🧠Oumi
Easily fine-tune, evaluate and deploy Qwen, Gemma, or any open weight LLM!
⭐ Popular: 9k stars, gaining about 6 a week
View on GitHub ↗repo profile
momentum
durability
bus factor = how many people it takes to cover more than half the commits (6 months). 1 is a solo project; higher means the work is spread across a team. top-author share is the single busiest author's slice of those commits.
since we covered it
why it's a big deal
- Enables training and fine-tuning of large-scale AI models with support for techniques like LoRA, QLoRA, and DPO.
- Works across multiple model architectures, including Llama, DeepSeek, Qwen, and Phi.
- Integrates seamlessly with cloud providers (AWS, Azure, GCP, Lambda) for remote job execution.
under the hood
- Supports zero-boilerplate configuration for fine-tuning, distillation, and benchmarking.
- Includes native tools for LLM-as-a-judge, data synthesis, and structured evaluation.
- Runs efficiently on GPUs and NPUs, leveraging distributed training techniques.Designed for both research and enterprise AI model development.
our take from PR#2, 2025-02-19
star history
- PR#2 7k 2025-02-19
- now 9k + 2k since first covered
curve is sampled from GitHub's star history, plus our own daily readings since we covered it; the dashed stretch is before we first covered it, the solid line since. figures at coverage are the numbers we printed then (approx.), current count is live.
understory
Quietly building: more output than attention, for now.
- output, commits & releases
- clout, star velocity
output = commits & releases; clout = star velocity, both 0 to 100 monthly indices; the gap where output runs above clout is the understory. The understory →
covered in
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The End-to-End Platform for Training AI Foundation Models
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