I want to talk about a new platform aiming to make AI Models fully open source. **They’ve got 1229 stars from Github just today(2nd February, 2025)**
## Oumi: Open Universal Machine Intelligence
**[Oumi](https://oumi.ai?utm_source=mehmetakar_devto)** is a fully open-source platform designed to simplify the lifecycle of foundation models, from data preparation and training to evaluation and deployment. Whether you’re working on a small-scale project or training large models, Oumi offers the necessary tools and workflows with one consistent API.
### About Oumi
Oumi was founded by former **Google and Apple AI engineers**, aiming to address the transparency and accessibility challenges in AI research. The company is backed by **13 leading research universities**, including **Princeton, Stanford, MIT, UC Berkeley, University of Oxford, University of Cambridge, University of Waterloo, and Carnegie Mellon**.
Unlike traditional AI development models, Oumi adopts a **distributed approach** by leveraging university clusters and cloud-based computing instead of relying on massive centralized data centers. With a **$10 million seed round**, the company is focused on building a truly open and collaborative AI ecosystem.
According to **Manos Koukoumidis**, Oumi’s CEO and a former **Google Cloud AI senior engineering manager**, existing AI models like DeepSeek and Llama offer limited transparency. **Oumi’s goal** is to remove these barriers by providing complete access to model architectures, training data, and methodologies.
### Key Features
– 🚀 **Scalability**: Train models from 10M to 405B parameters with state-of-the-art techniques.
– 🤖 **Model Variety**: Supports Llama, DeepSeek, Qwen, Phi, and more.
– 🔄 **Data Synthesis & Curation**: Use LLM judges for dataset refinement.
– ⚡ **Optimized Inference**: Deploy with vLLM and SGLang engines.
– 📊 **Evaluation**: Benchmark models efficiently.
– 🌎 **Cross-Platform Compatibility**: Run on laptops, clusters, or cloud services like AWS, Azure, GCP, and Lambda.
– 🔌 **API Integration**: Works with OpenAI, Anthropic, Vertex AI, and more.
—
## 🚀 Getting Started
### Installation
Oumi is easy to install and set up:
“`sh
# Install the package (CPU & NPU only)
pip install oumi
# OR, with GPU support (Requires Nvidia or AMD GPU)
pip install oumi[gpu]
# Install the latest version from the source
git clone https://github.com/oumi-ai/oumi.git
cd oumi
pip install .
“`
For additional installation options, check out the [Installation Guide](https://oumi.ai/docs/en/latest/get_started/installation.html).
### Basic Usage
Once installed, you can start training, evaluating, and running inference with Oumi using simple CLI commands.
#### 🔧 Training a Model
“`sh
oumi train -c configs/recipes/smollm/sft/135m/quickstart_train.yaml
“`
#### 📋 Evaluating a Model
“`sh
oumi evaluate -c configs/recipes/smollm/evaluation/135m/quickstart_eval.yaml
“`
#### 🤖 Running Inference
“`sh
oumi infer -c configs/recipes/smollm/inference/135m_infer.yaml –interactive
“`
### Running Jobs Remotely
Oumi supports remote training on cloud platforms like AWS, GCP, and Azure.
“`sh
# Deploy on AWS\oumi launch up -c configs/recipes/smollm/sft/135m/quickstart_aws_job.yaml
“`
More details on cloud integrations can be found in the [Cloud Deployment Guide](https://oumi.ai/docs/en/latest/user_guides/train/train.html?utm_source=mehmetakar_devto).
—
## 🛠 Advanced Features
### 🏗 Fine-Tuning with LoRA
Oumi supports **LoRA**, **QLoRA**, and **DPO** for efficient fine-tuning of large models.
“`sh
oumi train -c configs/recipes/llama3_1/sft/8b_lora/train.yaml
“`
### ⚡ Inference with vLLM
For efficient inference at scale:
“`sh
oumi infer -c configs/recipes/llama3_1/inference/8b_rvllm_infer.yaml
“`
### 📈 Evaluating Across Benchmarks
Oumi provides built-in evaluation tools for model benchmarking.
“`sh
oumi evaluate -c configs/recipes/llama3_1/evaluation/8b_eval.yaml
“`
—
## 🌟 Why Choose Oumi?
– **Zero Boilerplate**: Start quickly with pre-configured recipes.
– **Enterprise-Grade**: Designed for large-scale model training.
– **Research-Friendly**: Easily reproducible experiments.
– **Broad Model Support**: Works with small to massive models.
– **Optimized Performance**: Supports distributed training.
– **Open Source**: Community-driven, free to use.
Oumi’s **radical approach** contrasts with AI giants like OpenAI, which invest billions into massive infrastructure projects. Oumi proves that **high-quality AI models can be built and trained without centralized data centers**, using **collaborative university-based computing**.
For more details, visit the official **[Oumi Documentation](https://oumi.ai/docs?utm_source=mehmetakar_devto)**.
—