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

<p align="center">
  <a href="https://trendshift.io/repositories/12865">
    <img alt="GitHub trending" src="https://trendshift.io/api/badge/repositories/12865" />
  </a>
</p>

## 🔥 News

- [2026/06] Added support for the Gemma 4 model family
- [2026/05] [Oumi v0.8 released](https://github.com/oumi-ai/oumi/releases/tag/v0.8) 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] [MCP Integration Phase 1](https://github.com/oumi-ai/oumi/pull/2234): package scaffold and dependencies for MCP server support
- [2026/03] New: `oumi deploy` command for deploying oumi models dedicated inference endpoints on fireworks.ai and parasail
- [2026/03] Added support for Qwen3.5 model family
- [2026/03] Inference engines received multiple improvements: list_models api, improved error reporting
- [2026/02] [Preview of using the Oumi Platform and Lambda to fine-tune and deploy a 4B model for user intent classification](https://youtu.be/0XpfYRpd_FA)
- [2026/02] [Lambda and Oumi partner for end-to-end custom model development](https://blog.oumi.ai/p/lambda-and-oumi-partner-for-end-to)
- [2025/12] [Oumi v0.6.0 released](https://github.com/oumi-ai/oumi/releases/tag/v0.6.0) 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](https://www.youtube.com/watch?v=6wPikqRZ7bQ&t=3203s)
- [2025/12] [Oumi co-sponsors WeMakeDevs AI Agents Assemble Hackathon with over 2000 project submissions](https://www.wemakedevs.org/hackathons/assemblehack25)
- [2025/11] [Oumi v0.5.0 released](https://github.com/oumi-ai/oumi/releases/tag/v0.5) with advanced data synthesis, hyperparameter tuning automation, support for OpenEnv, and more

<details>
<summary>Older updates</summary>

- [2025/11] [Example notebook to perform RLVF fine-tuning with OpenEnv](https://github.com/oumi-ai/oumi/blob/main/notebooks/Oumi%20-%20OpenEnv%20GRPO%20with%20trl.ipynb), an open source library from the Meta PyTorch team for creating, deploying, and distributing agentic RL environments
- [2025/10] [Oumi v0.4.1](https://github.com/oumi-ai/oumi/releases/tag/v0.4.1) and [v0.4.2](https://github.com/oumi-ai/oumi/releases/tag/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](https://github.com/oumi-ai/oumi/releases/tag/v0.4.0) 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](https://github.com/oumi-ai/oumi/tree/main/configs/recipes/gpt_oss)
- [2025/08] Aug 14 Webinar - [OpenAI's gpt-oss: Separating the Substance from the Hype](https://youtu.be/g1PkAV7fXn0).
- [2025/08] [Oumi v0.3.0 released](https://github.com/oumi-ai/oumi/releases/tag/v0.3.0) with model quantization (AWQ), an improved LLM-as-a-Judge API, and Adaptive Inference
- [2025/07] Recipe for [Qwen3 235B](https://github.com/oumi-ai/oumi/blob/main/configs/recipes/qwen3/inference/235b_a22b_together_infer.yaml)
- [2025/07] July 24 webinar: ["Training a State-of-the-art Agent LLM with Oumi + Lambda"](https://youtu.be/f3SU_heBP54)
- [2025/06] [Oumi v0.2.0 released](https://github.com/oumi-ai/oumi/releases/tag/v0.2.0) 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](https://oumi.ai/blog/posts/announcing-dcvlr) at NeurIPS2025
- [2025/06] Recipes for training, inference, and eval with the newly released [Falcon-H1](https://github.com/oumi-ai/oumi/tree/main/configs/recipes/falcon_h1) and [Falcon-E](https://github.com/oumi-ai/oumi/tree/main/configs/recipes/falcon_e) models
- [2025/05] Support and recipes for [InternVL3 1B](https://github.com/oumi-ai/oumi/tree/main/configs/recipes/vision/internvl3)
- [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](https://github.com/oumi-ai/oumi/tree/main/configs/recipes/qwen3)
- [2025/04] Introducing HallOumi: a State-of-the-Art Claim-Verification Model [(technical overview)](https://oumi.ai/blog/posts/introducing-halloumi)
- [2025/04] Oumi now supports two new Vision-Language models: [Phi4](https://github.com/oumi-ai/oumi/tree/main/configs/recipes/vision/phi4) and [Qwen 2.5](https://github.com/oumi-ai/oumi/tree/main/configs/recipes/vision/qwen2_5_vl_3b)

</details>

## 🔎 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 405B parameters using state-of-the-art techniques (SFT, LoRA, QLoRA, GRPO, and more)
- 🤖 Work with both text and multimodal models (Llama, DeepSeek, Qwen, Phi, and others)
- 🔄 Synthesize and curate training data with LLM judges
- ⚡️ Deploy models efficiently with popular inference engines (vLLM, SGLang)
- 📊 Evaluate models comprehensively across standard benchmarks
- 🌎 Run anywhere - from laptops to clusters to clouds (AWS, Azure, GCP, Lambda, and more)
- 🔌 Integrate with both open models and commercial APIs (OpenAI, Anthropic, Vertex AI, Together, Parasail, ...)

