oumi

oumi-aiApache-2.0活跃维护

一站式搞定 Qwen、Gemma 等开源大模型和多模态模型的微调（SFT/RL）、评测与部署，零代码也能上手。

Easily fine-tune (SFT/RL), evaluate, and deploy Qwen, Gemma, or any open agentic LLM/VLM!

v0.9发布于 2026-09-16 21:48 UTC9,431star +2 / 7 天798fork同步于 2026-11-10 23:31 UTC

零代码微调大模型微调模型评测模型部署强化学习

对比 GitHub 官网 agentic-ai dpo fine-tuning gemma gpt-oss

怎么上手

由 README 提炼，执行前请确认

原文

uv pip install oumi
uv pip install 'oumi[gpu]'

硬件要求按 README 说明

README 未给出具体显存要求；GPU 支持需安装 oumi[gpu] 或使用带 --gpus all 的 Docker 镜像，README 未列明支持的 GPU 厂商与型号。

支持 Apple Silicon多卡 / 张量并行有 Docker 镜像

下载：源码 zip·GitHub Releases（安装包）本站不镜像安装包

项目说明

由 README 自动翻译提炼

没人工点评？写下你的经验

一站式开源平台，覆盖基础模型微调、评估与部署全流程，开箱即用

一站式搞定 Qwen、Gemma 等开源大模型和多模态模型的微调（SFT/RL）、评测与部署，零代码也能上手。

核心能力

用现成 recipe 配置直接跑 SFT、DPO、GRPO 训练
支持 FSDP、DeepSpeed、DDP 分布式训练
集成 vLLM、SGLang 优化推理引擎
内置 LLM-as-a-Judge 与 RubricJudge 评估
用 oumi launch 一键提交到 AWS、GCP、Azure、Lambda

容易踩的坑

GPU 功能需装 oumi[gpu] 扩展，基础包不含；远程任务示例命令文件名写的是 gcp_job，切云厂商靠 --resources.cloud 参数而非换配置文件；项目处于 beta，部分高级功能可能变动。

内容由模型根据 README 生成，可能不完整或不准确，以官方文档为准。看原文 README

用户点评

登录用户的真实使用记录，可按版本和硬件筛选

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还没有人点评。第一个说出真实体验的人最有价值。

README 原文

未翻译

在 GitHub 打开

Everything you need to build state-of-the-art foundation models, end-to-end

🔥 News

[2026/09] Oumi v0.9 released: an end-to-end stack for agentic, tool-using models, with tool-calling SFT/DPO, executable tool environments, GRPO over any Oumi environment with verl, and a multi-criteria RubricJudge.
[2026/08] Extended GRPO reinforcement learning to support tool use
[2026/07] Added support for tools, environments (simulated, lookup, database), and agentic data synthesis
[2026/06] Added support for the Gemma 4 model family
[2026/06] Added partial-failure support across inference, judging, and data synthesis
[2026/05] Oumi v0.8 released with oumi deploy CLI for dedicated inference endpoints, an oumi-mcp MCP server for Claude/Cursor integration, batch API support across Anthropic/Fireworks/Together, and Transformers v5 / TRL / vLLM dependency upgrades
[2026/03] Upgraded to Transformers v5, TRL v0.30, vLLM v0.19, and veRL v0.7 compatibility
[2026/03] New oumi deploy command for deploying Oumi models to dedicated inference endpoints on Fireworks and Parasail
[2026/03] Added support for the Qwen3.5 model family
[2026/02] Preview of using the Oumi Platform and Lambda to fine-tune and deploy a 4B model for user intent classification
[2026/02] Lambda and Oumi partner for end-to-end custom model development
[2026/01] Oumi v0.7 released with Python 3.14 and PyTorch 2.9 support, Fireworks and OpenRouter inference engines, and rule-based evaluation judges
[2025/12] Oumi v0.6.0 released with Python 3.13 support, oumi analyze CLI command, TRL 0.26+ support, and more
[2025/12] WeMakeDevs AI Agents Assemble Hackathon: Oumi webinar on Finetuning for Text-to-SQL
[2025/12] Oumi co-sponsors WeMakeDevs AI Agents Assemble Hackathon with over 2000 project submissions
[2025/11] Oumi v0.5.0 released with advanced data synthesis, hyperparameter tuning automation, support for OpenEnv, and more

