h2o-llmstudio vs MLX-LoRA-Studio
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 — Native Mac app for on-device LLM fine-tuning via mlx-lm-lora: pick a model, choose SFT/LoRA/DPO-family algorithms, watch loss fall live, push to Hugging Face. No cloud, no code.
Same promise — fine-tune without writing code — different homes: H2O LLM Studio is a web UI/Docker framework for GPU boxes; MLX LoRA Studio is a native macOS app for the machine on your desk.
| h2o-llmstudio | MLX-LoRA-Studio | |
|---|---|---|
| Stars | 5.0k | 250 |
| Forks | 538 | 24 |
| Language | Python | Swift |
| License | Apache-2.0 | MIT |
| Last activity | 6 days ago | 4 days ago |
| Topics | training | training, local |
| Curated connections | 4 | 3 |
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.
MLX-LoRA-Studio — the curator's take
The shortest path from base model to tuned adapter on a Mac: no Python env, no GPU rental — a native SwiftUI app driving mlx-lm-lora, with an algorithm menu unusually deep for a GUI (SFT through DPO-family preference methods, FTPO, Dynamic Fine-Tuning, memory-bounded Chunked NLL), live loss curves and direct HF upload. NOT for serious scale: your ceiling is unified memory on one machine — multi-GPU and large post-training runs belong in LLaMA-Factory/TRL territory. Young single-maintainer project (~250 stars) with a quarantine-xattr dance on first launch; Apple Silicon only, by design.