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Latitude is an open-source observability tool for AI agents. It helps identify failures, dispatch coding agents to fix issues, and verify fixes using real traces. The platform is available on GitHub.
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layer is a command-line tool for neural network inference, following the Unix philosophy. It enables users to perform inference from the command line. The project is open-source and hosted on GitHub.
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libonnx is a lightweight, portable pure C99 ONNX inference engine for embedded devices with hardware acceleration support. Its .c and .h files can be dropped into a project and compiled with it. Models are loaded from a file into a context, and inference runs on input tensors via onnx_run. It is based on ONNX version v1.17.0 with opset 24 support, and some operators are not implemented.
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LLM App is an open-source project providing ready-to-run cloud templates for retrieval-augmented generation (RAG), AI pipelines and enterprise search with live data. It is Docker-friendly and designed to stay synchronized with data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL and real-time data APIs, hosted by pathwaycom on GitHub.
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LLMKube is a Kubernetes operator for self-hosted large language model inference across heterogeneous GPU fleets supporting NVIDIA CUDA, AMD Vulkan, and Apple Silicon Metal. It supports runtimes including llama.cpp, vLLM, TGI, and mlx-server, offers multi-GPU sharding, model caching, and OpenAI-compatible endpoints. It is licensed under Apache-2.0 and designed for homelab and on-prem fleet deployment, and is actively developed.
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Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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A platform for deploying and serving machine learning models.
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MindSpore is an open source deep learning training and inference framework that could be used for mobile, edge, and cloud computing scenarios. Its source code is hosted in the mindspore-ai/mindspore repository on GitHub.
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Mirascope is a project labeled the LLM Anti-Framework that provides one unified interface for using frontier LLMs. Its examples show calling LLMs with a decorator, getting structured output via Pydantic models, and building an agent with tools. It is organized as a monorepo with Python and TypeScript implementations plus documentation.
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MLEM is an open source tool designed to package, serve, and deploy machine learning models on any platform. The project's primary repository is currently archived, with maintainers noting plans to resurrect it in the future.
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MLReef is an open source ML-Ops platform for collaborating, reproducing and sharing machine learning work. The repository notes it is no longer supported or updated there, with work continuing on GitLab. Described sections include data management with versioned data hosting, containerized code repositories, experiment tracking and ML pipelines on Kubernetes, cloud or bare metal.
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MLRun is an open source MLOps platform for building and managing continuous machine learning applications across their lifecycle. It integrates with development and CI/CD environments to automate the delivery of production data, ML pipelines, and online applications.
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Open-source project that supports deploying, serving, monitoring, or operating AI and machine-learning systems.
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Modelz LLM is an inference server for running open source large language models such as FastChat, LLaMA and ChatGLM on local or cloud environments with an OpenAI compatible API. It supports interaction through the OpenAI python SDK or LangChain and provides docker images for deployment on Kubernetes or Modelz.
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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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NASGym is a proof-of-concept OpenAI Gym environment for Neural Architecture Search. It implements reinforcement learning steps for NAS and encodes networks using the Neural Structure Code from BlockQNN. A network is modeled as a list of NSCs, an action adds a layer, and the reward is accuracy after early-stop training. CIFAR-10 is the default dataset. The code and documentation may contain minor bugs.
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NCNN is a neural network inference framework optimized for mobile, embedded, and desktop deployment. It has no third-party runtime dependencies and runs across CPU and Vulkan GPU backends. Tools such as pnnx convert PyTorch and ONNX models to ncnn, allowing deployment to phones, PCs, browsers, and edge devices. The project is used in several Tencent applications.
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NDScala is a Scala 3 library for N-dimensional and multi-dimensional arrays (tensors), described as comparable to NumPy ndarray or PyTorch Tensor, with type safety over shapes, array and axis labels and numeric data types. The repository includes materials on shape-safe neural network training and an ONNXScala component.
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Easy-to-use library to boost AI inference.
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An Easy-to-Use and High-Performance AI deployment framework.
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Neural networks framework in pure C: training and inference, no dependencies.
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Serve Llama 2 and other large language models locally from command line or through a browser interface.
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Ombre is an open-source AI infrastructure layer. It runs eight specialized agents on every request to handle security, caching, memory, hallucination detection, and tamper-proof audit trails. It is installed with one line of code, and user data never leaves the user's server.
github.com
OneComp is a Python package for large language model (LLM) compression, hosted as a public repository under the FujitsuResearch GitHub organization. The project is designed for LLM compression tasks and accepts community contributions to its codebase.