JSLT VS MiniGPT-4

I follow that approach in this open-source repo, and since PRs come from volunteers I've sometimes just updated the docs afterwards myself. But in a work situation you could be strict about it.

I made this language. It was a lot of fun, and has been very useful.

If you're working in Java I would look at JSLT for the JSON transforms. (Disclaimer: I created JSLT, for a similar use case.)

You could use a transformation language like JSLT to write a transform, then apply it to all files. Not sure if that's suitable for you.

I just think the tech has been out for so long it's not as big of a deal. Mini-Gpt4 has been out for 6 months! Of course the descriptions aren't exactly gpt-4 grade, but with mistral 7b being used as the language model instead of llama 7b, the reasoning ability will improve noticeably.

[1] https://github.com/Vision-CAIR/MiniGPT-4

Isn't there only two open multimodal LLMs, LLaVA and mini-gpt4?

For LLM visual instruction, you can use LLaVA, LaVIN, or MiniGPT-4.

Please read the rules before posting. If you want a model for visual instruction, use LLaVA, LaVIN, or MiniGPT-4.

So we use MiniGPT-4 for image parsing, and yep it does return a pretty detailed (albeit not always accurate) description of the photo. You can actually play around with it on Huggingface here.

We use MiniGPT-4 first to interpret the image and then pass the results onto GPT-4. Hopefully, once GPT-4 makes its multi-modal functionality available, we can do it all in one request.

But I would like to bring up that there are some multi models(llava, miniGPT-4) that are built based on censored llama based models like vicuna. I tried several multi modal models like llava, minigpt4 and blip2. Llava has very good captioning and question answering abilities and it is also much faster than the others(basically real time), though it has some hallucination issue.