[2025/11] Example notebook to perform RLVF fine-tuning with OpenEnv, an open source library from the Meta PyTorch team for creating, deploying, and distributing agentic RL environments
[2025/10] Oumi v0.4.1 and v0.4.2 released] with support for Qwen3-VL and Transformers v4.56, data synthesis documentation and examples, and many bug fixes
[2025/09] Oumi v0.4.0 released with DeepSpeed support, a Hugging Face Hub cache management tool, KTO/Vision DPO trainer support
[2025/08] Training and inference support for OpenAI's gpt-oss-20b and gpt-oss-120b: recipes here
[2025/08] Aug 14 Webinar - OpenAI's gpt-oss: Separating the Substance from the Hype.
[2025/08] Oumi v0.3.0 released with model quantization (AWQ), an improved LLM-as-a-Judge API, and Adaptive Inference
[2025/07] Recipe for Qwen3 235B
[2025/07] July 24 webinar: "Training a State-of-the-art Agent LLM with Oumi + Lambda"
[2025/06] Oumi v0.2.0 released with support for GRPO fine-tuning, a plethora of new model support, and much more
[2025/06] Announcement of Data Curation for Vision Language Models (DCVLR) competition at NeurIPS2025
[2025/06] Recipes for training, inference, and eval with the newly released Falcon-H1 and Falcon-E models
[2025/05] Support and recipes for InternVL3 1B
[2025/04] Added support for training and inference with Llama 4 models: Scout (17B activated, 109B total) and Maverick (17B activated, 400B total) variants, including full fine-tuning, LoRA, and QLoRA configurations
[2025/04] Recipes for Qwen3 model family
[2025/04] Introducing HallOumi: a State-of-the-Art Claim-Verification Model (technical overview)
[2025/04] Oumi now supports two new Vision-Language models: Phi4 and Qwen 2.5

🔎 About

Oumi is a fully open-source platform that streamlines the entire lifecycle of foundation models - from data preparation and training to evaluation and deployment. Whether you're developing on a laptop, launching large scale experiments on a cluster, or deploying models in production, Oumi provides the tools and workflows you need.

With Oumi, you can:

🚀 Train and fine-tune models from 10M to 400B+ parameters using state-of-the-art techniques (SFT, LoRA, QLoRA, DPO, GRPO, and more)
🛠️ Train agentic, tool-using models: tool-calling SFT, executable tool environments (database, HTTP endpoint, lookup, simulated), and reinforcement learning over those environments with verl GRPO
🤖 Work with both text and multimodal models (Llama, Gemma, Qwen, DeepSeek, GLM, Phi, and others)
🔄 Synthesize and curate training data, including multi-turn tool-use conversations, with LLM judges
⚡️ Serve models efficiently with popular inference engines (vLLM, SGLang), or deploy to dedicated endpoints with oumi deploy
📊 Evaluate models across standard benchmarks and with multi-criteria rubric judges
🌎 Run anywhere - from laptops to clusters to clouds (AWS, Azure, GCP, Lambda, Modal, Slurm, and more)
🔌 Integrate with both open models and commercial APIs (OpenAI, Anthropic, Vertex AI, Together, Fireworks, Parasail, ...), and drive Oumi from Claude or Cursor with the oumi-mcp MCP server

All with one consistent API, production-grade reliability, and all the flexibility you need for research.

Learn more at oumi.ai, or jump right in with the quickstart guide.

🚀 Getting Started

Notebook	Try in Colab	Goal
🎯 Getting Started: A Tour		Quick tour of core features: training, evaluation, inference, and job management
🔧 Model Finetuning Guide		End-to-end guide to LoRA tuning with data prep, training, and evaluation
📚 Model Distillation		Guide to distilling large models into smaller, efficient ones
📋 Model Evaluation		Comprehensive model evaluation using Oumi's evaluation framework
☁️ Remote Training		Launch and monitor training jobs on cloud (AWS, Azure, GCP, Lambda, etc.) platforms
📈 LLM-as-a-Judge		Filter and curate training data with built-in judges
🎯 GRPO Reinforcement Learning		Train a model with GRPO and a custom reward function
🖼️ Vision Language Models		Fine-tune and run inference with vision-language models
⚡️ vLLM Inference		Run fast local inference with the vLLM engine

🔧 Usage

Installation

Choose the installation method that works best for you:

# Basic installation
uv pip install oumi

# With GPU support
uv pip install 'oumi[gpu]'

# Latest development version
uv pip install git+https://github.com/oumi-ai/oumi.git

Don't have uv? Install it or use pip instead.

