Chatclient.ai
LLaVA
Chatclient.ai | LLaVA | |
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- | 21 | |
0 | 17,273 | |
- | - | |
4.3 | 9.1 | |
10 months ago | 15 days ago | |
Python | Python | |
- | Apache License 2.0 |
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Chatclient.ai
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Tracking mentions began in Dec 2020.
LLaVA
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PaliGemma: Open-Source Multimodal Model by Google
Here's a tutorial https://wandb.ai/byyoung3/ml-news/reports/How-to-Fine-Tune-L...
There's not really a super easy to use software solution yet, but a few different ones have cropped up. Right now you'll have to read papers to get the training recipes.
- https://github.com/haotian-liu/LLaVA/blob/main/scripts/finet...
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Show HN: I Remade the Fake Google Gemini Demo, Except Using GPT-4 and It's Real
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
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Image-to-Caption Generator
https://github.com/haotian-liu/LLaVA (fairly established and well supported)
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Llamafile lets you distribute and run LLMs with a single file
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...
- FLaNK Stack Weekly for 27 November 2023
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Using GPT-4 Vision with Vimium to browse the web
There are open source models such as https://github.com/THUDM/CogVLM and https://github.com/haotian-liu/LLaVA.
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Is supervised learning dead for computer vision?
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)
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Adept Open Sources 8B Multimodal Modal
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
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AI — weekly megathread!
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].
- LLaVA: Visual Instruction Tuning: Large Language-and-Vision Assistant
What are some alternatives?
MiniGPT-4 - Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
CogVLM - a state-of-the-art-level open visual language model | 多模态预训练模型
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
llama.cpp - LLM inference in C/C++
mPLUG-Owl - mPLUG-Owl & mPLUG-Owl2: Modularized Multimodal Large Language Model
llamafile - Distribute and run LLMs with a single file.
image2dsl - This repository contains the implementation of an Image to DSL (Domain Specific Language) model. The model uses a pre-trained Vision Transformer (ViT) as an encoder to extract image features and a custom Transformer Decoder to generate DSL code from the extracted features.
Segment-Everything-Everywhere-
LaVIN - [NeurIPS 2023] Official implementations of "Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models"
openai-gpt4 - decentralising the Ai Industry, free gpt-4/3.5 scripts through several reverse engineered api's ( poe.com, phind.com, chat.openai.com, phind.com, writesonic.com, sqlchat.ai, t3nsor.com, you.com etc...) [Moved to: https://github.com/xtekky/gpt4free]
vimGPT - Browse the web with GPT-4V and Vimium
langchain - 🦜🔗 Build context-aware reasoning applications