Decreased number of fields.

PyTorchConference2025_GithubRepos.json

@@ -4,1543 +4,849 @@
     "repo_link": "https://github.com/llvm/llvm-project",
     "category": "compiler",
     "github_about_section": "The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.",
-    "homepage_link": "http://llvm.org",
-    "github_topic_closest_fit": "compiler",
-    "contributors_all": 7219,
-    "contributors_2025": 2378,
-    "contributors_2024": 2130,
-    "contributors_2023": 1920,
-    "contributors_2026_q1": 1364
+    "homepage_link": "http://llvm.org"
   },
   {
     "repo_name": "pytorch",
     "repo_link": "https://github.com/pytorch/pytorch",
     "category": "machine learning framework",
     "github_about_section": "Tensors and Dynamic neural networks in Python with strong GPU acceleration",
-    "homepage_link": "https://pytorch.org",
-    "github_topic_closest_fit": "machine-learning",
-    "contributors_all": 5824,
-    "contributors_2025": 1187,
-    "contributors_2024": 1090,
-    "contributors_2023": 1024,
-    "contributors_2026_q1": 560
+    "homepage_link": "https://pytorch.org"
   },
   {
     "repo_name": "kubernetes",
     "repo_link": "https://github.com/kubernetes/kubernetes",
     "category": "container orchestration",
     "github_about_section": "Production-Grade Container Scheduling and Management",
-    "homepage_link": "https://kubernetes.io",
-    "github_topic_closest_fit": "kubernetes",
-    "contributors_all": 5218,
-    "contributors_2025": 544,
-    "contributors_2024": 499,
-    "contributors_2023": 565,
-    "contributors_2026_q1": 258
+    "homepage_link": "https://kubernetes.io"
   },
   {
     "repo_name": "tensorflow",
     "repo_link": "https://github.com/tensorflow/tensorflow",
     "category": "machine learning framework",
     "github_about_section": "An Open Source Machine Learning Framework for Everyone",
-    "homepage_link": "https://tensorflow.org",
-    "github_topic_closest_fit": "machine-learning",
-    "contributors_all": 4705,
-    "contributors_2025": 506,
-    "contributors_2024": 523,
-    "contributors_2023": 630,
-    "contributors_2026_q1": 257
+    "homepage_link": "https://tensorflow.org"
   },
   {
     "repo_name": "transformers",
     "repo_link": "https://github.com/huggingface/transformers",
     "category": "multi-purpose library",
     "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.",
-    "homepage_link": "https://huggingface.co/transformers",
-    "github_topic_closest_fit": "machine-learning",
-    "contributors_all": 3801,
-    "contributors_2025": 860,
-    "contributors_2024": 769,
-    "contributors_2023": 758,
-    "contributors_2026_q1": 222
+    "homepage_link": "https://huggingface.co/transformers"
   },
   {
     "repo_name": "spark",
     "repo_link": "https://github.com/apache/spark",
     "category": "data processing",
     "github_about_section": "Apache Spark - A unified analytics engine for large-scale data processing",
-    "homepage_link": "https://spark.apache.org",
-    "github_topic_closest_fit": "data-processing",
-    "contributors_all": 3171,
-    "contributors_2025": 322,
-    "contributors_2024": 300,
-    "contributors_2023": 336,
-    "contributors_2026_q1": 132
+    "homepage_link": "https://spark.apache.org"
   },
   {
     "repo_name": "hhvm",
     "repo_link": "https://github.com/facebook/hhvm",
     "category": "virtual machine",
     "github_about_section": "A virtual machine for executing programs written in Hack.",
-    "homepage_link": "https://hhvm.com",
-    "github_topic_closest_fit": "virtual-machine",
-    "contributors_all": 2773,
-    "contributors_2025": 692,
-    "contributors_2024": 648,
-    "contributors_2023": 604,
-    "contributors_2026_q1": 383
+    "homepage_link": "https://hhvm.com"
   },
   {
     "repo_name": "vllm",
     "repo_link": "https://github.com/vllm-project/vllm",
     "category": "inference engine",
     "github_about_section": "A high-throughput and memory-efficient inference and serving engine for LLMs",
-    "homepage_link": "https://docs.vllm.ai",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 2513,
-    "contributors_2025": 1369,
-    "contributors_2024": 579,
-    "contributors_2023": 145,
-    "contributors_2026_q1": 698
+    "homepage_link": "https://docs.vllm.ai"
   },
   {
     "repo_name": "elasticsearch",
     "repo_link": "https://github.com/elastic/elasticsearch",
     "category": "search engine",
     "github_about_section": "Free and Open Source, Distributed, RESTful Search Engine",
-    "homepage_link": "https://elastic.co/products/elasticsearch",
-    "github_topic_closest_fit": "search-engine",
-    "contributors_all": 2360,
-    "contributors_2025": 317,
