LLM-Microscope is a toolkit designed for quantifying and visualizing language model internals. It provides functions for calculating anisotropy, intrinsic dimension, and linearity score. The toolkit also includes a Logit Lens feature for analyzing model predictions and losses. Users can easily install the toolkit using pip and explore the functionalities through provided examples.

RAGoon is a high-level library designed for batched embeddings generation, fast web-based RAG (Retrieval-Augmented Generation) processing, and quantized indexes processing. It provides NLP utilities for multi-model embedding production, high-dimensional vector visualization, and enhancing language model performance through search-based querying, web scraping, and data augmentation techniques.

This repository contains the official implementation of the code for the paper 'Your Transformer is Secretly Linear'. It provides functions for calculating anisotropy score, intrinsic dimension, linearity score, and centered linearity score based on pseudo-random features. Additionally, a pip package is available for easy installation. Users can also download the dataset used in the paper for further analysis.

CAPPr is a tool for text classification that does not require training or post-processing. It allows users to have their language models pick from a list of choices or compute the probability of a completion given a prompt. The tool aims to help users get more out of open source language models by simplifying the text classification process. CAPPr can be used with GGUF models, Hugging Face models, models from the OpenAI API, and for tasks like caching instructions, extracting final answers from step-by-step completions, and running predictions in batches with different sets of completions.

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.

xLSTM is a new Recurrent Neural Network architecture based on ideas of the original LSTM. Through Exponential Gating with appropriate normalization and stabilization techniques and a new Matrix Memory it overcomes the limitations of the original LSTM and shows promising performance on Language Modeling when compared to Transformers or State Space Models. The package is based on PyTorch and was tested for versions >=1.8. For the CUDA version of xLSTM, you need Compute Capability >= 8.0. The xLSTM tool provides two main components: xLSTMBlockStack for non-language applications or integrating in other architectures, and xLSTMLMModel for language modeling or other token-based applications.

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.

Zeta is a tool designed to build state-of-the-art AI models faster by providing modular, high-performance, and scalable building blocks. It addresses the common issues faced while working with neural nets, such as chaotic codebases, lack of modularity, and low performance modules. Zeta emphasizes usability, modularity, and performance, and is currently used in hundreds of models across various GitHub repositories. It enables users to prototype, train, optimize, and deploy the latest SOTA neural nets into production. The tool offers various modules like FlashAttention, SwiGLUStacked, RelativePositionBias, FeedForward, BitLinear, PalmE, Unet, VisionEmbeddings, niva, FusedDenseGELUDense, FusedDropoutLayerNorm, MambaBlock, Film, hyper_optimize, DPO, and ZetaCloud for different tasks in AI model development.

LightRAG is a PyTorch library designed for building and optimizing Retriever-Agent-Generator (RAG) pipelines. It follows principles of simplicity, quality, and optimization, offering developers maximum customizability with minimal abstraction. The library includes components for model interaction, output parsing, and structured data generation. LightRAG facilitates tasks like providing explanations and examples for concepts through a question-answering pipeline.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

Whetstone.ChatGPT is a simple light-weight library that wraps the Open AI API with support for dependency injection. It supports features like GPT 4, GPT 3.5 Turbo, chat completions, audio transcription and translation, vision completions, files, fine tunes, images, embeddings, moderations, and response streaming. The library provides a video walkthrough of a Blazor web app built on it and includes examples such as a command line bot. It offers quickstarts for dependency injection, chat completions, completions, file handling, fine tuning, image generation, and audio transcription.

PyWhy-LLM is an innovative library that integrates Large Language Models (LLMs) into the causal analysis process, empowering users with knowledge previously only available through domain experts. It seamlessly augments existing causal inference processes by suggesting potential confounders, relationships between variables, backdoor sets, front door sets, IV sets, estimands, critiques of DAGs, latent confounders, and negative controls. By leveraging LLMs and formalizing human-LLM collaboration, PyWhy-LLM aims to enhance causal analysis accessibility and insight.

ExtractThinker is a library designed for extracting data from files and documents using Language Model Models (LLMs). It offers ORM-style interaction between files and LLMs, supporting multiple document loaders such as Tesseract OCR, Azure Form Recognizer, AWS TextExtract, and Google Document AI. Users can customize extraction using contract definitions, process documents asynchronously, handle various document formats efficiently, and split and process documents. The project is inspired by the LangChain ecosystem and focuses on Intelligent Document Processing (IDP) using LLMs to achieve high accuracy in document extraction tasks.

Beyond LLM offers an all-in-one toolkit for experimentation, evaluation, and deployment of Retrieval-Augmented Generation (RAG) systems. It simplifies the process with automated integration, customizable evaluation metrics, and support for various Large Language Models (LLMs) tailored to specific needs. The aim is to reduce LLM hallucination risks and enhance reliability.

The GenAI repository contains pipelines that implement image and text generation tasks. The implementation uses OpenVINO capabilities to optimize the pipelines. Each sample covers a family of models and suggests certain modifications to adapt the code to specific needs. It includes the following pipelines: 1. Benchmarking script for large language models 2. Text generation C++ samples that support most popular models like LLaMA 2 3. Stable Diffuison (with LoRA) C++ image generation pipeline 4. Latent Consistency Model (with LoRA) C++ image generation pipeline

Phi-3-MLX is a versatile AI framework that leverages both the Phi-3-Vision multimodal model and the Phi-3-Mini-128K language model optimized for Apple Silicon using the MLX framework. It provides an easy-to-use interface for a wide range of AI tasks, from advanced text generation to visual question answering and code execution. The project features support for batched generation, flexible agent system, custom toolchains, model quantization, LoRA fine-tuning capabilities, and API integration for extended functionality.

ExplainableAI.jl is a Julia package that implements interpretability methods for black-box classifiers, focusing on local explanations and attribution maps in input space. The package requires models to be differentiable with Zygote.jl. It is similar to Captum and Zennit for PyTorch and iNNvestigate for Keras models. Users can analyze and visualize explanations for model predictions, with support for different XAI methods and customization. The package aims to provide transparency and insights into model decision-making processes, making it a valuable tool for understanding and validating machine learning models.

LLM-Microscope is a toolkit designed for quantifying and visualizing language model internals. It provides functions for calculating anisotropy, intrinsic dimension, and linearity score. The toolkit also includes a Logit Lens feature for analyzing model predictions and losses. Users can easily install the toolkit using pip and explore the functionalities through provided examples.

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.

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.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

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.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.