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LatestinclusionAI1.5
inclusionAIAny-to-Any

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.

Jul 15, 2025
NotableMIT
Ming-Lite-Omni 1.5

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.

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.

Sources

  • inclusionAI/Ming-Lite-Omni-1.5

    Hugging Face

    Visit
OlderPusa V1: A New Open Model for Image-to-Video AnimationRaphaelLiu · Image → Video · last yearNewerHiDream.ai Releases 17B Open Image Editing ModelHiDream.ai · Image Editing · last year

Get the model

Hugging Face

Specs

Context window33K tokens
Size37.8 GB
PrecisionBF16
ArchitectureBailingMMNativeForConditionalGeneration
LicenseMIT
Downloads1.7K
Likes84

Can you run it?

Runs on a laptop — about 12.8 GB at 4-bit.

  • BF16 (as published)

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    39.3 GB
  • 8-bit

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    21.5 GB
  • 4-bit

    16 GB GPU (RTX 4080 / 5070 Ti) · 24 GB Mac

    12.8 GB
Your machine
  • Mixture-of-experts: every expert must be loaded, so memory follows total parameters, not the active slice.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Any-to-Any

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