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Oumi
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.
Overview
Overview
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.
Capabilities
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.
Deployment
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.
Highlights
Zero‑boilerplate recipes for popular models and workflows
Native DeepSpeed, FSDP, and vLLM/SGLang integration
LLM‑as‑a‑Judge for automated data curation
Unified API for training, evaluation, and inference across clouds
Pros
- Broad model support from 10M to 405B parameters
- Flexible deployment on laptops, clusters, and major clouds
- Active community and enterprise‑grade reliability
- Extensible CLI and Python API
Considerations
- Beta status; some advanced features may change
- GPU support requires appropriate drivers and the `oumi[gpu]` install
- Custom distributed setups can have a steep learning curve
- Documentation may lag behind rapid releases
Managed products teams compare with
When teams consider Oumi, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fit guide
Great for
- Researchers needing reproducible experiments
- Enterprises scaling large‑scale model training
- Developers building multimodal applications
- Teams wanting a single stack from data prep to deployment
Not ideal when
- Beginners seeking a plug‑and‑play UI only
- Projects limited to CPU‑only environments beyond small models
- Users requiring strict commercial licensing guarantees
- Teams needing fully managed SaaS inference services
How teams use it
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.
Tech snapshot
Python86%
Jupyter Notebook12%
Shell1%
Jinja1%
Makefile1%
Dockerfile1%
Frequently asked questions
What hardware is required for GPU training?
Oumi supports Nvidia and AMD GPUs; install the `oumi[gpu]` extra to enable CUDA or ROCm acceleration.
Can I use Oumi with proprietary models?
Yes, Oumi’s API works with both open models and commercial APIs such as OpenAI, Anthropic, Vertex AI, Together, and Parasail.
How does Oumi handle distributed training?
It provides native integrations for DeepSpeed, FSDP, and DDP, configurable via recipe YAML files.
Is there a cloud‑managed service?
Oumi is a toolkit; you launch jobs on your own cloud accounts (AWS, Azure, GCP, Lambda) using the `oumi launch` command.
Where can I find example recipes?
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
Active- Stars
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- Watchers
- 8,829
- Forks
- 694
LicenseApache-2.0
Repo age1 year old
Last commityesterday
Primary languagePython
Last synced yesterday