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Kubernetes custom resource definition for serving ML models on arbitrary frameworks.
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A generic approach that allows to mimic most factorization models by feature engineering.
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Model viewer for inspecting neural network architecture files from many machine learning frameworks.
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Reference implementations of ML models written in numpy.
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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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Hidden Markov Models for Python, implemented in Cython for speed and efficiency.
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Peer-to-peer network of data owners and data scientists who can collectively train AI models using PySyft.
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Source 2 Viewer is an all-in-one tool to browse VPK archives, view, extract, and decompile Source 2 assets, including maps, models, materials, textures, sounds, and more.
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Light-weight, universal resource scheduler for container orchestrator systems.
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Practical principles for building controllable LLM applications around deterministic software.
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A machine learning / bayesian inference assigning attributes to objects.
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MCP server for Todoist integration enabling natural language task management with Claude.
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Open-source implementation of Google Vizier for hyper parameters tuning.
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A framework for performing reproducible AI and ML for Weights and Biases.
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An Artificial Intelligence Automation Platform.
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Open-source project that applies AI to data analysis, extraction, visualization, or business intelligence.
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Open Diffusion Models for High-Quality Video Generation.
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Easy way to turn any app into searchable data for LLMs.
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Open-source Python library enabling ML model inspection and interpretation.
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An open source Python library focused on outlier, adversarial and drift detection.
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Instruct-tune LLaMA on consumer hardware.
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Creates or edits images with generative models and visual controls.
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Ambrosia helps you clean up your LLM datasets using other LLMs.