# Ask-Anything (VideoChat): a family of video-understanding chat models from OpenGVLab

> Ask-Anything is not one model but a repository of VideoChat variants that pair a vision encoder with a language model so you can ask questions about a video. The end-to-end VideoChat2 path is the one to use today; the older ChatGPT, MOSS and StableLM directories are historical.

**OpenGVLab/Ask-Anything** — [CVPR2024 Highlight][VideoChatGPT] ChatGPT with video understanding! And many more supported LMs such as miniGPT4, StableLM, and MOSS.

- Repository: https://github.com/OpenGVLab/Ask-Anything
- Website: https://vchat.opengvlab.com/
- Stars: 3,354 · Forks: 269
- Language: Python
- License: MIT
- Published: 2026-09-14 · Updated: 2026-09-14 · Language: en
- Canonical page: https://hysenlabs.com/projects/opengvlab-ask-anything

## What Ask-Anything actually is, beyond the name

The name is misleading. Ask-Anything is not a general question-answering service and not a ChatGPT plugin. It is a research repository from OpenGVLab that collects several generations of VideoChat, a model family that takes a video plus a natural-language question and produces an answer. The README describes VideoChat2 as "a robust baseline built on UMT and Vicuna-v0", which is the clearest statement of what the current code does: a video encoder (UMT) feeds visual tokens into a language model (Vicuna) that has been instruction-tuned on video and image chat data.

The audience is narrow and specific. You are a researcher or engineer who wants an open checkpoint you can run on your own hardware, fine-tune, or benchmark against. The repository also ships MVBench, described as "a comprehensive benchmark for video understanding", and 2M instruction samples under video_chat2/DATA.md. That combination (model, benchmark, training data) is what the project is really selling, and it is why the CVPR2024 Highlight tag appears in the description.

The top-level layout tells the history. Directories named video_chat_with_ChatGPT, video_chat_with_MOSS, video_chat_with_StableLM and video_miniGPT4 are early 2023 experiments that routed video captions into existing chatbots. video_chat and video_chat2 are the end-to-end models. If you are evaluating the project in 2026, only video_chat2 matters for new work.

## How VideoChat2 turns frames into answers

The data flow is a two-stage pipeline. First, video frames are sampled and encoded by UMT into a sequence of visual embeddings. Second, those embeddings are projected into the language model's token space and concatenated with the tokenized question, so Vicuna generates the answer conditioned on both. This is the standard multimodal design the README implies when it calls VideoChat2 a baseline built on UMT and Vicuna-v0.

The practical consequence is that frame sampling is the dominant design decision. The model sees a fixed budget of frames, not every frame, so questions that depend on a brief event between samples will fail regardless of how good the language model is. The repository acknowledges this pressure indirectly: the 2024/06/07 entry introduces VideoChat2_HD, fine-tuned with high-resolution data, and the 2024/06/06 entry introduces VideoChat2_phi3 as "a faster model". Those are two different answers to the same constraint, one buying detail and one buying speed.

There is also a vLLM branch. The 2024/06/25 update points to a branch of videochat2 using vllm to speed up inference. That branch is separate from main, so if throughput matters you are choosing between the maintained main line and a branch that the README does not describe in detail.

## Getting started with VideoChat2 without inventing commands

The top-level README does not list install commands. Its Getting Started section is a set of links: End2End points at video_chat#running-usage, and the other entries point at the ChatGPT, MOSS, StableLM and miniGPT4 directories. For VideoChat2, the README directs you to the video_chat2 directory and its own documentation, which is where the environment, checkpoint and demo instructions live. Anything beyond that, including exact Python versions, requirements files and demo entry points, has to come from those files rather than from this page.

What the README does give you is a set of ready paths into the model. It links two Hugging Face Spaces, OpenGVLab/VideoChatGPT for "[VideoChat-7B-8Bit] End2End ChatBOT for video and image" and OpenGVLab/InternVideo2-Chat-8B-HD, plus an OpenXLab app for VideoChat2. If you want to see the behaviour before committing to a local install, those hosted demos are the intended route, and they are the only runnable artifacts the README documents directly.

For local work, the example/ directory ships two clips, hitting_baseball.mp4 and yoga.mp4. They are the obvious first inputs once you have the video_chat2 demo running: upload one, ask what is happening, and check the answer against what you see. The demo notebooks are also named in the updates, demo_mistral.ipynb and demo_mistral_hd.ipynb under video_chat2/demo, and the README says those are the scripts for testing EgoSchema and Video-MME. Starting from a notebook rather than a bare script is the lower-risk path, because the notebook carries its own setup cells.

## Where VideoChat2 breaks down

The frame-sampling design is the first failure mode. Anything that requires temporal precision finer than the sampling interval is invisible to the model. Counting, ordering of fast events, and reading small on-screen text are all weak spots for this class of architecture, and nothing in the README claims otherwise.

The second is cost. This is a local model with two large components. You need a GPU with enough memory for both the vision encoder and the language model, plus disk for the checkpoints. The README offers VideoChat2_phi3 as a faster alternative and a vLLM branch for inference speed, which is an admission that the default path is heavy.