# Pull the latest image
docker pull ghcr.io/oumi-ai/oumi:latest

# Run oumi commands
docker run --gpus all -it ghcr.io/oumi-ai/oumi:latest oumi --help

# Train with a mounted config
docker run --gpus all -v $(pwd):/workspace -it ghcr.io/oumi-ai/oumi:latest \
    oumi train --config /workspace/my_config.yaml

Try Oumi without setting up a Python environment. This installs Oumi in an isolated environment:

curl -LsSf https://oumi.ai/install.sh | bash

For more advanced installation options, see the installation guide.

Oumi CLI

You can quickly use the oumi command to train, evaluate, and infer models using one of the existing recipes:

# Training
oumi train -c configs/recipes/smollm/sft/135m/quickstart_train.yaml

# Evaluation
oumi evaluate -c configs/recipes/smollm/evaluation/135m/quickstart_eval.yaml

# Inference
oumi infer -c configs/recipes/smollm/inference/135m_infer.yaml --interactive

For more advanced options, see the training, evaluation, inference, and llm-as-a-judge guides.

Running Jobs Remotely

You can run jobs remotely on cloud platforms (AWS, Azure, GCP, Lambda, etc.) using the oumi launch command:

# GCP
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_gcp_job.yaml

# AWS
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_gcp_job.yaml --resources.cloud aws

# Azure
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_gcp_job.yaml --resources.cloud azure

# Lambda
oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_gcp_job.yaml --resources.cloud lambda

Note: Oumi is in beta and under active development. The core features are stable, but some advanced features might change as the platform improves.

💻 Why use Oumi?

If you need a comprehensive platform for training, evaluating, or deploying models, Oumi is a great choice.

Here are some of the key features that make Oumi stand out:

🔧 Zero Boilerplate: Get started in minutes with ready-to-use recipes for popular models and workflows. No need to write training loops or data pipelines.
🏢 Enterprise-Grade: Built and validated by teams training models at scale
🎯 Research Ready: Perfect for ML research with easily reproducible experiments, and flexible interfaces for customizing each component.
🌐 Broad Model Support: Works with most popular model architectures - from tiny models to the largest ones, text-only to multimodal.
🚀 SOTA Performance: Native support for distributed training techniques (FSDP, DeepSpeed, DDP) and optimized inference engines (vLLM, SGLang).
🤝 Community First: 100% open source with an active community. No vendor lock-in, no strings attached.

📚 Examples & Recipes

Explore the growing collection of ready-to-use configurations for state-of-the-art models and training workflows:

Note: These configurations are not an exhaustive list of what's supported, simply examples to get you started. You can find a more exhaustive list of supported models, and datasets (supervised fine-tuning, pre-training, preference tuning, and vision-language finetuning) in the oumi documentation.

Qwen Family

Model	Example Configurations
Qwen3-Next 80B A3B	LoRA • Inference • Inference (Instruct) • Evaluation
Qwen3.5 0.8B	FFT • LoRA • Inference (vLLM) • Inference
Qwen3 30B A3B	LoRA • Inference • Evaluation
Qwen3 32B	LoRA • Inference • Evaluation
Qwen3 14B	LoRA • Inference • Evaluation
Qwen3 8B	FFT • Inference • Evaluation
Qwen3 4B	FFT • Inference • Evaluation
Qwen3 1.7B	FFT • Inference • Evaluation
Qwen3 0.6B	FFT • Inference • Evaluation
Qwen3-Coder 30B A3B Instruct	Inference (vLLM) • Inference (vLLM, tool calling) • Inference (GGUF) • Inference (GGUF, macOS)
QwQ 32B	FFT • LoRA • QLoRA • Inference • Evaluation
Qwen2.5-VL 3B	SFT • LoRA • Inference (vLLM) • Inference
Qwen2-VL 2B	SFT • LoRA • Inference (vLLM) • Inference (SGLang) • Inference • Evaluation

