UI, compare runs visually, export to the Hub. The CLI runs the same configs headless, so GUI experiments graduate to scripted jobs. NOT for RL post-training at scale (its RL is experimental — that's verl/slime territory) and not for frontier-size models; and it's opinionated toward the H2O ecosystem — if your workflow is already Hub-native code, a library fits better than a studio.
LlamaFactory — the curator's take
The default answer to 'how do I fine-tune model X': whatever the architecture (Llama, Qwen, Mistral, VLMs…), whatever the method (LoRA, QLoRA, DPO, PPO, full), one YAML config or the LlamaBoard GUI runs it — with the broadest model-coverage matrix in open source and academic citation weight behind it. If TRL is the library you code against, LlamaFactory is the trainer you configure. NOT for frontier-scale RL dataflows (verl/slime territory), and the kitchen-sink coverage means version bumps occasionally break niche model+method combos — pin versions for anything long-running.