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

Learn more at [oumi.ai](https://oumi.ai/docs), or jump right in with the [quickstart guide](https://oumi.ai/docs/en/latest/get_started/quickstart.html).

## 🚀 Getting Started

| **Notebook** | **Try in Colab** | **Goal** |
|----------|--------------|-------------|
| **🎯 Getting Started: A Tour** | <a target="_blank" href="https://colab.research.google.com/github/oumi-ai/oumi/blob/main/notebooks/Oumi - A Tour.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | Quick tour of core features: training, evaluation, inference, and job management |
| **🔧 Model Finetuning Guide** | <a target="_blank" href="https://colab.research.google.com/github/oumi-ai/oumi/blob/main/notebooks/Oumi - Finetuning Tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | End-to-end guide to LoRA tuning with data prep, training, and evaluation |
| **📚 Model Distillation** | <a target="_blank" href="https://colab.research.google.com/github/oumi-ai/oumi/blob/main/notebooks/Oumi - Distill a Large Model.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | Guide to distilling large models into smaller, efficient ones |
| **📋 Model Evaluation** | <a target="_blank" href="https://colab.research.google.com/github/oumi-ai/oumi/blob/main/notebooks/Oumi - Evaluation with Oumi.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | Comprehensive model evaluation using Oumi's evaluation framework |
| **☁️ Remote Training** | <a target="_blank" href="https://colab.research.google.com/github/oumi-ai/oumi/blob/main/notebooks/Oumi - Running Jobs Remotely.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | Launch and monitor training jobs on cloud (AWS, Azure, GCP, Lambda, etc.) platforms |
| **📈 LLM-as-a-Judge** | <a target="_blank" href="https://colab.research.google.com/github/oumi-ai/oumi/blob/main/notebooks/Oumi - Simple Judge.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> | Filter and curate training data with built-in judges |

## 🔧 Usage

### Installation

Choose the installation method that works best for you:

<details open>
<summary><b>Using pip (Recommended)</b></summary>

```bash
# 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](https://docs.astral.sh/uv/getting-started/installation/) or use `pip` instead.

</details>

<details>
<summary><b>Using Docker</b></summary>

```bash
# 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
```

</details>

<details>
<summary><b>Quick Install Script (Experimental)</b></summary>

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

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

</details>

For more advanced installation options, see the [installation guide](https://oumi.ai/docs/en/latest/get_started/installation.html).

### Oumi CLI

You can quickly use the `oumi` command to train, evaluate, and infer models using one of the existing [recipes](/configs/recipes):

```shell
# 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](https://oumi.ai/docs/en/latest/user_guides/train/train.html), [evaluation](https://oumi.ai/docs/en/latest/user_guides/evaluate/evaluate.html), [inference](https://oumi.ai/docs/en/latest/user_guides/infer/infer.html), and [llm-as-a-judge](https://oumi.ai/docs/en/latest/user_guides/judge/judge.html) guides.