The third is scope. Ask-Anything is not a video search engine, not a transcription tool, and not a general assistant. It answers questions about a clip you give it. If you need keyword search across an archive, or you need transcripts, this is the wrong tool and you will spend more effort bending it than picking something built for that job.

Finally, the README is thin on operational detail. It does not document rollback, does not describe how to update checkpoints in place, and does not state hardware minimums in the text available. You will be reading the video_chat2 subdirectory and the papers to fill those gaps.

## Ask-Anything versus LLaVA-style image-first stacks

The closest comparison in the README itself is MiniGPT-4. The video_miniGPT4 directory is described as a "simple extension of MiniGPT-4" that communicates implicitly with Vicuna and is "not sensitive with time". That phrase is the whole difference. MiniGPT-4 was built for single images; the Ask-Anything variant bolts video on by feeding frames through an image model, which means the temporal relationship between frames is not modelled explicitly.

VideoChat2 takes the other route. It trains the visual encoder and the projection on video instruction data from the start, and the README ties its results to video benchmarks (MVBench, NExT-QA, STAR, TVQA, EgoSchema, IntentQA, Video-MME). If your questions are about motion, order, or events unfolding over time, the video-native path is the one that was designed for it. If your inputs are mostly still frames with occasional short clips, a MiniGPT-4-style stack is lighter and the temporal machinery is wasted on you.

A second alternative is the project's own successor. The 2026/07/17 update announces VideoChat3, described as "a fully open, efficient 4B Video MLLM for general, long-form, and streaming video understanding", hosted in a different repository. For long or streaming video, that is where the project points, not at the code in this repository.

## Maintenance, licence and what upgrading costs

The repository is not archived, and the last push was on 2026-07-17. That push corresponds to the VideoChat3 announcement, which is a pointer to another repository rather than a change to the VideoChat2 code in this one. The most recent entries that touch this codebase directly are from mid-2024: the vLLM branch in June 2024, the HD and phi3 models, and the MVBench QA fix that the README says "may only affect the results by 0.5%".

Read that as a stable-but-quiet codebase. Nothing suggests active development of the video_chat2 path, and the project's own update trail moves to VideoChat-Flash, TPO and VideoChat3. If you adopt Ask-Anything, budget for the possibility that fixes come from you rather than upstream.

Upgrade cost is dominated by checkpoints, not code. Swapping VideoChat2 for VideoChat2_HD or VideoChat2_phi3 means downloading new weights and re-validating your prompts, because the README presents them as differently fine-tuned models with different benchmark profiles, not as drop-in patches.

The licence is MIT, which is permissive and permits commercial use and modification. That applies to the repository code. Model weights and the instruction data may carry their own terms from their upstream sources, and the README does not consolidate those terms in one place. Check each checkpoint's own licence before you ship anything; this is a description of what the repository states, not legal advice.

## Conclusion

Adopt Ask-Anything if you need an open, MIT-licensed video question-answering baseline and are willing to run a multi-gigabyte model locally on a GPU. Do not adopt it if you want a hosted API, if your videos are long and you expect frame-level precision, or if you are looking for the ChatGPT wrapper that gave the project its name: that directory is legacy. Before committing, verify that the video_chat2 environment resolves on your CUDA version, check the download size of the UMT and Vicuna checkpoints against your disk, and read video_chat2/DATA.md to see whether the 2M instruction samples match your domain.

## FAQ

### What is Ask-Anything?

It is OpenGVLab's repository for the VideoChat family, a set of models that answer natural-language questions about a video. The current end-to-end model is VideoChat2, built on UMT and Vicuna-v0.

### Is Ask-Anything an AI product?

It is an AI research repository rather than a hosted product. You install it locally, download checkpoints, and run a demo, or use the Hugging Face Spaces links the README provides.

### Can I ask anything to ChatGPT and get video answers?

Ask-Anything does include a video_chat_with_ChatGPT directory, but the README places it among the early 2023 experiments. It works by sending video captions to ChatGPT, which the project notes is sensitive to time, unlike the end-to-end models.

### Can I ask anything to Ask-Anything, or is it limited to video?

The models answer questions about a video or an image you supply. The README describes VideoChat1 as instruction tuning for video chatting that "also supports image one", and the VideoChatGPT Space as an end-to-end chatbot for video and image.

### What is an ask me anything, and is that what this project is?

No. An ask-me-anything is a format for open public Q&A with a person. Ask-Anything is a video-understanding model repository, and its questions are about the content of a clip rather than about a speaker.

## Sources

- [Issues](https://github.com/OpenGVLab/Ask-Anything/issues)
- [License: MIT](https://github.com/OpenGVLab/Ask-Anything/blob/main/LICENSE)
- [OpenGVLab/Ask-Anything on GitHub](https://github.com/OpenGVLab/Ask-Anything)
- [Project website](https://vchat.opengvlab.com/)
- [README](https://github.com/OpenGVLab/Ask-Anything/blob/main/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/opengvlab-ask-anything