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LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models (ECCV 2024)
A project page template for academic papers. Demo at https://eliahuhorwitz.github.io/Academic-project-page-template/
Jekyll website template for personal academic or research group web pages.
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Simple project webpage template. Originally used in Colorful Image Colorization. ECCV, 2016.
Official inference repo for FLUX.1 models
【EMNLP 2024🔥】Video-LLaVA: Learning United Visual Representation by Alignment Before Projection
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Kolmogorov-Arnold Transformer: A PyTorch Implementation with CUDA kernel
A collection of LLM papers, blogs, and projects, with a focus on OpenAI o1 and reasoning techniques.
[EMNLP 2023 Demo] Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
VisionLLaMA: A Unified LLaMA Backbone for Vision Tasks
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
Official repository for "Hardware Resilience Properties of Text-Guided Image Classifiers" [NeurIPS 2023]
Best Practices, code samples, and documentation for Computer Vision.
llama3 implementation one matrix multiplication at a time
Scaling Data-Constrained Language Models
CoreNet: A library for training deep neural networks
Open weights language model from Google DeepMind, based on Griffin.
Package for extracting and mapping the results of every single tensor operation in a PyTorch model in one line of code.
PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)
Examples and guides for using the OpenAI API
An open-source academic paper management tool.
[ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support