-    "contributors_2024": 284,
-    "contributors_2023": 270,
-    "contributors_2026_q1": 201
+    "homepage_link": "https://elastic.co/products/elasticsearch"
   },
   {
     "repo_name": "numpy",
     "repo_link": "https://github.com/numpy/numpy",
     "category": "scientific computing",
     "github_about_section": "The fundamental package for scientific computing with Python.",
-    "homepage_link": "https://numpy.org",
-    "github_topic_closest_fit": "scientific-computing",
-    "contributors_all": 2230,
-    "contributors_2025": 237,
-    "contributors_2024": 233,
-    "contributors_2023": 252,
-    "contributors_2026_q1": 80
+    "homepage_link": "https://numpy.org"
   },
   {
     "repo_name": "scipy",
     "repo_link": "https://github.com/scipy/scipy",
     "category": "scientific computing",
     "github_about_section": "SciPy library main repository",
-    "homepage_link": "https://scipy.org",
-    "github_topic_closest_fit": "scientific-computing",
-    "contributors_all": 2028,
-    "contributors_2025": 213,
-    "contributors_2024": 251,
-    "contributors_2023": 245,
-    "contributors_2026_q1": 76
+    "homepage_link": "https://scipy.org"
   },
   {
     "repo_name": "llama.cpp",
     "repo_link": "https://github.com/ggml-org/llama.cpp",
     "category": "inference engine",
     "github_about_section": "LLM inference in C/C++",
-    "homepage_link": "https://ggml.ai",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 1649,
-    "contributors_2025": 535,
-    "contributors_2024": 575,
-    "contributors_2023": 461,
-    "contributors_2026_q1": 246
+    "homepage_link": "https://ggml.ai"
   },
   {
     "repo_name": "ray",
     "repo_link": "https://github.com/ray-project/ray",
     "category": "multi-purpose library",
     "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.",
-    "homepage_link": "https://ray.io",
-    "github_topic_closest_fit": "machine-learning",
-    "contributors_all": 1509,
-    "contributors_2025": 397,
-    "contributors_2024": 223,
-    "contributors_2023": 230,
-    "contributors_2026_q1": 173
+    "homepage_link": "https://ray.io"
   },
   {
     "repo_name": "sglang",
     "repo_link": "https://github.com/sgl-project/sglang",
     "category": "inference engine",
     "github_about_section": "SGLang is a fast serving framework for large language models and vision language models.",
-    "homepage_link": "https://docs.sglang.ai",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 1370,
-    "contributors_2025": 796,
-    "contributors_2024": 189,
-    "contributors_2023": 1,
-    "contributors_2026_q1": 504
+    "homepage_link": "https://docs.sglang.ai"
   },
   {
     "repo_name": "rocm-systems",
     "repo_link": "https://github.com/ROCm/rocm-systems",
     "category": "multi-purpose library",
     "github_about_section": "super repo for rocm systems projects",
-    "homepage_link": "https://amd.com/en/products/software/rocm.html",
-    "github_topic_closest_fit": "amd",
-    "contributors_all": 1213,
-    "contributors_2025": 500,
-    "contributors_2024": 351,
-    "contributors_2023": 213,
-    "contributors_2026_q1": 257
+    "homepage_link": "https://amd.com/en/products/software/rocm.html"
   },
   {
     "repo_name": "jax",
     "repo_link": "https://github.com/jax-ml/jax",
     "category": "scientific computing",
     "github_about_section": "Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more",
-    "homepage_link": "https://docs.jax.dev",
-    "github_topic_closest_fit": "scientific-computing",
-    "contributors_all": 1059,
-    "contributors_2025": 317,
-    "contributors_2024": 280,
-    "contributors_2023": 202,
-    "contributors_2026_q1": 133
+    "homepage_link": "https://docs.jax.dev"
   },
   {
     "repo_name": "onnxruntime",
     "repo_link": "https://github.com/microsoft/onnxruntime",
     "category": "machine learning interoperability",
     "github_about_section": "ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator",
-    "homepage_link": "https://onnxruntime.ai",
-    "contributors_all": 899,
-    "contributors_2025": 237,
-    "contributors_2024": 213,
-    "contributors_2023": 213,
-    "contributors_2026_q1": 107
+    "homepage_link": "https://onnxruntime.ai"
   },
   {
     "repo_name": "verl",
     "repo_link": "https://github.com/volcengine/verl",
     "category": "reinforcement learning",
     "github_about_section": "verl: Volcano Engine Reinforcement Learning for LLMs",
-    "homepage_link": "https://verl.readthedocs.io",
-    "github_topic_closest_fit": "deep-reinforcement-learning",
-    "contributors_all": 624,
-    "contributors_2025": 454,
-    "contributors_2024": 10,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 153