### Running Jobs Remotely

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

```shell
# 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 <ins>beta</ins> 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](https://oumi.ai/docs/en/latest/resources/models/supported_models.html), and datasets ([supervised fine-tuning](https://oumi.ai/docs/en/latest/resources/datasets/sft_datasets.html), [pre-training](https://oumi.ai/docs/en/latest/resources/datasets/pretraining_datasets.html), [preference tuning](https://oumi.ai/docs/en/latest/resources/datasets/preference_datasets.html), and [vision-language finetuning](https://oumi.ai/docs/en/latest/resources/datasets/vl_sft_datasets.html)) in the oumi documentation.

### Qwen Family

| Model | Example Configurations |
|-------|------------------------|
| Qwen3-Next 80B A3B | [LoRA](/configs/recipes/qwen3_next/sft/80b_a3b_lora/train.yaml) • [Inference](/configs/recipes/qwen3_next/inference/80b_a3b_infer.yaml) • [Inference (Instruct)](/configs/recipes/qwen3_next/inference/80b_a3b_instruct_infer.yaml) • [Evaluation](/configs/recipes/qwen3_next/evaluation/80b_a3b_eval.yaml) |
| Qwen3 30B A3B | [LoRA](/configs/recipes/qwen3/sft/30b_a3b_lora/train.yaml) • [Inference](/configs/recipes/qwen3/inference/30b_a3b_infer.yaml) • [Evaluation](/configs/recipes/qwen3/evaluation/30b_a3b_eval.yaml) |
| Qwen3 32B | [LoRA](/configs/recipes/qwen3/sft/32b_lora/train.yaml) • [Inference](/configs/recipes/qwen3/inference/32b_infer.yaml) • [Evaluation](/configs/recipes/qwen3/evaluation/32b_eval.yaml) |
| Qwen3 14B | [LoRA](/configs/recipes/qwen3/sft/14b_lora/train.yaml) • [Inference](/configs/recipes/qwen3/inference/14b_infer.yaml) • [Evaluation](/configs/recipes/qwen3/evaluation/14b_eval.yaml) |
| Qwen3 8B | [FFT](/configs/recipes/qwen3/sft/8b_full/train.yaml) • [Inference](/configs/recipes/qwen3/inference/8b_infer.yaml) • [Evaluation](/configs/recipes/qwen3/evaluation/8b_eval.yaml) |
| Qwen3 4B | [FFT](/configs/recipes/qwen3/sft/4b_full/train.yaml) • [Inference](/configs/recipes/qwen3/inference/4b_infer.yaml) • [Evaluation](/configs/recipes/qwen3/evaluation/4b_eval.yaml) |
| Qwen3 1.7B | [FFT](/configs/recipes/qwen3/sft/1.7b_full/train.yaml) • [Inference](/configs/recipes/qwen3/inference/1.7b_infer.yaml) • [Evaluation](/configs/recipes/qwen3/evaluation/1.7b_eval.yaml) |
| Qwen3 0.6B | [FFT](/configs/recipes/qwen3/sft/0.6b_full/train.yaml) • [Inference](/configs/recipes/qwen3/inference/0.6b_infer.yaml) • [Evaluation](/configs/recipes/qwen3/evaluation/0.6b_eval.yaml) |
| QwQ 32B | [FFT](/configs/recipes/qwq/sft/full_train.yaml) • [LoRA](/configs/recipes/qwq/sft/lora_train.yaml) • [QLoRA](/configs/recipes/qwq/sft/qlora_train.yaml) • [Inference](/configs/recipes/qwq/inference/infer.yaml) • [Evaluation](/configs/recipes/qwq/evaluation/eval.yaml) |
| Qwen2.5-VL 3B | [SFT](/configs/recipes/vision/qwen2_5_vl_3b/sft/full/train.yaml) • [LoRA](/configs/recipes/vision/qwen2_5_vl_3b/sft/lora/train.yaml)• [Inference (vLLM)](configs/recipes/vision/qwen2_5_vl_3b/inference/vllm_infer.yaml) • [Inference](configs/recipes/vision/qwen2_5_vl_3b/inference/infer.yaml) |
| Qwen2-VL 2B | [SFT](/configs/recipes/vision/qwen2_vl_2b/sft/full/train.yaml) • [LoRA](/configs/recipes/vision/qwen2_vl_2b/sft/lora/train.yaml) • [Inference (vLLM)](configs/recipes/vision/qwen2_vl_2b/inference/vllm_infer.yaml) • [Inference (SGLang)](configs/recipes/vision/qwen2_vl_2b/inference/sglang_infer.yaml) • [Inference](configs/recipes/vision/qwen2_vl_2b/inference/infer.yaml) • [Evaluation](configs/recipes/vision/qwen2_vl_2b/evaluation/eval.yaml) |

### 🐋 DeepSeek R1 Family

| Model | Example Configurations |
|-------|------------------------|
| DeepSeek R1 671B | [Inference (Together AI)](configs/recipes/deepseek_r1/inference/671b_together_infer.yaml) |
