StarWhisper is a multi-modal model repository developed under the support of the National Astronomical Observatory-Zhijiang Laboratory. It includes language models, temporal models, and multi-modal models ranging from 7B to 72B. The repository provides pre-trained models and technical reports for tasks such as pulsar identification, light curve classification, and telescope control. It aims to integrate astronomical knowledge using large models and explore the possibilities of solving specific astronomical problems through multi-modal approaches.

Janus is a series of unified multimodal understanding and generation models, including Janus-Pro, Janus, and JanusFlow. Janus-Pro is an advanced version that improves both multimodal understanding and visual generation significantly. Janus decouples visual encoding for unified multimodal understanding and generation, surpassing previous models. JanusFlow harmonizes autoregression and rectified flow for unified multimodal understanding and generation, achieving comparable or superior performance to specialized models. The models are available for download and usage, supporting a broad range of research in academic and commercial communities.

Unitxt is a customizable library for textual data preparation and evaluation tailored to generative language models. It natively integrates with common libraries like HuggingFace and LM-eval-harness and deconstructs processing flows into modular components, enabling easy customization and sharing between practitioners. These components encompass model-specific formats, task prompts, and many other comprehensive dataset processing definitions. The Unitxt-Catalog centralizes these components, fostering collaboration and exploration in modern textual data workflows. Beyond being a tool, Unitxt is a community-driven platform, empowering users to build, share, and advance their pipelines collaboratively.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

Dynamiq is an orchestration framework designed to streamline the development of AI-powered applications, specializing in orchestrating retrieval-augmented generation (RAG) and large language model (LLM) agents. It provides an all-in-one Gen AI framework for agentic AI and LLM applications, offering tools for multi-agent orchestration, document indexing, and retrieval flows. With Dynamiq, users can easily build and deploy AI solutions for various tasks.

Remote-MCP is a type-safe, bidirectional, and simple solution for remote MCP communication, enabling remote access and centralized management of model contexts. It provides a bridge for immediate remote access to a remote MCP server from a local MCP client, without waiting for future official implementations. The repository contains client and server libraries for creating and connecting to remotely accessible MCP services. The core features include basic type-safe client/server communication, MCP command/tool/prompt support, custom headers, and ongoing work on crash-safe handling and event subscription system.

CopilotKit is an open-source framework for building, deploying, and operating fully custom AI Copilots, including in-app AI chatbots, AI agents, and AI Textareas. It provides a set of components and entry points that allow developers to easily integrate AI capabilities into their applications. CopilotKit is designed to be flexible and extensible, so developers can tailor it to their specific needs. It supports a variety of use cases, including providing app-aware AI chatbots that can interact with the application state and take action, drop-in replacements for textareas with AI-assisted text generation, and in-app agents that can access real-time application context and take action within the application.

VinAI_Translate is a Vietnamese-English Neural Machine Translation System offering state-of-the-art text-to-text translation models for Vietnamese-to-English and English-to-Vietnamese. The system includes pre-trained models with different configurations and parameters, allowing for further fine-tuning. Users can interact with the models through the VinAI Translate system website or the HuggingFace space 'VinAI Translate'. Evaluation scripts are available for assessing the translation quality. The tool can be used in the 'transformers' library for Vietnamese-to-English and English-to-Vietnamese translations, supporting both GPU-based batch translation and CPU-based sequence translation examples.

Ollama SDK for .NET is a fully generated C# SDK based on OpenAPI specification using OpenApiGenerator. It supports automatic releases of new preview versions, source generator for defining tools natively through C# interfaces, and all modern .NET features. The SDK provides support for all Ollama API endpoints including chats, embeddings, listing models, pulling and creating new models, and more. It also offers tools for interacting with weather data and providing weather-related information to users.

Torch-Pruning (TP) is a library for structural pruning that enables pruning for a wide range of deep neural networks. It uses an algorithm called DepGraph to physically remove parameters. The library supports pruning off-the-shelf models from various frameworks and provides benchmarks for reproducing results. It offers high-level pruners, dependency graph for automatic pruning, low-level pruning functions, and supports various importance criteria and modules. Torch-Pruning is compatible with both PyTorch 1.x and 2.x versions.

LLMRec is a PyTorch implementation for the WSDM 2024 paper 'Large Language Models with Graph Augmentation for Recommendation'. It is a novel framework that enhances recommenders by applying LLM-based graph augmentation strategies to recommendation systems. The tool aims to make the most of content within online platforms to augment interaction graphs by reinforcing u-i interactive edges, enhancing item node attributes, and conducting user node profiling from a natural language perspective.

Cactus is an energy-efficient and fast AI inference framework designed for phones, wearables, and resource-constrained arm-based devices. It provides a bottom-up approach with no dependencies, optimizing for budget and mid-range phones. The framework includes Cactus FFI for integration, Cactus Engine for high-level transformer inference, Cactus Graph for unified computation graph, and Cactus Kernels for low-level ARM-specific operations. It is suitable for implementing custom models and scientific computing on mobile devices.

MatMul-Free LM is a language model architecture that eliminates the need for Matrix Multiplication (MatMul) operations. This repository provides an implementation of MatMul-Free LM that is compatible with the 🤗 Transformers library. It evaluates how the scaling law fits to different parameter models and compares the efficiency of the architecture in leveraging additional compute to improve performance. The repo includes pre-trained models, model implementations compatible with 🤗 Transformers library, and generation examples for text using the 🤗 text generation APIs.

orch is a library for building language model powered applications and agents for the Rust programming language. It can be used for tasks such as text generation, streaming text generation, structured data generation, and embedding generation. The library provides functionalities for executing various language model tasks and can be integrated into different applications and contexts. It offers flexibility for developers to create language model-powered features and applications in Rust.

VILA is a family of open Vision Language Models optimized for efficient video understanding and multi-image understanding. It includes models like NVILA, LongVILA, VILA-M3, VILA-U, and VILA-1.5, each offering specific features and capabilities. The project focuses on efficiency, accuracy, and performance in various tasks related to video, image, and language understanding and generation. VILA models are designed to be deployable on diverse NVIDIA GPUs and support long-context video understanding, medical applications, and multi-modal design.

The KAN-GPT repository is a PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling. It provides a model for generating text based on prompts, with a focus on improving performance compared to traditional MLP-GPT models. The repository includes scripts for training the model, downloading datasets, and evaluating model performance. Development tasks include integrating with other libraries, testing, and documentation.

StarWhisper is a multi-modal model repository developed under the support of the National Astronomical Observatory-Zhijiang Laboratory. It includes language models, temporal models, and multi-modal models ranging from 7B to 72B. The repository provides pre-trained models and technical reports for tasks such as pulsar identification, light curve classification, and telescope control. It aims to integrate astronomical knowledge using large models and explore the possibilities of solving specific astronomical problems through multi-modal approaches.

StarWhisper is a multi-modal model repository developed under the support of the National Astronomical Observatory-Zhijiang Laboratory. It includes language models, temporal models, and multi-modal models ranging from 7B to 72B. The repository provides pre-trained models and technical reports for tasks such as pulsar identification, light curve classification, and telescope control. It aims to integrate astronomical knowledge using large models and explore the possibilities of solving specific astronomical problems through multi-modal approaches.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.