Commit ·
eb16c60
1
Parent(s): 9f15961
Updated contributors* fields
Browse files- PyTorchConference2025_GithubRepos.json +141 -101
PyTorchConference2025_GithubRepos.json
CHANGED
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@@ -6,7 +6,7 @@
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"github_about_section": "The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.",
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"homepage_link": "http://llvm.org",
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"github_topic_closest_fit": "compiler",
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"contributors_all":
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"contributors_2025": 2378,
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"contributors_2024": 2130,
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"contributors_2023": 1920
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"github_about_section": "A high-throughput and memory-efficient inference and serving engine for LLMs",
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"homepage_link": "https://docs.vllm.ai",
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"github_topic_closest_fit": "inference",
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"contributors_all":
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"contributors_2025": 1369,
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"contributors_2024": 579,
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"contributors_2023": 145
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@@ -30,7 +30,7 @@
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"github_about_section": "Tensors and Dynamic neural networks in Python with strong GPU acceleration",
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"homepage_link": "https://pytorch.org",
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"github_topic_closest_fit": "machine-learning",
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"contributors_all":
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"contributors_2025": 1187,
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"contributors_2024": 1090,
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"contributors_2023": 1024
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"github_about_section": "Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.",
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"homepage_link": "https://huggingface.co/transformers",
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"github_topic_closest_fit": "machine-learning",
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"contributors_all":
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"contributors_2025": 860,
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"contributors_2024": 769,
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"contributors_2023": 758
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"github_about_section": "SGLang is a fast serving framework for large language models and vision language models.",
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"homepage_link": "https://docs.sglang.ai",
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"github_topic_closest_fit": "inference",
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"contributors_all":
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"contributors_2025": 796,
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"contributors_2024": 189,
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"contributors_2023": 1
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"github_about_section": "A virtual machine for executing programs written in Hack.",
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"homepage_link": "https://hhvm.com",
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"github_topic_closest_fit": "virtual-machine",
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"contributors_all":
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"contributors_2025": 692,
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"contributors_2024": 648,
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"contributors_2023": 604
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"github_about_section": "LLM inference in C/C++",
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"homepage_link": "https://ggml.ai",
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"github_topic_closest_fit": "inference",
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"contributors_all":
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"contributors_2025": 535,
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"github_about_section": "Production-Grade Container Scheduling and Management",
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"homepage_link": "https://kubernetes.io",
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"github_topic_closest_fit": "kubernetes",
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"contributors_all":
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"contributors_2025":
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"contributors_2024":
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"contributors_2023": 565
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},
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{
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"github_about_section": "An Open Source Machine Learning Framework for Everyone",
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"homepage_link": "https://tensorflow.org",
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"github_topic_closest_fit": "machine-learning",
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"contributors_all":
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"contributors_2025":
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"contributors_2024": 523,
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"contributors_2023": 630
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},
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"github_about_section": "verl: Volcano Engine Reinforcement Learning for LLMs",
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"homepage_link": "https://verl.readthedocs.io",
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"github_topic_closest_fit": "deep-reinforcement-learning",
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"contributors_all":
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"contributors_2025": 454,
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"contributors_2024": 10,
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"contributors_2023": 0
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"github_about_section": "super repo for rocm systems projects",
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"homepage_link": "https://amd.com/en/products/software/rocm.html",
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"github_topic_closest_fit": "amd",
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"contributors_all":
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"contributors_2025":
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"contributors_2024":
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"contributors_2023":
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},
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{
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"repo_name": "ray",
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"github_about_section": "Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.",
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"homepage_link": "https://ray.io",
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"github_topic_closest_fit": "machine-learning",
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"contributors_all":
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"contributors_2025": 397,
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"contributors_2024": 223,
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"contributors_2023": 230
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"github_about_section": "Apache Spark - A unified analytics engine for large-scale data processing",
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"homepage_link": "https://spark.apache.org",
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"github_topic_closest_fit": "data-processing",
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"contributors_all":
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"contributors_2025": 322,
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"contributors_2023": 336
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"github_about_section": "an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM",
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"homepage_link": "https://block.github.io/goose",
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"github_topic_closest_fit": "ai-agents",
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"contributors_all":
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"contributors_2025": 319,
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"contributors_2024": 32,
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"contributors_2023": 0
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"github_about_section": "Free and Open Source, Distributed, RESTful Search Engine",
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"homepage_link": "https://elastic.co/products/elasticsearch",
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"github_topic_closest_fit": "search-engine",
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"contributors_all":
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"contributors_2025": 316,
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"contributors_2024": 284,