| Distilled Llama 8B | [FFT](/configs/recipes/deepseek_r1/sft/distill_llama_8b/full_train.yaml) • [LoRA](/configs/recipes/deepseek_r1/sft/distill_llama_8b/lora_train.yaml) • [QLoRA](/configs/recipes/deepseek_r1/sft/distill_llama_8b/qlora_train.yaml) • [Inference](configs/recipes/deepseek_r1/inference/distill_llama_8b_infer.yaml) • [Evaluation](/configs/recipes/deepseek_r1/evaluation/distill_llama_8b/eval.yaml) |
| Distilled Llama 70B | [FFT](/configs/recipes/deepseek_r1/sft/distill_llama_70b/full_train.yaml) • [LoRA](/configs/recipes/deepseek_r1/sft/distill_llama_70b/lora_train.yaml) • [QLoRA](/configs/recipes/deepseek_r1/sft/distill_llama_70b/qlora_train.yaml) • [Inference](configs/recipes/deepseek_r1/inference/distill_llama_70b_infer.yaml) • [Evaluation](/configs/recipes/deepseek_r1/evaluation/distill_llama_70b/eval.yaml) |
| Distilled Qwen 1.5B | [FFT](/configs/recipes/deepseek_r1/sft/distill_qwen_1_5b/full_train.yaml) • [LoRA](/configs/recipes/deepseek_r1/sft/distill_qwen_1_5b/lora_train.yaml) • [Inference](configs/recipes/deepseek_r1/inference/distill_qwen_1_5b_infer.yaml) • [Evaluation](/configs/recipes/deepseek_r1/evaluation/distill_qwen_1_5b/eval.yaml) |
| Distilled Qwen 32B | [LoRA](/configs/recipes/deepseek_r1/sft/distill_qwen_32b/lora_train.yaml) • [Inference](configs/recipes/deepseek_r1/inference/distill_qwen_32b_infer.yaml) • [Evaluation](/configs/recipes/deepseek_r1/evaluation/distill_qwen_32b/eval.yaml) |

### 🦙 Llama Family

| Model | Example Configurations |
|-------|------------------------|
| Llama 4 Scout Instruct 17B | [FFT](/configs/recipes/llama4/sft/scout_instruct_full/train.yaml) • [LoRA](/configs/recipes/llama4/sft/scout_instruct_lora/train.yaml) • [QLoRA](/configs/recipes/llama4/sft/scout_instruct_qlora/train.yaml) • [Inference (vLLM)](/configs/recipes/llama4/inference/scout_instruct_vllm_infer.yaml) • [Inference](/configs/recipes/llama4/inference/scout_instruct_infer.yaml) • [Inference (Together.ai)](/configs/recipes/llama4/inference/scout_instruct_together_infer.yaml) |
| Llama 4 Scout 17B | [FFT](/configs/recipes/llama4/sft/scout_base_full/train.yaml)  |
| Llama 3.1 8B | [FFT](/configs/recipes/llama3_1/sft/8b_full/train.yaml) • [LoRA](/configs/recipes/llama3_1/sft/8b_lora/train.yaml) • [QLoRA](/configs/recipes/llama3_1/sft/8b_qlora/train.yaml) • [Pre-training](/configs/recipes/llama3_1/pretraining/8b/train.yaml) • [Inference (vLLM)](configs/recipes/llama3_1/inference/8b_rvllm_infer.yaml) • [Inference](/configs/recipes/llama3_1/inference/8b_infer.yaml) • [Evaluation](/configs/recipes/llama3_1/evaluation/8b_eval.yaml) |
| Llama 3.1 70B | [FFT](/configs/recipes/llama3_1/sft/70b_full/train.yaml) • [LoRA](/configs/recipes/llama3_1/sft/70b_lora/train.yaml) • [QLoRA](/configs/recipes/llama3_1/sft/70b_qlora/train.yaml) • [Inference](/configs/recipes/llama3_1/inference/70b_infer.yaml) • [Evaluation](/configs/recipes/llama3_1/evaluation/70b_eval.yaml) |
| Llama 3.1 405B | [FFT](/configs/recipes/llama3_1/sft/405b_full/train.yaml) • [LoRA](/configs/recipes/llama3_1/sft/405b_lora/train.yaml) • [QLoRA](/configs/recipes/llama3_1/sft/405b_qlora/train.yaml) |
| Llama 3.2 1B | [FFT](/configs/recipes/llama3_2/sft/1b_full/train.yaml) • [LoRA](/configs/recipes/llama3_2/sft/1b_lora/train.yaml) • [QLoRA](/configs/recipes/llama3_2/sft/1b_qlora/train.yaml) • [Inference (vLLM)](/configs/recipes/llama3_2/inference/1b_vllm_infer.yaml) • [Inference (SGLang)](/configs/recipes/llama3_2/inference/1b_sglang_infer.yaml) • [Inference](/configs/recipes/llama3_2/inference/1b_infer.yaml) • [Evaluation](/configs/recipes/llama3_2/evaluation/1b_eval.yaml) |
| Llama 3.2 3B | [FFT](/configs/recipes/llama3_2/sft/3b_full/train.yaml) • [LoRA](/configs/recipes/llama3_2/sft/3b_lora/train.yaml) • [QLoRA](/configs/recipes/llama3_2/sft/3b_qlora/train.yaml) • [Inference (vLLM)](/configs/recipes/llama3_2/inference/3b_vllm_infer.yaml) • [Inference (SGLang)](/configs/recipes/llama3_2/inference/3b_sglang_infer.yaml) • [Inference](/configs/recipes/llama3_2/inference/3b_infer.yaml) • [Evaluation](/configs/recipes/llama3_2/evaluation/3b_eval.yaml) |