+    "homepage_link": "https://verl.readthedocs.io"
   },
   {
     "repo_name": "ollama",
     "repo_link": "https://github.com/ollama/ollama",
     "category": "inference engine",
     "github_about_section": "Get up and running with OpenAI gpt-oss, DeepSeek-R1, Gemma 3 and other models.",
-    "homepage_link": "https://ollama.com",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 602,
-    "contributors_2025": 202,
-    "contributors_2024": 314,
-    "contributors_2023": 97,
-    "contributors_2026_q1": 40
+    "homepage_link": "https://ollama.com"
   },
   {
     "repo_name": "triton",
     "repo_link": "https://github.com/triton-lang/triton",
     "category": "parallel computing dsl",
     "github_about_section": "Development repository for the Triton language and compiler",
-    "homepage_link": "https://triton-lang.org",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 583,
-    "contributors_2025": 233,
-    "contributors_2024": 206,
-    "contributors_2023": 159,
-    "contributors_2026_q1": 105
+    "homepage_link": "https://triton-lang.org"
   },
   {
     "repo_name": "executorch",
     "repo_link": "https://github.com/pytorch/executorch",
     "category": "model compiler",
     "github_about_section": "On-device AI across mobile, embedded and edge for PyTorch",
-    "homepage_link": "https://executorch.ai",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 540,
-    "contributors_2025": 267,
-    "contributors_2024": 243,
-    "contributors_2023": 77,
-    "contributors_2026_q1": 136
+    "homepage_link": "https://executorch.ai"
   },
   {
     "repo_name": "triton-windows",
     "repo_link": "https://github.com/woct0rdho/triton-windows",
     "category": "parallel computing dsl",
-    "github_about_section": "Fork of the Triton language and compiler for Windows support and easy installation",
-    "contributors_all": 537,
-    "contributors_2025": 233,
-    "contributors_2024": 207,
-    "contributors_2023": 159,
-    "contributors_2026_q1": 67
+    "github_about_section": "Fork of the Triton language and compiler for Windows support and easy installation"
   },
   {
     "repo_name": "trl",
     "repo_link": "https://github.com/huggingface/trl",
     "category": "reinforcement learning",
     "github_about_section": "Train transformer language models with reinforcement learning.",
-    "homepage_link": "http://hf.co/docs/trl",
-    "github_topic_closest_fit": "reinforcement-learning",
-    "contributors_all": 491,
-    "contributors_2025": 189,
-    "contributors_2024": 154,
-    "contributors_2023": 122,
-    "contributors_2026_q1": 59
+    "homepage_link": "http://hf.co/docs/trl"
   },
   {
     "repo_name": "goose",
     "repo_link": "https://github.com/block/goose",
     "category": "agent",
     "github_about_section": "an open source, extensible AI agent that goes beyond code suggestions - install, execute, edit, and test with any LLM",
-    "homepage_link": "https://block.github.io/goose",
-    "github_topic_closest_fit": "ai-agents",
-    "contributors_all": 488,
-    "contributors_2025": 319,
-    "contributors_2024": 32,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 126
+    "homepage_link": "https://block.github.io/goose"
   },
   {
     "repo_name": "DeepSpeed",
     "repo_link": "https://github.com/deepspeedai/DeepSpeed",
     "category": "training framework",
     "github_about_section": "DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.",
-    "homepage_link": "https://deepspeed.ai",
-    "contributors_all": 472,
-    "contributors_2025": 96,
-    "contributors_2024": 134,
-    "contributors_2023": 165,
-    "contributors_2026_q1": 30
+    "homepage_link": "https://deepspeed.ai"
   },
   {
     "repo_name": "numba",
     "repo_link": "https://github.com/numba/numba",
     "category": "compiler",
     "github_about_section": "NumPy aware dynamic Python compiler using LLVM",
-    "homepage_link": "https://numba.pydata.org",
-    "contributors_all": 453,
-    "contributors_2025": 40,
-    "contributors_2024": 32,
-    "contributors_2023": 55,
-    "contributors_2026_q1": 26
+    "homepage_link": "https://numba.pydata.org"
   },
   {
     "repo_name": "torchrec",
     "repo_link": "https://github.com/meta-pytorch/torchrec",
     "category": "distributed computing",
     "github_about_section": "Pytorch domain library for recommendation systems",
-    "homepage_link": "https://meta-pytorch.org/torchrec/",
-    "contributors_all": 448,
-    "contributors_2026_q1": 75,
-    "contributors_2025": 153,
-    "contributors_2024": 155,
-    "contributors_2023": 100
+    "homepage_link": "https://meta-pytorch.org/torchrec/"