💎 Gemma Family

Model	Example Configurations
Gemma 4 E2B Instruct	FFT • LoRA
Gemma 4 E4B Instruct	FFT • LoRA
Gemma 4 12B Instruct	FFT • LoRA
Gemma 4 26B A4B Instruct	LoRA
Gemma 4 31B Instruct	LoRA
Gemma 3 4B Instruct	FFT • Inference • Evaluation
Gemma 3 12B Instruct	LoRA • Inference • Evaluation
Gemma 3 27B Instruct	LoRA • Inference • Evaluation

🐋 DeepSeek R1 Family

Model	Example Configurations
DeepSeek R1 671B	Inference (Together AI)
Distilled Llama 8B	FFT • LoRA • QLoRA • Inference • Evaluation
Distilled Llama 70B	FFT • LoRA • QLoRA • Inference • Evaluation
Distilled Qwen 1.5B	FFT • LoRA • Inference • Evaluation
Distilled Qwen 32B	LoRA • Inference • Evaluation

🦙 Llama Family

Model	Example Configurations
Llama 4 Scout Instruct 17B	FFT • LoRA • QLoRA • Inference (vLLM) • Inference • Inference (Together.ai)
Llama 4 Scout 17B	FFT
Llama 3.1 8B	FFT • LoRA • QLoRA • Pre-training • Inference (vLLM) • Inference • Evaluation
Llama 3.1 70B	FFT • LoRA • QLoRA • Inference • Evaluation
Llama 3.1 405B	FFT • LoRA • QLoRA
Llama 3.2 1B	FFT • Inference (vLLM) • Inference (SGLang) • Inference • Evaluation
Llama 3.2 3B	FFT • LoRA • QLoRA • Inference (vLLM) • Inference (SGLang) • Inference • Evaluation
Llama 3.3 70B	FFT • LoRA • QLoRA • Inference (vLLM) • Inference • Evaluation
Llama 3.2 Vision 11B	SFT • Inference (vLLM) • Inference (SGLang) • Evaluation

🦅 Falcon family

Model	Example Configurations
Falcon-H1	FFT • Inference • Evaluation
Falcon-E (BitNet)	FFT • DPO • Evaluation

🧠 gpt-oss Family

Model	Example Configurations
gpt-oss 20B	LoRA (single GPU) • LoRA (multi-GPU) • Inference (vLLM) • Inference
gpt-oss 120B	LoRA (multi-GPU) • Inference (vLLM) • Inference (Together AI) • Inference

🧩 GLM Family

Model	Example Configurations
GLM-4.7	Inference (Fireworks)
GLM-4.5-Air	Inference (GGUF) • Inference (GGUF, macOS)
GLM-4 9B Chat	Inference (vLLM)

🦉 OLMo 3 Family

Model	Example Configurations
OLMo 3 7B Instruct	FFT • Inference • Evaluation
OLMo 3 32B Instruct	LoRA • Inference • Evaluation

🎨 Vision Models

Model	Example Configurations
Llama 3.2 Vision 11B	SFT • LoRA • Inference (vLLM) • Inference (SGLang) • Evaluation
LLaVA 7B	SFT • Inference (vLLM) • Inference
Phi3 Vision 4.2B	SFT • LoRA • Inference (vLLM)
Phi4 Vision 5.6B	SFT • LoRA • Inference (vLLM) • Inference
Qwen2-VL 2B	SFT • LoRA • Inference (vLLM) • Inference (SGLang) • Inference • Evaluation
InternVL3 1B	SFT
Molmo 7B-D	SFT
Molmo 7B-O	SFT • GRPO
Qwen3-VL 2B	Inference
Qwen3-VL 4B	Inference
Qwen3-VL 8B	Inference
Qwen3-VL 30B A3B Instruct	FFT • LoRA
Qwen2.5-VL 3B	SFT • LoRA • Inference (vLLM) • Inference
SmolVLM-Instruct 2B	SFT • LoRA

🔍 Even more options

This section lists all the language models that can be used with Oumi. Thanks to the integration with the 🤗 Transformers library, you can easily use any of these models for training, evaluation, or inference.