| Llama 3.3 70B | [FFT](/configs/recipes/llama3_3/sft/70b_full/train.yaml) • [LoRA](/configs/recipes/llama3_3/sft/70b_lora/train.yaml) • [QLoRA](/configs/recipes/llama3_3/sft/70b_qlora/train.yaml) • [Inference (vLLM)](/configs/recipes/llama3_3/inference/70b_vllm_infer.yaml) • [Inference](/configs/recipes/llama3_3/inference/70b_infer.yaml) • [Evaluation](/configs/recipes/llama3_3/evaluation/70b_eval.yaml) |
| Llama 3.2 Vision 11B | [SFT](/configs/recipes/vision/llama3_2_vision/sft/11b_full/train.yaml) • [Inference (vLLM)](/configs/recipes/vision/llama3_2_vision/inference/11b_rvllm_infer.yaml) • [Inference (SGLang)](/configs/recipes/vision/llama3_2_vision/inference/11b_sglang_infer.yaml) • [Evaluation](/configs/recipes/vision/llama3_2_vision/evaluation/11b_eval.yaml) |

### 🦅 Falcon family

| Model | Example Configurations |
|-------|------------------------|
| [Falcon-H1](https://huggingface.co/collections/tiiuae/falcon-h1-6819f2795bc406da60fab8df) | [FFT](/configs/recipes/falcon_h1/sft/) • [Inference](/configs/recipes/falcon_h1/inference/) • [Evaluation](/configs/recipes/falcon_h1/evaluation/) |
| [Falcon-E (BitNet)](https://huggingface.co/collections/tiiuae/falcon-edge-series-6804fd13344d6d8a8fa71130) | [FFT](/configs/recipes/falcon_e/sft/) • [DPO](/configs/recipes/falcon_e/dpo/) • [Evaluation](/configs/recipes/falcon_e/evaluation/) |

### 💎 Gemma 3 Family

| Model | Example Configurations |
|-------|------------------------|
| Gemma 3 4B Instruct | [FFT](/configs/recipes/gemma3/sft/4b_full/train.yaml) • [Inference](/configs/recipes/gemma3/inference/4b_instruct_infer.yaml) • [Evaluation](/configs/recipes/gemma3/evaluation/4b/eval.yaml) |
| Gemma 3 12B Instruct | [LoRA](/configs/recipes/gemma3/sft/12b_lora/train.yaml) • [Inference](/configs/recipes/gemma3/inference/12b_instruct_infer.yaml) • [Evaluation](/configs/recipes/gemma3/evaluation/12b/eval.yaml) |
| Gemma 3 27B Instruct | [LoRA](/configs/recipes/gemma3/sft/27b_lora/train.yaml) • [Inference](/configs/recipes/gemma3/inference/27b_instruct_infer.yaml) • [Evaluation](/configs/recipes/gemma3/evaluation/27b/eval.yaml) |

### 🦉 OLMo 3 Family

| Model | Example Configurations |
|-------|------------------------|
| OLMo 3 7B Instruct | [FFT](/configs/recipes/olmo3/sft/7b_full/train.yaml) • [Inference](/configs/recipes/olmo3/inference/7b_infer.yaml) • [Evaluation](/configs/recipes/olmo3/evaluation/7b/eval.yaml) |
| OLMo 3 32B Instruct | [LoRA](/configs/recipes/olmo3/sft/32b_lora/train.yaml) • [Inference](/configs/recipes/olmo3/inference/32b_infer.yaml) • [Evaluation](/configs/recipes/olmo3/evaluation/32b/eval.yaml) |

### 🎨 Vision Models

| Model | Example Configurations |
|-------|------------------------|
| Llama 3.2 Vision 11B | [SFT](/configs/recipes/vision/llama3_2_vision/sft/11b_full/train.yaml) • [LoRA](/configs/recipes/vision/llama3_2_vision/sft/11b_lora/train.yaml) • [Inference (vLLM)](/configs/recipes/vision/llama3_2_vision/inference/11b_rvllm_infer.yaml) • [Inference (SGLang)](/configs/recipes/vision/llama3_2_vision/inference/11b_sglang_infer.yaml) • [Evaluation](/configs/recipes/vision/llama3_2_vision/evaluation/11b_eval.yaml) |
| LLaVA 7B | [SFT](/configs/recipes/vision/llava_7b/sft/train.yaml) • [Inference (vLLM)](configs/recipes/vision/llava_7b/inference/vllm_infer.yaml) • [Inference](/configs/recipes/vision/llava_7b/inference/infer.yaml) |
| Phi3 Vision 4.2B | [SFT](/configs/recipes/vision/phi3/sft/full/train.yaml) • [LoRA](/configs/recipes/vision/phi3/sft/lora/train.yaml) • [Inference (vLLM)](configs/recipes/vision/phi3/inference/vllm_infer.yaml) |
