LLaVA-OneVision on live video

What LLaVA-OneVision is good at

LLaVA-OneVision's core idea is transferring learned task capability across visual settings: skills picked up from single-image data carry over to multi-image and video tasks because all three share one training recipe and one model family. That gives it broad, general-purpose visual reasoning rather than a narrow specialty.

The family spans 0.5B to 72B parameters, so the same recipe covers everything from lightweight edge deployment to flagship-class quality. It has also become one of the most-forked open VLM training stacks, which means its architecture and data choices are well understood and widely reused.

• General visual question answering across single and multi-image inputs
• Video understanding using the same recipe as image tasks
• A size range that fits both edge and flagship deployments

LLaVA-OneVision and Overshoot's streaming workflow

LLaVA-OneVision is not currently in Overshoot's live model catalog. For models that are, the workflow is: publish a camera or screen share over WebRTC to open a Stream, then send a chat-completions request whose image_url or video_url is an ovs:// reference. Requests point at the latest frame for a live read, or a recent segment, sampled at up to 1 fps by default, when the question depends on motion over a few seconds.

Catalog models stream answers back over SSE with low time to first token, and using the same thread_id across a session keeps prompt caching effective for repeated queries against one stream. LLaVA-OneVision's unified image-and-video recipe would map naturally onto that segment-based querying if it were available through the API.

LLaVA-OneVision among open VLMs

LLaVA-OneVision predates newer open flagships like InternVL3 and Qwen2.5-VL, but its Apache 2.0 license and multi-size lineup keep it relevant where unrestricted commercial use and deployment flexibility matter more than chasing the newest benchmark numbers. Teams that need a well-documented, widely reused baseline for fine-tuning often start here before moving to a larger, newer model.

Frequently asked questions

Can LLaVA-OneVision analyze live video?

LLaVA-OneVision was trained on a video task setting as part of its unified recipe, so it handles sampled video frames well. It is not currently in Overshoot's live model catalog, so it cannot read Overshoot streams today. Catalog models read a live WebRTC stream referenced with an ovs:// URL and stream answers back over SSE.

Is LLaVA-OneVision open source?

Yes. It ships under Apache 2.0, which permits unrestricted commercial use, self-hosting, and fine-tuning, and is part of why it became one of the most widely forked open VLM training stacks.

Is LLaVA-OneVision available on Overshoot?

Not currently. LLaVA-OneVision, in any of its 0.5B to 72B sizes, is not in Overshoot's live model catalog. The catalog changes over time, so check GET /v1beta/models for the current list of models available through the API.

How does LLaVA-OneVision compare to newer open VLMs like InternVL3 or Qwen2.5-VL?

Newer models generally push higher on benchmark quality, but LLaVA-OneVision remains a strong choice where its Apache 2.0 license, size flexibility, and well-understood training recipe matter more than being on the newest architecture.

Related models

• Qwen2.5-VL 7B
• InternVL3 78B
• Phi-3.5 Vision
• Moondream 2