Models prefixed with a checkmark (✅) have been thoroughly tested and validated by the Oumi community, with ready-to-use recipes available in the configs/recipes directory.

Instruct Models

Model	Size	Paper	HF Hub	License	Open ¹
✅ SmolLM-Instruct	135M/360M/1.7B	Blog	Hub	Apache 2.0	✅
✅ DeepSeek R1 Family	1.5B/8B/32B/70B/671B	Blog	Hub	MIT	❌
✅ Llama 3.1 Instruct	8B/70B/405B	Paper	Hub	License	❌
✅ Llama 3.2 Instruct	1B/3B	Paper	Hub	License	❌
✅ Llama 3.3 Instruct	70B	Paper	Hub	License	❌
✅ Phi-3.5-Instruct	4B/14B	Paper	Hub	License	❌
✅ Qwen3	0.6B-32B	Paper	Hub	License	❌
Qwen2.5-Instruct	0.5B-70B	Paper	Hub	License	❌
OLMo 2 Instruct	7B	Paper	Hub	Apache 2.0	✅
✅ OLMo 3 Instruct	7B/32B	Paper	Hub	Apache 2.0	✅
MPT-Instruct	7B	Blog	Hub	Apache 2.0	✅
Command R	35B/104B	Blog	Hub	License	❌
Granite-3.1-Instruct	2B/8B	Paper	Hub	Apache 2.0	❌
Gemma 2 Instruct	2B/9B	Blog	Hub	License	❌
✅ Gemma 4 Instruct	E2B/E4B/12B/26B A4B/31B	Blog	Hub	License	❌
✅ Qwen3.5	0.8B	Blog	Hub	License	❌
GLM-4.5-Air	106B (Total)	Blog	Hub	MIT	❌
✅ Gemma 3 Instruct	4B/12B/27B	Blog	Hub	License	❌
DBRX-Instruct	130B MoE	Blog	Hub	Apache 2.0	❌
Falcon-Instruct	7B/40B	Paper	Hub	Apache 2.0	❌
✅ Llama 4 Scout Instruct	17B (Activated) 109B (Total)	Paper	Hub	License	❌
✅ Llama 4 Maverick Instruct	17B (Activated) 400B (Total)	Paper	Hub	License	❌

Vision-Language Models

Model	Size	Paper	HF Hub	License	Open
✅ Llama 3.2 Vision	11B	Paper	Hub	License	❌
✅ LLaVA-1.5	7B	Paper	Hub	License	❌
✅ Phi-3 Vision	4.2B	Paper	Hub	License	❌
✅ BLIP-2	3.6B	Paper	Hub	MIT	❌
✅ Qwen2-VL	2B	Blog	Hub	License	❌
✅ Qwen3-VL	2B/4B/8B	Blog	Hub	License	❌
✅ SmolVLM-Instruct	2B	Blog	Hub	Apache 2.0	✅

Base Models

Model	Size	Paper	HF Hub	License	Open
✅ SmolLM2	135M/360M/1.7B	Blog	Hub	Apache 2.0	✅
✅ Llama 3.2	1B/3B	Paper	Hub	License	❌
✅ Llama 3.1	8B/70B/405B	Paper	Hub	License	❌
✅ GPT-2	124M-1.5B	Paper	Hub	MIT	✅
DeepSeek V2	7B/13B	Paper	Hub	License	❌
Gemma2	2B/9B	Blog	Hub	License	❌
GPT-J	6B	Blog	Hub	Apache 2.0	✅
GPT-NeoX	20B	Paper	Hub	Apache 2.0	✅
Mistral	7B	Paper	Hub	Apache 2.0	❌
Mixtral	8x7B/8x22B	Blog	Hub	Apache 2.0	❌
MPT	7B	Blog	Hub	Apache 2.0	✅
OLMo	1B/7B	Paper	Hub	Apache 2.0	✅
✅ Llama 4 Scout	17B (Activated) 109B (Total)	Paper	Hub	License	❌

Reasoning Models

Model	Size	Paper	HF Hub	License	Open
✅ gpt-oss	20B/120B	Paper	Hub	Apache 2.0	❌
✅ Qwen3	0.6B-32B	Paper	Hub	License	❌
✅ Qwen3-Next	80B-A3B	Blog	Hub	License	❌
Qwen QwQ	32B	Blog	Hub	License	❌

