Discover top open-source software, updated regularly with real-world adoption signals.
End-to-end platform for building, training, and deploying foundation models
A unified toolkit that streamlines data preparation, model fine-tuning, evaluation, and production inference for text and multimodal foundation models across laptops, clusters, and cloud environments.
Oumi is a comprehensive, open‑source stack that covers the full lifecycle of foundation models. It lets researchers and engineers move from raw data to a deployed model with a single, consistent API, eliminating the need to stitch together disparate tools.
The platform supports models ranging from 10 M to 405 B parameters, offering state‑of‑the‑art fine‑tuning methods (LoRA, QLoRA, GRPO, etc.) and distributed training back‑ends such as DeepSpeed, FSDP, and DDP. Built‑in LLM‑as‑a‑Judge utilities enable automated data curation, while integrated inference engines (vLLM, SGLang) provide low‑latency serving for both text‑only and multimodal models.
Oumi runs anywhere—from a local laptop to large GPU clusters and major cloud providers (AWS, Azure, GCP, Lambda). Jobs are launched via the oumi launch CLI, preserving experiment metadata and allowing seamless scaling without code changes.
When teams consider Oumi, 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.
Fine‑tune a 70B Llama model on a custom dataset
Achieve domain‑specific performance with LoRA/QLoRA in hours using DeepSpeed on a cloud GPU cluster.
Curate training data with LLM judges
Automatically filter noisy text using the built‑in LLM‑as‑a‑Judge, improving downstream model quality.
Deploy a multimodal vision‑language model for inference
Serve real‑time predictions via vLLM or SGLang on AWS, handling image‑text inputs with low latency.
Run reproducible experiments across local and cloud
Switch from a laptop to GCP or Lambda with a single CLI command, preserving experiment metadata.
Oumi supports Nvidia and AMD GPUs; install the `oumi[gpu]` extra to enable CUDA or ROCm acceleration.
Yes, Oumi’s API works with both open models and commercial APIs such as OpenAI, Anthropic, Vertex AI, Together, and Parasail.
It provides native integrations for DeepSpeed, FSDP, and DDP, configurable via recipe YAML files.
Oumi is a toolkit; you launch jobs on your own cloud accounts (AWS, Azure, GCP, Lambda) using the `oumi launch` command.
The repository includes a growing collection of ready‑to‑use configurations for models like Llama, Qwen, Falcon, and vision‑language models; see the docs and quickstart guide.
Project at a glance
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