LLaVA
langchain
LLaVA | langchain | |
---|---|---|
21 | 36 | |
16,713 | 84,994 | |
- | 5.2% | |
9.3 | 10.0 | |
6 days ago | 4 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
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.
langchain
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Deploy LangServe Application to AWS
Limited by the current packaging method of Pluto, it does not yet support LangChain's Template Ecosystem. Coming soon
- Construyendo un asistente genAI de WhatsApp con Amazon Bedrock
- Show HN: SpRAG – Open-source RAG implementation for challenging real-world tasks
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Aider: AI pair programming in your terminal
Big fan of Aider.
We are interesting in integrating Aider as a tool for Dosu https://dosu.dev/ to help it navigate and modify a codebase on issues like this https://github.com/langchain-ai/langchain/issues/8263#issuec...
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🦙 Llama-2-GGML-CSV-Chatbot 🤖
Developed using Langchain and Streamlit technologies for enhanced performance.
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Building a WhatsApp generative AI assistant with Amazon Bedrock and Python
Tip: Kenton Blacutt, an AWS Associate Cloud App Developer, collaborated with Langchain, creating the Amazon Dynamodb based memory class that allows us to store the history of a langchain agent in an Amazon DynamoDB.
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👑 Top Open Source Projects of 2023 🚀
LangChain was first released in October 2022 as an open-source side project, a framework that makes developing AI applications more flexible. It got so popular that it was promptly turned into a startup.
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Fuck You, Show Me the Prompt
> Furthermore, the prompt has a spelling error (Let'w) and also overly focuses on the negative about identifying errors - which makes me skeptical that this prompt has been optimized or tested.
Fixed in https://github.com/langchain-ai/langchain/commit/7c6009b76f0...
- LangChain Repository Disappeared
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🆓 Local & Open Source AI: a kind ollama & LlamaIndex intro
Being able to plug third party frameworks (Langchain, LlamaIndex) so you can build complex projects
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/)
llama_index - LlamaIndex is a data framework for your LLM applications
CogVLM - a state-of-the-art-level open visual language model | 多模态预训练模型
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
mPLUG-Owl - mPLUG-Owl & mPLUG-Owl2: Modularized Multimodal Large Language Model
griptape - Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.
llama.cpp - LLM inference in C/C++
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
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.
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks