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Oumi - an open-source AI platform that supports model training with 10 million to 405 billion parameters.

Oumi is a fully open-source AI platform that simplifies the entire lifecycle from data preparation and model training to evaluation and deployment. It supports model training with 10 million to 405 billion parameters, covering text and multimodal models (such as Lla...).

What is Oumi?

Oumi is a fully open-source AI platform that simplifies the entire lifecycle from data preparation and model training to evaluation and deployment. It supports model training with 10 million to 405 billion parameters, covering text and multimodal models (such as Llama and Qwen), and provides a zero-boilerplate development experience.

Oumi's main functions

  • Model training and fine-tuningIt supports a variety of training techniques, such as Supervised Fine-Tuning (SFT), LoRA, QLoRA, DPO, etc.
  • Multimodal supportSupports the training and deployment of text and multimodal models.
  • Data Synthesis and ManagementIt can synthesize and organize training data through the LLM (Large Language Model) evaluator.
  • Efficient deploymentIt supports a variety of popular inference engines (such as vLLM and SGLang) and can run locally, in a cluster, or in the cloud (AWS, Azure, GCP, etc.).
  • Enterprise-level supportWe provide customized model development, secure and reliable AI solutions, and expert support.

Oumi's technical principles

  • Zero BoilerplateOumi simplifies the AI development process through a highly abstract design. Developers do not need to write a lot of repetitive code; they can simply define the model's training parameters, data paths, training strategies, etc., through simple configuration files (such as YAML format).
  • Flexible training frameworkOumi supports various training techniques, including Supervised Fine-Tuning (SFT), LoRA (Low-Rank Adaptation), QLoRA (Quantization + LoRA), and DPO (Direct Preference Optimization). Developers can choose the appropriate training method based on their specific needs to optimize model performance.
  • Distributed trainingOumi optimizes the distributed training process, supporting training tasks on multiple GPUs and multiple nodes. Developers can efficiently train large models on large datasets, maintaining the stability and scalability of the training process.

Oumi's project address

Oumi's application scenarios

  • autonomous drivingIt integrates data from sensors such as images, radar, and sonar to achieve comprehensive environmental perception and obstacle detection.
  • Human-computer interactionIt combines voice, image, and text information to achieve a more natural and intelligent human-computer interaction.
  • academic researchIt supports researchers in conducting experiments and developing models quickly, ensuring the reproducibility of experiments.
  • Virtual Reality and Augmented Reality: Generate realistic virtual environments through multimodal models to enhance user experience.
  • Intelligent Customer ServiceIn the e-commerce and finance sectors, we provide intelligent customer service to improve user satisfaction.
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