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Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.
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Published directory entries matching your search.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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Open platform for operating large language models (LLMs) in production. Fine-tune, serve, deploy, and monitor any LLMs with ease.
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A PyTorch-based framework to train and validate the models producing high-quality embeddings.
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A real-time multi-person keypoint detection library for body, face, hands, and foot estimation.
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On-device voice dictation for macOS — transcribes a 5-minute clip in 2.8 s; noise-robust, paste at cursor. 99 languages, ~8 MB, no telemetry. MIT.
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Gitingest - Turn any Git repository into a simple text digest of its codebase so it can be fed into any LLM.
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Jan - Run LLMs like Mistral or Llama2 locally and offline on your computer, or connect to remote AI APIs.
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An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more.
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Google TPU optimizations for transformers models.
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Python-based meta-heuristic optimization techniques.
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Resource scheduling and cluster management for AI.
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Swift Language Bindings of TensorFlow. Using native TensorFlow models on both macOS / Linux.
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An app and guide to easily configure Windows, Linux, MacOS, Google TV, Stremio, Home Assistant and more (including WSL2, GPU drivers & development tools). Improve your UX & productivity.
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A Julia framework for probabilistic graphical models.
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Python library for working with Probabilistic Graphical Models.
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Retrieval Augmented Generation (RAG) framework and context engine powered by Pinecone.
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A better version of Jieba, developed by Peking University.
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Production-Ready LLM Agent SDK for Every Developer.
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Use AutoML to do model compression.
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A macOS app for browsing Claude Code sessions, exploring diffs, and re-running commands. Reads local transcripts, runs offline, open source.
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Multilingual text (NLP) processing toolkit.
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A high-speed inference engine for deploying LLMs locally.
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Ask questions to your documents without an internet connection, using the power of LLMs.