| Phi4 Vision 5.6B | [SFT](/configs/recipes/vision/phi4/sft/full/train.yaml) • [LoRA](/configs/recipes/vision/phi4/sft/lora/train.yaml) • [Inference (vLLM)](configs/recipes/vision/phi4/inference/vllm_infer.yaml) • [Inference](/configs/recipes/vision/phi4/inference/infer.yaml) |
| Qwen2-VL 2B | [SFT](/configs/recipes/vision/qwen2_vl_2b/sft/full/train.yaml) • [LoRA](/configs/recipes/vision/qwen2_vl_2b/sft/lora/train.yaml) • [Inference (vLLM)](configs/recipes/vision/qwen2_vl_2b/inference/vllm_infer.yaml) • [Inference (SGLang)](configs/recipes/vision/qwen2_vl_2b/inference/sglang_infer.yaml) • [Inference](configs/recipes/vision/qwen2_vl_2b/inference/infer.yaml) • [Evaluation](configs/recipes/vision/qwen2_vl_2b/evaluation/eval.yaml) |
| Qwen3-VL 2B | [Inference](/configs/recipes/vision/qwen3_vl_2b/inference/infer.yaml) |
| Qwen3-VL 4B | [Inference](/configs/recipes/vision/qwen3_vl_4b/inference/infer.yaml) |
| Qwen3-VL 8B | [Inference](/configs/recipes/vision/qwen3_vl_8b/inference/infer.yaml) |
| Qwen2.5-VL 3B | [SFT](/configs/recipes/vision/qwen2_5_vl_3b/sft/full/train.yaml) • [LoRA](/configs/recipes/vision/qwen2_5_vl_3b/sft/lora/train.yaml)• [Inference (vLLM)](configs/recipes/vision/qwen2_5_vl_3b/inference/vllm_infer.yaml) • [Inference](configs/recipes/vision/qwen2_5_vl_3b/inference/infer.yaml) |
| SmolVLM-Instruct 2B | [SFT](/configs/recipes/vision/smolvlm/sft/full/train.yaml) • [LoRA](/configs/recipes/vision/smolvlm/sft/lora/train.yaml) |

### 🔍 Even more options

This section lists all the language models that can be used with Oumi. Thanks to the integration with the [🤗 Transformers](https://github.com/huggingface/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](configs/recipes) directory.

<details>
<summary>📋 Click to see more supported models</summary>

#### Instruct Models

| Model | Size | Paper | HF Hub  | License  | Open [^1] |
|-------|------|-------|---------|----------|------|
| ✅ SmolLM-Instruct | 135M/360M/1.7B | [Blog](https://huggingface.co/blog/smollm) | [Hub](https://huggingface.co/HuggingFaceTB/SmolLM-135M-Instruct) | Apache 2.0 | ✅ |
| ✅ DeepSeek R1 Family | 1.5B/8B/32B/70B/671B | [Blog](https://api-docs.deepseek.com/news/news250120) | [Hub](https://huggingface.co/deepseek-ai/DeepSeek-R1) | MIT | ❌ |
| ✅ Llama 3.1 Instruct | 8B/70B/405B | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-3.1-70b-instruct) | [License](https://llama.meta.com/llama3/license/) | ❌  |
| ✅ Llama 3.2 Instruct | 1B/3B | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-3.2-3b-instruct) | [License](https://llama.meta.com/llama3/license/) | ❌  |
| ✅ Llama 3.3 Instruct | 70B | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-3.3-70b-instruct) | [License](https://llama.meta.com/llama3/license/) | ❌  |
| ✅ Phi-3.5-Instruct | 4B/14B | [Paper](https://arxiv.org/abs/2404.14219) | [Hub](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) | [License](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) | ❌  |
| ✅ Qwen3 | 0.6B-32B | [Paper](https://arxiv.org/abs/2505.09388) | [Hub](https://huggingface.co/Qwen/Qwen3-32B) | [License](https://github.com/QwenLM/Qwen/blob/main/LICENSE) | ❌  |
| Qwen2.5-Instruct | 0.5B-70B | [Paper](https://arxiv.org/abs/2309.16609) | [Hub](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | [License](https://github.com/QwenLM/Qwen/blob/main/LICENSE) | ❌  |
| OLMo 2 Instruct | 7B | [Paper](https://arxiv.org/abs/2402.00838) | [Hub](https://huggingface.co/allenai/OLMo-2-1124-7B) | Apache 2.0 | ✅ |
| ✅ OLMo 3 Instruct | 7B/32B | [Paper](https://arxiv.org/abs/2402.00838) | [Hub](https://huggingface.co/allenai/OLMo-3-7B-Instruct) | Apache 2.0 | ✅ |
| MPT-Instruct | 7B | [Blog](https://www.mosaicml.com/blog/mpt-7b) | [Hub](https://huggingface.co/mosaicml/mpt-7b-instruct) | Apache 2.0 | ✅ |
| Command R | 35B/104B | [Blog](https://cohere.com/blog/command-r7b) | [Hub](https://huggingface.co/CohereForAI/c4ai-command-r-plus) | [License](https://cohere.com/c4ai-cc-by-nc-license) | ❌ |
