Mergekit is a toolkit for merging pre-trained language models. It uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.

Garak is a free tool that checks if a Large Language Model (LLM) can be made to fail in a way that is undesirable. It probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. Garak's a free tool. We love developing it and are always interested in adding functionality to support applications.

TurnkeyML is a tools framework that integrates models, toolchains, and hardware backends to simplify the evaluation and actuation of deep learning models. It supports use cases like exporting ONNX files, performance validation, functional coverage measurement, stress testing, and model insights analysis. The framework consists of analysis, build, runtime, reporting tools, and a models corpus, seamlessly integrated to provide comprehensive functionality with simple commands. Extensible through plugins, it offers support for various export and optimization tools and AI runtimes. The project is actively seeking collaborators and is licensed under Apache 2.0.

The `vectara-agentic` Python library is designed for developing powerful AI assistants using Vectara and Agentic-RAG. It supports various agent types, includes pre-built tools for domains like finance and legal, and enables easy creation of custom AI assistants and agents. The library provides tools for summarizing text, rephrasing text, legal tasks like summarizing legal text and critiquing as a judge, financial tasks like analyzing balance sheets and income statements, and database tools for inspecting and querying databases. It also supports observability via LlamaIndex and Arize Phoenix integration.

MFLUX is a line-by-line port of the FLUX implementation in the Huggingface Diffusers library to Apple MLX. It aims to run powerful FLUX models from Black Forest Labs locally on Mac machines. The codebase is minimal and explicit, prioritizing readability over generality and performance. Models are implemented from scratch in MLX, with tokenizers from the Huggingface Transformers library. Dependencies include Numpy and Pillow for image post-processing. Installation can be done using `uv tool` or classic virtual environment setup. Command-line arguments allow for image generation with specified models, prompts, and optional parameters. Quantization options for speed and memory reduction are available. LoRA adapters can be loaded for fine-tuning image generation. Controlnet support provides more control over image generation with reference images. Current limitations include generating images one by one, lack of support for negative prompts, and some LoRA adapters not working.

Vidur is a high-fidelity and extensible LLM inference simulator designed for capacity planning, deployment configuration optimization, testing new research ideas, and studying system performance of models under different workloads and configurations. It supports various models and devices, offers chrome trace exports, and can be set up using mamba, venv, or conda. Users can run the simulator with various parameters and monitor metrics using wandb. Contributions are welcome, subject to a Contributor License Agreement and adherence to the Microsoft Open Source Code of Conduct.

This prototype synthesizes verified code with an LLM using Monte Carlo Tree Search (MCTS). It explores the space of possible generation of a verified program and checks at every step that it's on the right track by calling the verifier. This prototype uses Dafny, Coq, Lean, Scala, or Rust. By using this technique, weaker models that might not even know the generated language all that well can compete with stronger models.

HuggingFaceGuidedTourForMac is a guided tour on how to install optimized pytorch and optionally Apple's new MLX, JAX, and TensorFlow on Apple Silicon Macs. The repository provides steps to install homebrew, pytorch with MPS support, MLX, JAX, TensorFlow, and Jupyter lab. It also includes instructions on running large language models using HuggingFace transformers. The repository aims to help users set up their Macs for deep learning experiments with optimized performance.

SambaNova AI Starter Kits is a collection of open-source examples and guides designed to facilitate the deployment of AI-driven use cases for developers and enterprises. The kits cover various categories such as Data Ingestion & Preparation, Model Development & Optimization, Intelligent Information Retrieval, and Advanced AI Capabilities. Users can obtain a free API key using SambaNova Cloud or deploy models using SambaStudio. Most examples are written in Python but can be applied to any programming language. The kits provide resources for tasks like text extraction, fine-tuning embeddings, prompt engineering, question-answering, image search, post-call analysis, and more.

Windows Agent Arena (WAA) is a scalable Windows AI agent platform designed for testing and benchmarking multi-modal, desktop AI agents. It provides researchers and developers with a reproducible and realistic Windows OS environment for AI research, enabling testing of agentic AI workflows across various tasks. WAA supports deploying agents at scale using Azure ML cloud infrastructure, allowing parallel running of multiple agents and delivering quick benchmark results for hundreds of tasks in minutes.

code2prompt is a command-line tool that converts your codebase into a single LLM prompt with a source tree, prompt templating, and token counting. It automates generating LLM prompts from codebases of any size, customizing prompt generation with Handlebars templates, respecting .gitignore, filtering and excluding files using glob patterns, displaying token count, including Git diff output, copying prompt to clipboard, saving prompt to an output file, excluding files and folders, adding line numbers to source code blocks, and more. It helps streamline the process of creating LLM prompts for code analysis, generation, and other tasks.

ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.

RAFT (Reusable Accelerated Functions and Tools) is a C++ header-only template library with an optional shared library that contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

PaperQA is a minimal package for question and answering from PDFs or text files, providing very good answers with in-text citations. It uses OpenAI Embeddings to embed and search documents, and includes a process of embedding docs, queries, searching for top passages, creating summaries, using an LLM to re-score and select relevant summaries, putting summaries into prompt, and generating answers. The tool can be used to answer specific questions related to scientific research by leveraging citations and relevant passages from documents.

The `unstructured` library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of `unstructured` revolve around streamlining and optimizing the data processing workflow for LLMs. `unstructured` modular functions and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and efficient in transforming unstructured data into structured outputs.

cassIO is a framework-agnostic Python library that seamlessly integrates Apache Cassandra with ML/LLM/genAI workloads. It provides an easy-to-use interface for developers to connect their Cassandra databases to machine learning models, allowing them to perform complex data analysis and AI-powered tasks directly on their Cassandra data. cassIO is designed to be flexible and extensible, making it suitable for a wide range of use cases, from data exploration and visualization to predictive modeling and natural language processing.

Mergekit is a toolkit for merging pre-trained language models. It uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.

Mergekit is a toolkit for merging pre-trained language models. It uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.