OneLLM

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OneLLM is an AI Frameworks & Infra tool that provides a unified framework to align all modalities with language. It was accepted by CVPR 2024 and offers model weights and inference code.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is OneLLM?

OneLLM is a comprehensive framework designed to align diverse modalities, such as image, video, audio, point cloud, depth, normal, IMU, and fMRI, with language models. This innovative tool, accepted by CVPR 2024, provides a unified approach for cross-modal learning and AI model development. It offers readily available model weights and inference code, facilitating easy integration and experimentation. Developers can leverage OneLLM for various tasks including image-text pretraining, multimodal-text pretraining across multiple stages, and instruction tuning. The framework supports both single-node and multi-node training configurations, making it adaptable for different computational environments.

Best used for

Ideal for developers and data scientists who need to build AI models capable of understanding and processing information from various modalities, conduct advanced multimodal pretraining, and fine-tune models for specific instructions. Especially valuable for researchers and practitioners working on cutting-edge multimodal AI applications.

Common actions

align multimodal data

develop AI models

conduct cross-modal learning

pretrain language models

workflowscollaborationopen-sourcegithub copilotface swappinglow-code/no-codedeepfake"AI Agents"automated workflow

Capabilities

Key features

Align all modalities
Model weights release
Inference code
Image-text pretraining
Multimodal-text pretraining
Instruction tuning

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What modalities can OneLLM align with language?

OneLLM is designed to align a wide range of modalities with language, including image, video, audio, point cloud, depth, normal, IMU, and fMRI data. This allows for comprehensive cross-modal understanding and generation within a single framework.

Does OneLLM provide pre-trained models?

Yes, OneLLM provides pre-trained model weights on Hugging Face, specifically the csuhan/OneLLM-7B model. This allows users to quickly get started with the framework without needing to train models from scratch.

What kind of training capabilities does OneLLM offer?

OneLLM supports various training stages, including image-text pretraining, multi-stage multimodal-text pretraining (Stage II for video-audio-point-text, Stage III for depth-normal-imu-fmri-text), and instruction tuning. It also supports both single-node and multi-node DDP/SLURM training configurations.

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