CompressAI-Vision

CompressAI-Vision is a tool that helps you develop, test, and evaluate compression models with standardized tests in the context of compression methods optimized for machine tasks algorithms such as Neural-Network (NN)-based detectors. It currently focuses on two types of pipeline: Video compression for remote inference (`compressai-remote-inference`), which corresponds to the MPEG "Video Coding for Machines" (VCM) activity. Split inference (`compressai-split-inference`), which includes an evaluation framework for compressing intermediate features produced in the context of split models. The software supports all the pipelines considered in the related MPEG activity: "Feature Compression for Machines" (FCM).

Codellm-devkit (CLDK) is a Python library that serves as a multilingual program analysis framework bridging traditional static analysis tools and Large Language Models (LLMs) specialized for code (CodeLLMs). It simplifies the process of analyzing codebases across multiple programming languages, enabling the extraction of meaningful insights and facilitating LLM-based code analysis. The library provides a unified interface for integrating outputs from various analysis tools and preparing them for effective use by CodeLLMs. Codellm-devkit aims to enable the development and experimentation of robust analysis pipelines that combine traditional program analysis tools and CodeLLMs, reducing friction in multi-language code analysis and ensuring compatibility across different tools and LLM platforms. It is designed to seamlessly integrate with popular analysis tools like WALA, Tree-sitter, LLVM, and CodeQL, acting as a crucial intermediary layer for efficient communication between these tools and CodeLLMs. The project is continuously evolving to include new tools and frameworks, maintaining its versatility for code analysis and LLM integration.

Yalm (Yet Another Language Model) is an LLM inference implementation in C++/CUDA, emphasizing performance engineering, documentation, scientific optimizations, and readability. It is not for production use and has been tested on Mistral-v0.2 and Llama-3.2. Requires C++20-compatible compiler, CUDA toolkit, and LLM safetensor weights in huggingface format converted to .yalm file.

CALF (LLaTA) is a cross-modal fine-tuning framework that bridges the distribution discrepancy between temporal data and the textual nature of LLMs. It introduces three cross-modal fine-tuning techniques: Cross-Modal Match Module, Feature Regularization Loss, and Output Consistency Loss. The framework aligns time series and textual inputs, ensures effective weight updates, and maintains consistent semantic context for time series data. CALF provides scripts for long-term and short-term forecasting, requires Python 3.9, and utilizes word token embeddings for model training.

LlamaIndex is a data framework for building LLM applications. It provides tools for ingesting, structuring, and querying data, as well as integrating with LLMs and other tools. LlamaIndex is designed to be easy to use for both beginner and advanced users, and it provides a comprehensive set of features for building LLM applications.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

VMind is an open-source solution for intelligent visualization, providing an intelligent chart component based on LLM by VisActor. It allows users to create chart narrative works with natural language interaction, edit charts through dialogue, and export narratives as videos or GIFs. The tool is easy to use, scalable, supports various chart types, and offers one-click export functionality. Users can customize chart styles, specify themes, and aggregate data using LLM models. VMind aims to enhance efficiency in creating data visualization works through dialogue-based editing and natural language interaction.

Agentok Studio is a tool built upon AG2, a powerful agent framework from Microsoft, offering intuitive visual tools to streamline the creation and management of complex agent-based workflows. It simplifies the process for creators and developers by generating native Python code with minimal dependencies, enabling users to create self-contained code that can be executed anywhere. The tool is currently under development and not recommended for production use, but contributions are welcome from the community to enhance its capabilities and functionalities.

Polymarket Agents is a developer framework and set of utilities for building AI agents to trade autonomously on Polymarket. It integrates with Polymarket API, provides AI agent utilities for prediction markets, supports local and remote RAG, sources data from various services, and offers comprehensive LLM tools for prompt engineering. The architecture features modular components like APIs and scripts for managing local environments, server set-up, and CLI for end-user commands.

OASIS is a scalable, open-source social media simulator that integrates large language models with rule-based agents to realistically mimic the behavior of up to one million users on platforms like Twitter and Reddit. It facilitates the study of complex social phenomena such as information spread, group polarization, and herd behavior, offering a versatile tool for exploring diverse social dynamics and user interactions in digital environments. With features like scalability, dynamic environments, diverse action spaces, and integrated recommendation systems, OASIS provides a comprehensive platform for simulating social media interactions at a large scale.

