Repository History
80 repositories tagged with Machine Learning
AuditNLG: Auditing Generative AI for Trustworthiness
AuditNLG is an open-source library from Salesforce designed to enhance the trustworthiness of generative AI language models. It provides state-of-the-art techniques to detect and improve factualness, safety, and constraint adherence in AI-generated text. This library simplifies the process of auditing AI outputs, offering explanations and alternative suggestions for problematic content.
spacy-llm: Integrating LLMs into Structured NLP Pipelines with spaCy
spacy-llm seamlessly integrates Large Language Models (LLMs) into spaCy, offering a modular system for rapid prototyping and transforming unstructured LLM responses into robust outputs for various NLP tasks. It supports a wide range of LLMs, including OpenAI, Cohere, Anthropic, and open-source models, enabling users to combine the power of LLMs with spaCy's production-ready capabilities. This package allows for quick experimentation and the creation of efficient, reliable, and controlled NLP systems.
Dexter: An Autonomous Agent for Deep Financial Research
Dexter is an autonomous financial research agent designed to think, plan, and learn while performing analysis. It leverages task planning, self-reflection, and real-time market data to tackle complex financial questions. This project provides a powerful tool for in-depth financial exploration, emphasizing its educational and informational purposes.
Loop Library: Practical Repeatable AI-Agent Workflows
The Loop Library is a GitHub repository offering reusable AI agent workflows for various domains like engineering, content, and design. It introduces the concept of "loops," which are structured, repeatable instructions that guide AI agents through multi-step tasks. This skill enables agents to learn from results, adapt, and complete complex tasks more reliably than with one-shot prompts.
GLM-5: Flagship Models for Long-Horizon Agentic Engineering
GLM-5 is a series of flagship models, including GLM-5.2, GLM-5.1, and GLM-5, developed by zai-org for complex systems engineering and long-horizon agentic tasks. These models offer advanced coding capabilities, impressive context lengths, and state-of-the-art performance on various benchmarks. They are designed to sustain effective problem-solving over extended sessions through iterative reasoning and strategy revision.
AutoHedge: Build Your Autonomous AI Hedge Fund with Swarm Intelligence
AutoHedge is an enterprise-grade autonomous agent hedge fund that leverages swarm intelligence and specialized AI agents. This powerful Python project automates end-to-end market analysis, risk management, and trade execution. It allows users to build and deploy their own AI-driven trading strategies with minimal human intervention.
Feynman: The Open Source AI Research Agent
Feynman is an open-source AI research agent designed to automate and streamline complex research tasks. Built with TypeScript, it leverages multiple agents and tools to conduct in-depth investigations, literature reviews, and even experiment replications, providing source-grounded outputs.
autoresearch: AI Agents for Autonomous LLM Training Research
autoresearch, by Andrej Karpathy, pioneers autonomous AI research by enabling agents to experiment with LLM training on a single GPU. The system allows an AI agent to modify code, train a model for a fixed 5-minute duration, and iteratively optimize for improved performance. This innovative approach aims to automate the experimental cycle of AI research, fostering continuous discovery and optimization.
AI Engineering from Scratch: A Comprehensive Hands-On AI Curriculum
The "AI Engineering from Scratch" repository provides a free, MIT-licensed curriculum for mastering AI engineering from foundational math to advanced agent systems. It emphasizes a hands-on approach, guiding learners to build every algorithm from scratch before utilizing frameworks. With 435 lessons across 20 phases, this project equips students with the practical skills needed to professionally build and deploy AI solutions.
JARVIS: Connecting LLMs with the ML Community for AGI Exploration
JARVIS is an innovative system developed by Microsoft that aims to bridge Large Language Models (LLMs) with the broader Machine Learning community. It serves as a collaborative platform, using an LLM as a controller to orchestrate numerous expert models from Hugging Face Hub, thereby facilitating the exploration of Artificial General Intelligence (AGI) and solving complex AI tasks. This system streamlines the process of task planning, model selection, execution, and response generation.
fastFM: A High-Performance Python Library for Factorization Machines
fastFM is a powerful Python library designed for Factorization Machines, offering high-performance implementations of various optimization routines. It integrates seamlessly with the scikit-learn API, making it accessible for machine learning practitioners. The library supports regression, classification, and ranking problems, leveraging C and Cython for speed-critical operations.
LLMGym: A Unified Environment for LLM Agent Development and Benchmarking
LLMGym is a unified environment interface designed for developing and benchmarking LLM applications that learn from feedback. It provides a suite of seamlessly swappable environments, making fair and comprehensive comparisons easier for researchers and developers. This project aims to be the "gym" for LLM agents, offering an intuitive interface for various tasks.