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Flexible, high-performance serving system for machine learning models.
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Uniform deep learning inference framework for mobile, desktop and server.
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Minimize LLM token complexity to save API costs and model computations.
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Build and control your personal LLMs with fast and efficient fine-tuning.
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Open-source platform for high-performance ML model serving.
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Go binding for MXNet c predict api to do inference with a pre-trained model.
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Build multimodal AI services via cloud native technologies · Model Serving · Generative AI · Neural Search · Cloud Native.
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Open source MLOps project that eases model handoffs between data scientist and DevOps.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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Machine learning model serving framework with dynamic batching and pipelined stages, provides an easy-to-use Python interface.
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Python-free Rust inference server with OpenAI API compatibility and hot model swapping.
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Practical principles for building controllable LLM applications around deterministic software.
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MCP server for Todoist integration enabling natural language task management with Claude.
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A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision.
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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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A comprehensive set of fairness metrics for datasets and machine learning models.
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Easy way to turn any app into searchable data for LLMs.
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An open source Python library focused on outlier, adversarial and drift detection.
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Code and documentation to train Stanford's Alpaca models, and generate the data.
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Instruct-tune LLaMA on consumer hardware.
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Ambrosia helps you clean up your LLM datasets using other LLMs.
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Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler.