LLaVA VS vimGPT

Update: For anyone else facing the commercial use question on LLaVA - it is licensed under Apache 2.0. Can be used commercially with attribution: https://github.com/haotian-liu/LLaVA/blob/main/LICENSE

https://github.com/haotian-liu/LLaVA (fairly established and well supported)

That's not a llamafile thing, that's a llava-v1.5-7b-q4 thing - you're running the LLaVA 1.5 model at a 7 billion parameter size further quantized to 4 bits (the q4).

GPT4-Vision is running a MUCH larger model than the tiny 7B 4GB LLaVA file in this example.

LLaVA have a 13B model available which might do better, though there's no chance it will be anywhere near as good as GPT-4 Vision. https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZO...

There are open source models such as https://github.com/THUDM/CogVLM and https://github.com/haotian-liu/LLaVA.

Hey Everyone,

I’ve been diving deep into the world of computer vision recently, and I’ve gotta say, things are getting pretty exciting! I stumbled upon this vision-language model called LLaVA (https://github.com/haotian-liu/LLaVA), and it’s been nothing short of impressive.

In the past, if you wanted to teach a model to recognize the color of your car in an image, you’d have to go through the tedious process of training it from scratch. But now, with models like LLaVA, all you need to do is prompt it with a question like “What’s the color of the car?” and bam – you get your answer, zero-shot style.

It’s kind of like what we’ve seen in the NLP world. People aren’t training language models from the ground up anymore; they’re taking pre-trained models and fine-tuning them for their specific needs. And it looks like we’re headed in the same direction with computer vision.

Imagine being able to extract insights from images with just a simple text prompt. Need to step it up a notch? A bit of fine-tuning can do wonders, and from my experiments, it can even outperform models trained from scratch. It’s like getting the best of both worlds!

But here’s the real kicker: these foundational models, thanks to their extensive training on massive datasets, have an incredible grasp of image representations. This means you can fine-tune them with just a handful of examples, saving you the trouble of collecting thousands of images. Indeed, they can even learn with a single example (https://www.fast.ai/posts/2023-09-04-learning-jumps)

Fuyu is not open source. At best, it is source-available. It's also not the only one.

A few other multimodal models that you can run locally include IDEFICS[0][1], LLaVA[2], and CogVLM[3]. I believe all of these have better licenses than Fuyu.

[0]: https://huggingface.co/blog/idefics

[1]: https://huggingface.co/HuggingFaceM4/idefics-80b-instruct

[2]: https://github.com/haotian-liu/LLaVA

[3]: https://github.com/THUDM/CogVLM

Researchers released LLaVA-1.5. LLaVA (Large Language and Vision Assistant) is an open-source large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding. LLaVA-1.5 achieved SoTA on 11 benchmarks, with just simple modifications to the original LLaVA and completed training in ~1 day on a single 8-A100 node [Demo | Paper | GitHub].

Hi all, I’m wondering if there is a version of LLaVA https://github.com/haotian-liu/LLaVA that works with gguf and ggml models?? I know there is one for miniGPT4 but it just doesn’t seem as reliable as LLaVA but you need at least 24gb of vRAM for LLaVA to run it locally by the looks of it. The 4bit version still requires 12gb vram.

It's insane that this is now possible:

https://github.com/ishan0102/vimGPT/blob/682b5e539541cd6d710...

> "You need to choose which action to take to help a user do this task: {objective}. Your options are navigate, type, click, and done. Navigate should take you to the specified URL. Type and click take strings where if you want to click on an object, return the string with the yellow character sequence you want to click on, and to type just a string with the message you want to type. For clicks, please only respond with the 1-2 letter sequence in the yellow box, and if there are multiple valid options choose the one you think a user would select. For typing, please return a click to click on the box along with a type with the message to write. When the page seems satisfactory, return done as a key with no value. You must respond in JSON only with no other fluff or bad things will happen. The JSON keys must ONLY be one of navigate, type, or click. Do not return the JSON inside a code block."