Repository History
42 repositories tagged with machine-learning
SkillOpt: Optimizing Self-Evolving Agent Skills for LLMs
SkillOpt is an innovative text-space optimizer from Microsoft that enables the training of reusable natural-language skills for frozen LLM agents. It approaches skill development with the rigor of deep-learning optimization, using trajectory-driven edits and validation-gated updates. This results in deployable `best_skill.md` artifacts that significantly boost agent performance across various benchmarks and models without modifying model weights.
Awesome AI Agents 2026: The Ultimate List of AI Tools and Frameworks
This repository, `awesome-ai-agents-2026`, is a comprehensive and frequently updated collection of over 340 AI agents, frameworks, and tools across more than 20 categories. It serves as an essential resource for developers and researchers looking to explore the rapidly evolving landscape of artificial intelligence in 2026, covering everything from coding agents to creative AI and governance.
awesome-ai: A Curated List of 400+ AI APIs, Tools, and Frameworks
The awesome-ai repository by edwardtay offers a comprehensive, curated list of over 400 AI APIs, tools, frameworks, and platforms. Spanning more than 40 categories, it serves as an invaluable resource for developers and researchers navigating the vast landscape of artificial intelligence. This list helps users discover solutions for LLMs, agents, image/video generation, MLOps, and more.
AI-Agents-Projects-Tutorials: Comprehensive Guide to AI Agent Development
The AI-Agents-Projects-Tutorials repository offers an extensive collection of code implementations and tutorials for building advanced AI agents. It covers fundamental concepts such as multi-agent systems, memory management, planning, and reasoning loops. This resource is ideal for developers and researchers seeking practical insights into agentic AI development.
AsterMind-ELM: Modular Extreme Learning Machine for On-Device ML in JS/TS
AsterMind-ELM is a JavaScript/TypeScript library that modernizes Extreme Learning Machines (ELMs) for instant, on-device machine learning in web applications. It offers advanced features like Kernel ELMs, Online ELM, and DeepELM, enabling fast, private, and interpretable AI directly in the browser. This framework allows for building decentralized, self-training ML systems without relying on GPUs or servers.
Awesome-pytorch-list: A Comprehensive Collection of PyTorch Resources
The "Awesome-pytorch-list" is an extensive GitHub repository curating a wide range of PyTorch-related content. It serves as a valuable resource for developers and researchers, offering a structured overview of models, implementations, helper libraries, and tutorials. This list simplifies the discovery of essential tools and learning materials within the PyTorch ecosystem.
Axolotl: Streamlining LLM Fine-tuning with a Powerful Open-Source Framework
Axolotl is a comprehensive, free, and open-source framework designed to simplify the post-training and fine-tuning processes for large language models (LLMs). It offers extensive model support, diverse training methods, and robust performance optimizations, making it an invaluable tool for researchers and developers. With easy configuration and cloud-ready deployment, Axolotl empowers users to efficiently customize and enhance LLMs.
torchchat: Run PyTorch LLMs Locally on Servers, Desktop, and Mobile
torchchat is a PyTorch-native codebase designed to showcase the ability to run large language models (LLMs) seamlessly across various platforms. It enables local execution of LLMs using Python, within C/C++ applications on desktop or servers, and directly on iOS and Android devices. Although no longer under active development, it remains a valuable resource for understanding and implementing local LLM deployment strategies.
DeepFabric: High-Quality Synthetic Data for Agentic AI Systems
DeepFabric is an open-source Python library designed to generate high-quality synthetic training data for language models and agent evaluations. It excels at creating domain-specific datasets that teach models to think, plan, and act effectively, including correct tool usage and adherence to schema structures. This comprehensive pipeline also integrates training and evaluation capabilities, ensuring robust model development.
Lighteval: Your All-in-One Toolkit for LLM Evaluation
Lighteval is a comprehensive toolkit from Hugging Face for evaluating Large Language Models (LLMs) across various backends. It enables users to dive deep into model performance by saving detailed, sample-by-sample results and supports over 1000 evaluation tasks. The framework offers extensive customization options, allowing users to create custom tasks and metrics tailored to their specific needs.
Evidently: Open-Source ML and LLM Observability Framework
Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning and large language model systems. It provides over 100 built-in metrics for various tasks, from data drift detection to LLM judges, supporting both tabular and text data. This framework helps ensure the quality and performance of AI-powered systems throughout their lifecycle.
MarkLLM: An Open-Source Toolkit for LLM Watermarking
MarkLLM is an open-source toolkit designed to simplify the research and application of watermarking technologies for large language models (LLMs). It offers a unified framework for implementing various watermarking algorithms, alongside robust visualization and comprehensive evaluation tools. This toolkit helps researchers and the broader community understand and assess the authenticity and origin of machine-generated text.