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"contributors_2023": 270
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"github_about_section": "Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more",
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"homepage_link": "https://docs.jax.dev",
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"github_topic_closest_fit": "scientific-computing",
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"contributors_all":
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"contributors_2025":
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"contributors_2024": 280,
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"contributors_2023": 202
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},
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"github_about_section": "Specification and documentation for the Model Context Protocol",
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"homepage_link": "https://modelcontextprotocol.io",
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"github_topic_closest_fit": "mcp",
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"contributors_all":
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"contributors_2025":
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},
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"github_about_section": "On-device AI across mobile, embedded and edge for PyTorch",
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"homepage_link": "https://executorch.ai",
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"github_topic_closest_fit": "inference",
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"contributors_2025": 267,
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"contributors_2023": 77
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"github_about_section": "The fundamental package for scientific computing with Python.",
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"homepage_link": "https://numpy.org",
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"github_topic_closest_fit": "scientific-computing",
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"contributors_all":
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},
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"github_about_section": "Development repository for the Triton language and compiler",
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"homepage_link": "https://triton-lang.org",
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"github_topic_closest_fit": "parallel-programming",
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"contributors_2023": 159
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"github_about_section": "The Modular Platform (includes MAX & Mojo)",
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"homepage_link": "https://docs.modular.com",
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"github_topic_closest_fit": "parallel-programming",
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"contributors_all":
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"contributors_2025": 222,
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"github_about_section": "SciPy library main repository",
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"homepage_link": "https://scipy.org",
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"github_topic_closest_fit": "scientific-computing",
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"contributors_all":
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"contributors_2025":
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},
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"github_about_section": "Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.",
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"homepage_link": "https://ollama.com",
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"github_topic_closest_fit": "inference",
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"contributors_2025": 202,
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"contributors_2023": 97
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"github_about_section": "Train transformer language models with reinforcement learning.",
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"homepage_link": "http://hf.co/docs/trl",
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"github_topic_closest_fit": "reinforcement-learning",
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"contributors_2025": 189,
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"github_about_section": "FlashInfer: Kernel Library for LLM Serving",
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"homepage_link": "https://flashinfer.ai",
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"github_topic_closest_fit": "attention",
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"contributors_all":
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"category": "gpu kernels",
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"github_about_section": "AI Tensor Engine for ROCm",
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"homepage_link": "https://rocm.blogs.amd.com/software-tools-optimization/aiter-ai-tensor-engine/README.html",
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"contributors_all":
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"contributors_2025": 145,
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"category": "inference",
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"github_about_section": "Supercharge Your LLM with the Fastest KV Cache Layer",
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"homepage_link": "https://lmcache.ai",
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"contributors_all":
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"github_about_section": "Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI.",
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"homepage_link": "https://kvcache-ai.github.io/Mooncake",
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"github_topic_closest_fit": "inference",
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"contributors_all":
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"category": "training framework",
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"github_about_section": "A PyTorch native platform for training generative AI models",
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"homepage_link": "https://arxiv.org/abs/2410.06511",
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"contributors_all":
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"contributors_2025": 119,
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"github_about_section": "PyTorch native quantization and sparsity for training and inference",
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"homepage_link": "https://pytorch.org/ao",
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"github_topic_closest_fit": "quantization",
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"contributors_all":
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"github_about_section": "The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.",
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"homepage_link": "https://comfy.org",
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"github_topic_closest_fit": "stable-diffusion",
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"github_about_section": "Fine-tuning & Reinforcement Learning for LLMs. Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.",
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"homepage_link": "https://docs.unsloth.ai",
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"github_topic_closest_fit": "fine-tuning",
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"contributors_all":
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{
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"category": "training framework",
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"github_about_section": "A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support.",
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"homepage_link": "https://huggingface.co/docs/accelerate",
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"category": "training framework",
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"github_about_section": "DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.",
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"homepage_link": "https://deepspeed.ai",
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"github_about_section": "Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search",
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"homepage_link": "https://milvus.io",
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"github_topic_closest_fit": "vector-search",
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"contributors_all":
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"contributors_2025": 95,
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"github_about_section": "CUDA Templates and Python DSLs for High-Performance Linear Algebra",
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"homepage_link": "https://docs.nvidia.com/cutlass/index.html",
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"github_topic_closest_fit": "parallel-programming",
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"github_about_section": "Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels",
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"homepage_link": "https://tilelang.com",
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"github_topic_closest_fit": "parallel-programming",
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"category": "distributed computing",
