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H2O LLM Studio
No-code GUI for fine-tuning large language models effortlessly
H2O LLM Studio lets users fine-tune state-of-the-art LLMs via an intuitive graphical interface, supporting LoRA, 8-bit training, DPO optimization, visual performance tracking, and seamless export to Hugging Face.
Overview
Highlights
No‑code graphical interface for end‑to‑end fine‑tuning
Supports LoRA, 8‑bit training, and DPO/IPO/KTO optimizations
Visual experiment tracking with built‑in evaluation metrics
One‑click export to Hugging Face and integration with Neptune/W&B
Pros
- Intuitive UI lowers the barrier for model customization
- Low‑memory techniques allow training on modest GPUs
- Extensive hyperparameter control for research flexibility
- Seamless integration with popular logging and model hubs
Considerations
- Requires Ubuntu/Linux and an NVIDIA GPU with sufficient VRAM
- Limited support for Windows‑only environments
- Experimental RL features may be unstable
- Rapid development can affect backward compatibility
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Fit guide
Great for
- Data scientists who prefer a visual workflow over coding
- ML engineers needing rapid prototyping of LLM adaptations
- Researchers exploring LoRA, DPO, or other fine‑tuning methods
- Teams that want visual performance dashboards and easy model sharing
Not ideal when
- Users limited to Windows without WSL or Docker
- Environments with GPUs smaller than 12 GB VRAM
- Production pipelines that require fully automated CI/CD
- Projects needing a stable, long‑term API without frequent changes
How teams use it
Domain‑specific chatbot fine‑tuning
Deploy a customized assistant that answers company‑specific FAQs with higher relevance.
Academic research on instruction following
Rapidly compare LoRA and DPO techniques and publish benchmark results.
Low‑resource model adaptation
Train a 7B model using 8‑bit precision on a 24 GB GPU, reducing compute cost.
Model evaluation dashboard
Visually compare multiple checkpoints and select the best‑performing version for deployment.
Tech snapshot
Python99%
Makefile1%
Dockerfile1%
Gherkin1%
Shell1%
Frequently asked questions
What hardware is required to run H2O LLM Studio?
An Ubuntu 16.04+ system with an NVIDIA GPU (driver ≥ 470.57.02) and at least 12 GB VRAM; 24 GB is recommended for larger models.
Can I use the platform on Windows?
Direct installation is supported only on Linux, but you can run the Docker image via WSL 2 or a Linux VM.
How do I export a fine‑tuned model?
Models can be pushed to the Hugging Face Hub with a single click or downloaded as a local checkpoint.
Is reinforcement learning stable for production use?
RL features are marked experimental; they are suitable for exploration but not yet recommended for production workloads.
Do I need to install CUDA manually?
If you run on bare metal, you must install the appropriate NVIDIA drivers and CUDA toolkit; the Docker image includes the necessary runtime.
Project at a glance
Active- Stars
- 4,777
- Watchers
- 4,777
- Forks
- 508
LicenseApache-2.0
Repo age2 years old
Last commit2 days ago
Primary languagePython
Last synced 3 hours ago