| Granite-3.1-Instruct | 2B/8B | [Paper](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/paper.pdf) | [Hub](https://huggingface.co/ibm-granite/granite-3.1-8b-instruct) | Apache 2.0 | ❌ |
| Gemma 2 Instruct | 2B/9B | [Blog](https://ai.google.dev/gemma) | [Hub](https://huggingface.co/google/gemma-2-2b-it) | [License](https://ai.google.dev/gemma/terms) | ❌ |
| ✅ Gemma 3 Instruct | 4B/12B/27B | [Blog](https://ai.google.dev/gemma) | [Hub](https://huggingface.co/google/gemma-3-27b-it) | [License](https://ai.google.dev/gemma/terms) | ❌ |
| DBRX-Instruct | 130B MoE | [Blog](https://www.databricks.com/blog/introducing-dbrx-new-state-art-open-llm) | [Hub](https://huggingface.co/databricks/dbrx-instruct) | Apache 2.0 | ❌ |
| Falcon-Instruct | 7B/40B | [Paper](https://arxiv.org/abs/2306.01116) | [Hub](https://huggingface.co/tiiuae/falcon-7b-instruct) | Apache 2.0 | ❌  |
| ✅ Llama 4 Scout Instruct | 17B (Activated) 109B (Total) | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct) | [License](https://llama.meta.com/llama4/license/) | ❌  |
| ✅ Llama 4 Maverick Instruct | 17B (Activated) 400B (Total) | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct) | [License](https://llama.meta.com/llama4/license/) | ❌  |

#### Vision-Language Models

| Model | Size | Paper | HF Hub | License | Open |
|-------|------|-------|---------|----------|------|
| ✅ Llama 3.2 Vision | 11B | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-3.2-11b-vision) | [License](https://llama.meta.com/llama3/license/) | ❌  |
| ✅ LLaVA-1.5 | 7B | [Paper](https://arxiv.org/abs/2310.03744) | [Hub](https://huggingface.co/llava-hf/llava-1.5-7b-hf) | [License](https://ai.meta.com/llama/license) | ❌ |
| ✅ Phi-3 Vision | 4.2B | [Paper](https://arxiv.org/abs/2404.14219) | [Hub](https://huggingface.co/microsoft/Phi-3-vision-128k-instruct) | [License](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/LICENSE) | ❌ |
| ✅ BLIP-2 | 3.6B | [Paper](https://arxiv.org/abs/2301.12597) | [Hub](https://huggingface.co/Salesforce/blip2-opt-2.7b) | MIT | ❌ |
| ✅ Qwen2-VL | 2B | [Blog](https://qwenlm.github.io/blog/qwen2-vl/) | [Hub](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) | [License](https://github.com/QwenLM/Qwen/blob/main/LICENSE) | ❌  |
| ✅ Qwen3-VL | 2B/4B/8B | [Blog](https://qwenlm.github.io/blog/qwen3-vl/) | [Hub](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) | [License](https://github.com/QwenLM/Qwen/blob/main/LICENSE) | ❌  |
| ✅ SmolVLM-Instruct | 2B | [Blog](https://huggingface.co/blog/smolvlm) | [Hub](https://huggingface.co/HuggingFaceTB/SmolVLM-Instruct) | Apache 2.0 | ✅  |

#### Base Models

| Model | Size | Paper | HF Hub | License | Open |
|-------|------|-------|---------|----------|------|
| ✅ SmolLM2 | 135M/360M/1.7B | [Blog](https://huggingface.co/blog/smollm) | [Hub](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) | Apache 2.0 | ✅ |
| ✅ Llama 3.2 | 1B/3B | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-3.2-3b) | [License](https://llama.meta.com/llama3/license/) | ❌  |
| ✅ Llama 3.1 | 8B/70B/405B | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-3.1-70b) | [License](https://llama.meta.com/llama3/license/) | ❌  |
| ✅ GPT-2 | 124M-1.5B | [Paper](https://arxiv.org/abs/2005.14165) | [Hub](https://huggingface.co/gpt2) | MIT | ✅ |
| DeepSeek V2 | 7B/13B | [Blog](https://www.deepseek.com/blogs/deepseek-v2) | [Hub](https://huggingface.co/deepseek-ai/deepseek-llm-7b-v2) | [License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) | ❌ |
| Gemma2 | 2B/9B | [Blog](https://ai.google.dev/gemma) | [Hub](https://huggingface.co/google/gemma2-7b) | [License](https://ai.google.dev/gemma/terms) | ❌ |
| GPT-J | 6B | [Blog](https://www.eleuther.ai/artifacts/gpt-j) | [Hub](https://huggingface.co/EleutherAI/gpt-j-6b) | Apache 2.0 | ✅ |
