# Ming-Lite-Omni 1.5 Brings Any-to-Any Modality to Open Source

> The new MIT-licensed model from inclusionAI can process and generate a mix of text, images, audio, and video, pushing the boundaries of open multimodal AI.

Published by The Open Weights on Jul 15, 2025. Canonical: https://theopenweights.com/news/ming-lite-omni-1-5-oiyw

## Key facts

- Company: inclusionAI
- Model: Ming-Lite-Omni 1.5
- Version: 1.5
- Category: Any-to-Any
- Modalities: Any-to-Any
- License: MIT (Open weights, commercial use allowed)
- Context window: 33K tokens
- Architecture: BailingMMNativeForConditionalGeneration
- Precision: BF16
- Weights size: 37.8 GB
- Significance: notable
- Published: 2025-07-15
- Last verified: 2026-06-17
- Hugging Face: https://huggingface.co/inclusionAI/Ming-Lite-Omni-1.5
- Canonical URL: https://theopenweights.com/news/ming-lite-omni-1-5-oiyw

Startup inclusionAI has released Ming-Lite-Omni 1.5, a new open-source model designed to handle a wide array of data types simultaneously. Published under a permissive MIT license, the model aims to provide "any-to-any" omni-modal capabilities, a significant step forward for generalized AI research and development. The model and its components are available now on [Hugging Face](https://huggingface.co/inclusionAI/Ming-Lite-Omni-1.5).

Unlike many multimodal models that operate on a fixed input-to-output path (like text-to-image), an omni-modal system is designed to fluidly process and generate content across various formats. Ming-Lite-Omni can reportedly understand and create content using text, images, audio, and video, allowing for more complex and integrated AI applications.

## A Flexible Foundation for Multimodal AI

The model's true significance lies in its combination of advanced architecture and an unrestrictive license. This opens the door for developers and researchers to experiment with sophisticated multimodal tasks that have largely been the domain of closed, proprietary systems. Potential applications could include:

*   Generating a video with a descriptive soundtrack from a single text prompt.
*   Creating a detailed textual summary of an audio-visual recording.
*   Answering questions about a video by analyzing both its frames and its spoken audio.

While specific benchmarks have not been released, the "Lite" designation in its name suggests that Ming-Lite-Omni may be a more computationally accessible version of this complex technology. Its release provides a valuable new tool for building the next generation of AI that can see, hear, and communicate in multiple dimensions.

## Get the model

- [Hugging Face](https://huggingface.co/inclusionAI/Ming-Lite-Omni-1.5)

## Sources

- [inclusionAI/Ming-Lite-Omni-1.5](https://huggingface.co/inclusionAI/Ming-Lite-Omni-1.5) — Hugging Face, Jul 15, 2025

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "Ming-Lite-Omni 1.5 Brings Any-to-Any Modality to Open Source", The Open Weights, Jul 15, 2025, https://theopenweights.com/news/ming-lite-omni-1-5-oiyw