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"github_about_section": "PyTorch Single Controller",
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"homepage_link": "https://meta-pytorch.org/monarch",
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"github_about_section": "Efficient Triton Kernels for LLM Training",
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"homepage_link": "https://openreview.net/pdf?id=36SjAIT42G",
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"github_topic_closest_fit": "triton",
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"category": "fine tuning",
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"github_about_section": "PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.",
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"homepage_link": "https://huggingface.co/docs/peft",
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"category": "multi-purpose library",
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"github_about_section": "AMD ROCm Software - GitHub Home",
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"homepage_link": "https://rocm.docs.amd.com",
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"category": "mcp",
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"github_about_section": "Build effective agents using Model Context Protocol and simple workflow patterns",
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"github_topic_closest_fit": "mcp",
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"repo_link": "https://github.com/linux-rdma/rdma-core",
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"category": "systems level code",
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"github_about_section": "RDMA core userspace libraries and daemons",
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"github_about_section": "Open standard for machine learning interoperability",
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"homepage_link": "https://onnx.ai",
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"github_topic_closest_fit": "onnx",
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"github_about_section": "Letta is the platform for building stateful agents: open AI with advanced memory that can learn and self-improve over time.",
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"homepage_link": "https://docs.letta.com",
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"github_topic_closest_fit": "ai-agents",
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"github_about_section": "A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.",
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"homepage_link": "https://helionlang.com",
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"github_topic_closest_fit": "parallel-programming",
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"category": "evolutionary algorithm",
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"github_about_section": "Open-source implementation of AlphaEvolve",
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"repo_link": "https://github.com/Lightning-AI/lightning-thunder",
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"category": "model compiler",
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"github_about_section": "PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own.",
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"github_about_section": "The simplest way to serve AI/ML models in production",
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"homepage_link": "https://truss.baseten.co",
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"github_topic_closest_fit": "inference",
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"github_about_section": "CUDA Python: Performance meets Productivity",
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"homepage_link": "https://nvidia.github.io/cuda-python",
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"github_about_section": "A Python framework for accelerated simulation, data generation and spatial computing.",
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"homepage_link": "https://nvidia.github.io/warp",
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"github_topic_closest_fit": "physics-simulation",
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"category": "container orchestration",
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"github_about_section": "Build, Manage and Deploy AI/ML Systems",
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"homepage_link": "https://metaflow.org",
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"category": "compiler",
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"github_about_section": "NumPy aware dynamic Python compiler using LLVM",
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"homepage_link": "https://numba.pydata.org",
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"category": "distributed computing",
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"github_about_section": "Distributed Compiler based on Triton for Parallel Systems",
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"github_about_section": "Tile primitives for speedy kernels",
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"homepage_link": "https://hazyresearch.stanford.edu/blog/2024-10-29-tk2",
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"github_topic_closest_fit": "parallel-programming",
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"github_about_section": "OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)",
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"homepage_link": "http://docs.sglang.ai/ome",
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"github_topic_closest_fit": "k8s",
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"github_about_section": "The Triton Inference Server provides an optimized cloud and edge inferencing solution.",
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"homepage_link": "https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html",
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"github_topic_closest_fit": "inference",
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"category": "compiler",
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"github_about_section": "ccache - a fast compiler cache",
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"homepage_link": "https://ccache.dev",
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"github_about_section": "LAPACK is a library of Fortran subroutines for solving the most commonly occurring problems in numerical linear algebra.",
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"homepage_link": "https://netlib.org/lapack",
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"github_topic_closest_fit": "linear-algebra",
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{
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"repo_link": "https://github.com/Dao-AILab/quack",
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"category": "kernel examples",
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"github_about_section": "A Quirky Assortment of CuTe Kernels",
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"github_about_section": "KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems",
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"github_topic_closest_fit": "benchmark",
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"category": "kernel examples",
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"github_about_section": "Official Problem Sets / Reference Kernels for the GPU MODE Leaderboard!",
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"category": "performance testing",
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"github_about_section": "TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels",
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"repo_link": "https://github.com/huggingface/kernels",
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"category": "gpu kernels",
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"github_about_section": "Load compute kernels from the Hub",
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"github_about_section": "Wan: Open and Advanced Large-Scale Video Generative Models",
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"homepage_link": "https://wan.video",
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"category": "training framework",
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"github_about_section": "Primus-Turbo is a high-performance acceleration library dedicated to large-scale model training on AMD GPUs. Built and optimized for the AMD ROCm platform, it covers the full training stack — including core compute operators (GEMM, Attention, GroupedGEMM), communication primitives, optimizer modules, low-precision computation (FP8), and compute–communication overlap kernels.",
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"github_about_section": "Building the Virtuous Cycle for AI-driven LLM Systems",
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"category": "gpu kernels",
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"homepage_link": "https://huggingface.co/kernels-community",
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"category": "agent",
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"github_about_section": "Official inference library for Mistral models",
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"category": "debugger",
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"repo_link": "https://github.com/wandb/wandb",
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"category": "ml visualization",
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"category": "sdk",
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