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The memory layer for Personalized AI.
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Turn entire websites into LLM-ready markdown or structured data. Scrape, crawl and extract with a single API.
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A platform for deploying and serving machine learning models.
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Flexible Deep Learning Framework in Julia.
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Official repository to use and implement LLaMA 3.1 models, Meta's state-of-the-art large language models.
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An open source robotics benchmark for meta- and multi-task reinforcement learning.
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A distributed machine learning framework Apache Spark.
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JARVIS, a system to connect LLMs with ML community.
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General technology for enabling AI capabilities w/ LLMs and MLLMs.
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Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"s.
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Integrate LLM technology quickly and easily into your apps.
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LLM-powered multiagent persona simulation for imagination enhancement and business insights.
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MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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An asynchronous engine for continuous & autonomous machine learning, built for real-time usage.
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Intuitive convenience tooling for lightning-fast, efficient development and ensuring quality in LLM-based applications.
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C, C++, and Python tools for named entity recognition and relation extraction.
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A Julia package for fitting (statistical) mixed-effects models.
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All-in-one web-based IDE specialized for machine learning and data science.
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Machine learning and numerical analysis tools for Node.js and the Browser!
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500+ ML/AI interview Q&A with runnable code — covers ML fundamentals, deep learning, NLP, PyTorch, scikit-learn pipelines, and system design.
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Friendly machine learning for the web!
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A set of functions to support the development of machine learning algorithms.
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A Julia machine learning framework.
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A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.