LABO is a time series forecasting and analysis framework that integrates pre-trained and fine-tuned LLMs with multi-domain agent-based systems. It allows users to create and tune agents easily for various scenarios, such as stock market trend prediction and web public opinion analysis. LABO requires a specific runtime environment setup, including system requirements, Python environment, dependency installations, and configurations. Users can fine-tune their own models using LABO's Low-Rank Adaptation (LoRA) for computational efficiency and continuous model updates. Additionally, LABO provides a Python library for building model training pipelines and customizing agents for specific tasks.

cuVS is a library that contains state-of-the-art implementations of several algorithms for running approximate nearest neighbors and clustering on the GPU. It can be used directly or through the various databases and other libraries that have integrated it. The primary goal of cuVS is to simplify the use of GPUs for vector similarity search and clustering.

LoRAX is a framework that allows users to serve thousands of fine-tuned models on a single GPU, dramatically reducing the cost of serving without compromising on throughput or latency. It features dynamic adapter loading, heterogeneous continuous batching, adapter exchange scheduling, optimized inference, and is ready for production with prebuilt Docker images, Helm charts for Kubernetes, Prometheus metrics, and distributed tracing with Open Telemetry. LoRAX supports a number of Large Language Models as the base model including Llama, Mistral, and Qwen, and any of the linear layers in the model can be adapted via LoRA and loaded in LoRAX.

The DALM (Domain Adapted Language Modeling) toolkit is designed to unify general LLMs with vector stores to ground AI systems in efficient, factual domains. It provides developers with tools to build on top of Arcee's open source Domain Pretrained LLMs, enabling organizations to deeply tailor AI according to their unique intellectual property and worldview. The toolkit contains code for fine-tuning a fully differential Retrieval Augmented Generation (RAG-end2end) architecture, incorporating in-batch negative concept alongside RAG's marginalization for efficiency. It includes training scripts for both retriever and generator models, evaluation scripts, data processing codes, and synthetic data generation code.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

KG-RAG (Knowledge Graph-based Retrieval Augmented Generation) is a task agnostic framework that combines the explicit knowledge of a Knowledge Graph (KG) with the implicit knowledge of a Large Language Model (LLM). KG-RAG extracts "prompt-aware context" from a KG, which is defined as the minimal context sufficient enough to respond to the user prompt. This framework empowers a general-purpose LLM by incorporating an optimized domain-specific 'prompt-aware context' from a biomedical KG. KG-RAG is specifically designed for running prompts related to Diseases.

CompressAI-Vision is a tool that helps you develop, test, and evaluate compression models with standardized tests in the context of compression methods optimized for machine tasks algorithms such as Neural-Network (NN)-based detectors. It currently focuses on two types of pipeline: Video compression for remote inference (`compressai-remote-inference`), which corresponds to the MPEG "Video Coding for Machines" (VCM) activity. Split inference (`compressai-split-inference`), which includes an evaluation framework for compressing intermediate features produced in the context of split models. The software supports all the pipelines considered in the related MPEG activity: "Feature Compression for Machines" (FCM).

TokenPacker is a novel visual projector that compresses visual tokens by 75%∼89% with high efficiency. It adopts a 'coarse-to-fine' scheme to generate condensed visual tokens, achieving comparable or better performance across diverse benchmarks. The tool includes TokenPacker for general use and TokenPacker-HD for high-resolution image understanding. It provides training scripts, checkpoints, and supports various compression ratios and patch numbers.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

PEFT (Parameter-Efficient Fine-Tuning) is a collection of state-of-the-art methods that enable efficient adaptation of large pretrained models to various downstream applications. By only fine-tuning a small number of extra model parameters instead of all the model's parameters, PEFT significantly decreases the computational and storage costs while achieving performance comparable to fully fine-tuned models.

This repo hosts tutorial documentation for running generative AI models on NVIDIA Jetson devices. The documentation is auto-generated and hosted on GitHub Pages using their CI/CD feature to automatically generate/update the HTML documentation site upon new commits.

Emgu CV is a cross-platform .Net wrapper for the OpenCV image-processing library. It allows OpenCV functions to be called from .NET compatible languages. The wrapper can be compiled by Visual Studio, Unity, and "dotnet" command, and it can run on Windows, Mac OS, Linux, iOS, and Android.

MMStar is an elite vision-indispensable multi-modal benchmark comprising 1,500 challenge samples meticulously selected by humans. It addresses two key issues in current LLM evaluation: the unnecessary use of visual content in many samples and the existence of unintentional data leakage in LLM and LVLM training. MMStar evaluates 6 core capabilities across 18 detailed axes, ensuring a balanced distribution of samples across all dimensions.

VLMEvalKit is an open-source evaluation toolkit of large vision-language models (LVLMs). It enables one-command evaluation of LVLMs on various benchmarks, without the heavy workload of data preparation under multiple repositories. In VLMEvalKit, we adopt generation-based evaluation for all LVLMs, and provide the evaluation results obtained with both exact matching and LLM-based answer extraction.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.