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Testing infrastructure for LLM and agentic applications with collaborative evaluation.
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Text processing tools and wrappers (e.g. Vowpal Wabbit).
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Fast MATLAB-syntax runtime with automatic CPU/GPU execution and fused array kernels.
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Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...).
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A tool to help you configure, organize, log and reproduce experiments.
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A CLI utility to train and deploy ML/DL models on AWS SageMaker.
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High-performance, high-precision CPU, GPU, and memory profiler for Python.
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Chrome extension that uses local LLMs to assist with writing and drafting responses based on the context of your open tabs.
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MLOps framework to package, deploy, monitor and manage thousands of production machine learning models.
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A C++ library for unsupervised text tokenization and detokenization, widely used in modern NLP models.
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Natural Language Understanding library for intent classification and entity extraction.
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AI-generated visualization prototyping and editing platform, support 2D, 3D models, combined with LLM(Large Language Model) for quick editing.
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A full port of Stanford NLP packages to.NET and also available precompiled as a NuGet package.
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Hyperparameter Optimization for TensorFlow, Keras and PyTorch.
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TF-GAN is a lightweight library for training and evaluating Generative Adversarial Networks (GANs).
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Agent techniques to augment your LLM and push it beyond its limits.
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Text Retrieval and Annotation Toolkit, definitely the most comprehensive toolkit I’ve encountered so far for Ruby.
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Any URL to clean markdown for LLMs. Free API, no signup required. Strips JS/CSS and outputs LLM-ready content.
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Minimize LLM token complexity to save API costs and model computations.
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Some of the python libraries were cut-and-pasted from.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library.
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Ultralytics' YOLOv8 implementation with C++ support for real-time object detection and tracking, optimized for edge devices.
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Practical principles for building controllable LLM applications around deterministic software.