| GPT-NeoX | 20B | [Paper](https://arxiv.org/abs/2204.06745) | [Hub](https://huggingface.co/EleutherAI/gpt-neox-20b) | Apache 2.0 | ✅ |
| Mistral | 7B | [Paper](https://arxiv.org/abs/2310.06825) | [Hub](https://huggingface.co/mistralai/Mistral-7B-v0.1) | Apache 2.0 | ❌  |
| Mixtral | 8x7B/8x22B | [Blog](https://mistral.ai/news/mixtral-of-experts/) | [Hub](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) | Apache 2.0 | ❌  |
| MPT | 7B | [Blog](https://www.mosaicml.com/blog/mpt-7b) | [Hub](https://huggingface.co/mosaicml/mpt-7b) | Apache 2.0 | ✅ |
| OLMo | 1B/7B | [Paper](https://arxiv.org/abs/2402.00838) | [Hub](https://huggingface.co/allenai/OLMo-7B-hf) | Apache 2.0 | ✅ |
| ✅ Llama 4 Scout | 17B (Activated) 109B (Total) | [Paper](https://arxiv.org/abs/2407.21783) | [Hub](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E) | [License](https://llama.meta.com/llama4/license/) | ❌  |

#### Reasoning Models

| Model | Size | Paper | HF Hub | License | Open |
|-------|------|-------|---------|----------|------|
| ✅ gpt-oss | 20B/120B | [Paper](https://arxiv.org/abs/2508.10925) | [Hub](https://huggingface.co/openai/gpt-oss-120b) | Apache 2.0 | ❌  |
| ✅ Qwen3 | 0.6B-32B | [Paper](https://arxiv.org/abs/2505.09388) | [Hub](https://huggingface.co/Qwen/Qwen3-32B) | [License](https://github.com/QwenLM/Qwen/blob/main/LICENSE) | ❌  |
| ✅ Qwen3-Next | 80B-A3B | [Blog](https://qwenlm.github.io/blog/qwen3/) | [Hub](https://huggingface.co/Qwen/Qwen3-Next-80B-A3B) | [License](https://github.com/QwenLM/Qwen/blob/main/LICENSE) | ❌  |
| Qwen QwQ | 32B | [Blog](https://qwenlm.github.io/blog/qwq-32b-preview/) | [Hub](https://huggingface.co/Qwen/QwQ-32B-Preview) | [License](https://github.com/QwenLM/Qwen/blob/main/LICENSE) | ❌ |

#### Code Models

| Model | Size | Paper | HF Hub | License | Open |
|-------|------|-------|---------|----------|------|
| ✅ Qwen2.5 Coder | 0.5B-32B | [Blog](https://qwenlm.github.io/blog/qwen2.5/) | [Hub](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) | [License](https://github.com/QwenLM/Qwen/blob/main/LICENSE) | ❌  |
| DeepSeek Coder | 1.3B-33B | [Paper](https://arxiv.org/abs/2401.02954) | [Hub](https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct) | [License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) | ❌  |
| StarCoder 2 | 3B/7B/15B | [Paper](https://arxiv.org/abs/2402.19173) | [Hub](https://huggingface.co/bigcode/starcoder2-15b) | [License](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) | ✅ |

#### Math Models

| Model | Size | Paper | HF Hub | License | Open |
|-------|------|-------|---------|----------|------|
| DeepSeek Math | 7B | [Paper](https://arxiv.org/abs/2401.02954) | [Hub](https://huggingface.co/deepseek-ai/deepseek-math-7b-instruct) | [License](https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/LICENSE-MODEL) | ❌  | |

</details>

## 📖 Documentation

To learn more about all the platform's capabilities, see the [Oumi documentation](https://oumi.ai/docs).

## 🤝 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`](https://github.com/oumi-ai/oumi/blob/main/CONTRIBUTING.md) for guidance on how to contribute to send your first Pull Request.
- Make sure to join our [Discord community](https://discord.gg/oumi) to get help, share your experiences, and contribute to the project!
- If you are interested in joining one of the community's open-science efforts, check out our [open collaboration](https://oumi.ai/community) page.

## 🙏 Acknowledgements

Oumi makes use of [several libraries](https://oumi.ai/docs/en/latest/about/acknowledgements.html) 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:

```bibtex
@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](LICENSE) file for details.

[^1]: Open models are defined as models with fully open weights, training code, and data, and a permissive license. See [Open Source Definitions](https://opensource.org/ai) for more information.
