Discover top open-source software, updated regularly with real-world adoption signals.
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.
When teams consider H2O LLM Studio, these hosted platforms usually appear on the same shortlist.
ML hub with curated foundation models, pretrained algorithms, and solution templates you can deploy and fine-tune in SageMaker
Enterprise AI platform providing LLMs (Command, Aya) plus Embed/Rerank for retrieval
API-first platform to run, fine-tune, and deploy AI models without managing infrastructure
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
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.
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.
Direct installation is supported only on Linux, but you can run the Docker image via WSL 2 or a Linux VM.
Models can be pushed to the Hugging Face Hub with a single click or downloaded as a local checkpoint.
RL features are marked experimental; they are suitable for exploration but not yet recommended for production workloads.
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
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