#1616th of 31 in Training and fine-tuning
oumi
CLI and Python toolkit for fine-tuning, evaluating and deploying foundation models
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- Stars
- 9.4k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Sep 2026
- Language
- Python
8,972 on 1 Apr 20269,410 on 21 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Oumi covers the model lifecycle from one YAML-driven CLI: data synthesis with LLM judges, training (SFT, LoRA, QLoRA, DPO, GRPO), evaluation, and inference through vLLM, SGLang or commercial APIs. Recent versions add tool-calling SFT, executable tool environments and GRPO via verl. Jobs can run locally or be launched on AWS, Azure, GCP, Lambda, Modal or Slurm. The README labels it beta.
Who it is for: ML engineers and researchers fine-tuning and evaluating open models
Strengths
- Ready-made YAML recipes for many model families (Qwen, Gemma, Llama, gpt-oss, Phi)
- Supports SFT, LoRA, QLoRA, DPO, KTO and GRPO, with FSDP, DeepSpeed and DDP
- `oumi launch` runs jobs on AWS, Azure, GCP, Lambda and Slurm
- Includes eval, LLM-as-judge, data synthesis and an `oumi-mcp` MCP server
Weaknesses
- README says beta; some advanced features may change
- Broad scope means many dependencies (Transformers, TRL, vLLM, verl) to keep aligned
- GPU extras needed for most real training; hardware requirements not stated
- Dedicated endpoint deploy targets specific providers (Fireworks, Parasail)
What it needs
- GPU optional
- Docker
- Needs PyTorch, Transformers, TRL, vLLM, SGLang, verl
- Models: Llama, Gemma, Qwen, DeepSeek, GLM
Also in Training and fine-tuning
See all 31| Rank | Project | Score |
|---|---|---|
| 1 | unslothDesktop and web app to run, fine-tune and export open models | 88 out of 100 |
| 2 | deepspeedPyTorch library for distributed training of large models with ZeRO | 84 out of 100 |
| 3 | verlReinforcement learning training library for LLM post-training with PPO and GRPO | 84 out of 100 |
| #4 | peftPython library for LoRA and other parameter-efficient fine-tuning of Transformers models | 84 out of 100 |
| #5 | trlPost-training library with SFT, DPO, GRPO and KTO trainers | 81 out of 100 |