Video-LLaMA
LLaVA
Video-LLaMA | LLaVA | |
---|---|---|
8 | 21 | |
2,455 | 16,713 | |
5.8% | - | |
6.6 | 9.3 | |
6 days ago | 1 day ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Video-LLaMA
- Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
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OpenAI vs Google, Detect ChatGPT Content with 99% accuracy, Navigating AI compute costs
👀 Video-LLaMA - Empower large language models with video and audio understanding capability. (link) 🦦 Otter - Multi-modal model with improved instruction-following and in-context learning ability. 🔗 Linkly.AI - AI-powered lead analytics and management platform that helps you track, analyze, and streamline your leads in one place. 🎬 Jet Cut Ready - AI plugin for Adobe Premiere Pro that automatically removes silent parts in videos. (link) 💬 HeyGen's ChatGPT Plugin - Convert text into high-quality videos using AI text and video generation.
- Video-LLaMA: Instruction-Tuned Audio-Visual Lang Model for Video Understanding
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Unleash the Power of Video-LLaMA: Revolutionizing Language Models with Video and Audio Understanding!
Prepare to be blown away by the cutting-edge Video-LLaMA project! We're pushing the boundaries of language models by equipping them with the remarkable ability to comprehend video and audio. Get ready for an extraordinary adventure! 🌟
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Video-LLaMA An Instruction-tuned Audio-Visual Language Model for Video Understanding
Source Code: The codebase for pre-training and fine-tuning the Video-LLaMA model as well as the model weights are available on GitHub: https://github.com/DAMO-NLP-SG/Video-LLaMA
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Video-ChatGPT: Redefining Interactions with Visual Data
Tons of cool stuff happening in the space, also recently saw the LLaMa-Video version of this - https://github.com/DAMO-NLP-SG/Video-LLaMA
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Meet Video-LLaMA: A Multi-Modal Framework that Empowers Large Language Models (LLMs) with the Capability of Understanding both Visual and Auditory Content in the Video
Code: https://github.com/DAMO-NLP-SG/Video-LLaMA
LLaVA
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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
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LLaVA gguf/ggml version
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.
What are some alternatives?
mPLUG-Owl - mPLUG-Owl & mPLUG-Owl2: Modularized Multimodal Large Language Model
MiniGPT-4 - Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
NExT-GPT - Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model
CogVLM - a state-of-the-art-level open visual language model | 多模态预训练模型
Otter - 🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability.
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
Chinese-LLaMA-Alpaca - 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)
MiniGPT-4-discord-bot - A true multimodal LLaMA derivative -- on Discord!
llama.cpp - LLM inference in C/C++
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.
llamafile - Distribute and run LLMs with a single file.