Code Models

Model	Size	Paper	HF Hub	License	Open
✅ Qwen2.5 Coder	0.5B-32B	Blog	Hub	License	❌
DeepSeek Coder	1.3B-33B	Paper	Hub	License	❌
StarCoder 2	3B/7B/15B	Paper	Hub	License	✅

Math Models

Model	Size	Paper	HF Hub	License	Open
DeepSeek Math	7B	Paper	Hub	License	❌

📖 Documentation

To learn more about all the platform's capabilities, see the Oumi documentation.

🤝 Join the Community

Oumi is a community-first effort. Whether you are a developer, a researcher, or a non-technical user, all contributions are very welcome!

To contribute to the oumi repository, please check the CONTRIBUTING.md for guidance on how to contribute to send your first Pull Request.
Make sure to join our Discord community to get help, share your experiences, and contribute to the project!

🙏 Acknowledgements

Oumi makes use of several libraries and tools from the open-source community. We would like to acknowledge and deeply thank the contributors of these projects! ✨ 🌟 💫

📝 Citation

If you find Oumi useful in your research, please consider citing it:

@software{oumi2025,
  author = {Oumi Community},
  title = {Oumi: an Open, End-to-end Platform for Building Large Foundation Models},
  month = {January},
  year = {2025},
  url = {https://github.com/oumi-ai/oumi}
}

📜 License

This project is licensed under the Apache License 2.0. See the LICENSE file for details.

Open models are defined as models with fully open weights, training code, and data, and a permissive license. See Open Source Definitions for more information. ↩

怎么上手

项目说明

核心能力

容易踩的坑

最近发版

Oumi v0.9 Release Notes

Highlights

🛠️ Tool-use training

🌐 Tool-execution Environments

🎯 OumiVerlTool — RL over any Oumi environment

⚖️ RubricJudge — multi-criteria evaluation

🧬 Conversation synthesis

🔁 Partial-failure-tolerant pipelines

⚡ Inference hardening

Oumi v0.8 Release Notes

Highlights

oumi deploy — new CLI for dedicated inference endpoints

oumi-mcp — Model Context Protocol server

Batch API support across hosted inference providers

RPM / TPM rate limiting on RemoteInferenceEngine

Oumi 0.7 Release

✨ Highlights

🚀 New Features

Inference

Evaluation

Training

Data & Synthesis

Infrastructure

📈 Improvements

Performance

CLI

Documentation

Configs

⚠️ Breaking Changes

🐛 Bug Fixes

Oumi v0.6.0 Changelog

Highlights

Python 3.13 Support

New oumi analyze CLI Command

TRL GOLD Trainer

Code Evaluation Judges

Kubernetes Deployment

Option 1: Using SkyPilot (new in this release)

Option 2: Direct kubectl deployment

Custom Master Port for Distributed Training

ARM Docker Images for Mac

Bug Fixes

Documentation

Oumi v0.5.0 Release Notes

🚀 Major Features

Data Synthesis Module

Hyperparameter Tuning Module

Inference & Training Enhancements

HPC & Launcher Improvements

✨ Improvements

🐛 Bug Fixes

📦 Dependency Updates

🎉 New Contributors

📖 Full Changelog

用户点评

README 原文

Everything you need to build state-of-the-art foundation models, end-to-end

🔥 News

🔎 About

🚀 Getting Started

🔧 Usage

Installation

Oumi CLI

Running Jobs Remotely

💻 Why use Oumi?

📚 Examples & Recipes

Qwen Family

💎 Gemma Family

🐋 DeepSeek R1 Family

🦙 Llama Family

🦅 Falcon family

🧠 gpt-oss Family

🧩 GLM Family

🦉 OLMo 3 Family

🎨 Vision Models

🎯 `OumiVerlTool` — RL over any Oumi environment

⚖️ `RubricJudge` — multi-criteria evaluation

`oumi deploy` — new CLI for dedicated inference endpoints

`oumi-mcp` — Model Context Protocol server

RPM / TPM rate limiting on `RemoteInferenceEngine`

New `oumi analyze` CLI Command