   },
   {
     "repo_name": "modular",
     "repo_link": "https://github.com/modular/modular",
     "category": "parallel computing",
     "github_about_section": "The Modular Platform (includes MAX & Mojo)",
-    "homepage_link": "https://docs.modular.com",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 429,
-    "contributors_2025": 222,
-    "contributors_2024": 205,
-    "contributors_2023": 99,
-    "contributors_2026_q1": 149
+    "homepage_link": "https://docs.modular.com"
   },
   {
     "repo_name": "accelerate",
     "repo_link": "https://github.com/huggingface/accelerate",
     "category": "training framework",
     "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.",
-    "homepage_link": "https://huggingface.co/docs/accelerate",
-    "contributors_all": 417,
-    "contributors_2025": 97,
-    "contributors_2024": 124,
-    "contributors_2023": 149,
-    "contributors_2026_q1": 25
+    "homepage_link": "https://huggingface.co/docs/accelerate"
   },
   {
     "repo_name": "milvus",
     "repo_link": "https://github.com/milvus-io/milvus",
     "category": "vector database",
     "github_about_section": "Milvus is a high-performance, cloud-native vector database built for scalable vector ANN search",
-    "homepage_link": "https://milvus.io",
-    "github_topic_closest_fit": "vector-search",
-    "contributors_all": 402,
-    "contributors_2025": 95,
-    "contributors_2024": 84,
-    "contributors_2023": 72,
-    "contributors_2026_q1": 49
+    "homepage_link": "https://milvus.io"
   },
   {
     "repo_name": "onnx",
     "repo_link": "https://github.com/onnx/onnx",
     "category": "machine learning interoperability",
     "github_about_section": "Open standard for machine learning interoperability",
-    "homepage_link": "https://onnx.ai",
-    "github_topic_closest_fit": "onnx",
-    "contributors_all": 393,
-    "contributors_2025": 56,
-    "contributors_2024": 45,
-    "contributors_2023": 61,
-    "contributors_2026_q1": 21
+    "homepage_link": "https://onnx.ai"
   },
   {
     "repo_name": "modelcontextprotocol",
     "repo_link": "https://github.com/modelcontextprotocol/modelcontextprotocol",
     "category": "mcp",
     "github_about_section": "Specification and documentation for the Model Context Protocol",
-    "homepage_link": "https://modelcontextprotocol.io",
-    "github_topic_closest_fit": "mcp",
-    "contributors_all": 376,
-    "contributors_2025": 301,
-    "contributors_2024": 42,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 69
+    "homepage_link": "https://modelcontextprotocol.io"
   },
   {
     "repo_name": "ComfyUI",
     "repo_link": "https://github.com/comfyanonymous/ComfyUI",
     "category": "user interface",
     "github_about_section": "The most powerful and modular diffusion model GUI, api and backend with a graph/nodes interface.",
-    "homepage_link": "https://comfy.org",
-    "github_topic_closest_fit": "stable-diffusion",
-    "contributors_all": 331,
-    "contributors_2025": 108,
-    "contributors_2024": 119,
-    "contributors_2023": 94,
-    "contributors_2026_q1": 60
+    "homepage_link": "https://comfy.org"
   },
   {
     "repo_name": "peft",
     "repo_link": "https://github.com/huggingface/peft",
     "category": "fine tuning",
     "github_about_section": "PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.",
-    "homepage_link": "https://huggingface.co/docs/peft",
-    "contributors_all": 304,
-    "contributors_2025": 69,
-    "contributors_2024": 111,
-    "contributors_2023": 115,
-    "contributors_2026_q1": 25
+    "homepage_link": "https://huggingface.co/docs/peft"
   },
   {
     "repo_name": "flashinfer",
     "repo_link": "https://github.com/flashinfer-ai/flashinfer",
     "category": "gpu kernels",
     "github_about_section": "FlashInfer: Kernel Library for LLM Serving",
-    "homepage_link": "https://flashinfer.ai",
-    "github_topic_closest_fit": "attention",
-    "contributors_all": 290,
-    "contributors_2025": 158,
-    "contributors_2024": 50,
-    "contributors_2023": 11,
-    "contributors_2026_q1": 86
+    "homepage_link": "https://flashinfer.ai"
   },
   {
     "repo_name": "trainer",
     "repo_link": "https://github.com/kubeflow/trainer",
     "github_about_section": "Distributed AI Model Training and LLM Fine-Tuning on Kubernetes",
-    "homepage_link": "https://www.kubeflow.org/docs/components/trainer/",
-    "contributors_all": 278,
-    "contributors_2026_q1": 26,
-    "contributors_2025": 51,
-    "contributors_2024": 47,
-    "contributors_2023": 25
+    "homepage_link": "https://www.kubeflow.org/docs/components/trainer/"
   },
   {
     "repo_name": "cutlass",
     "repo_link": "https://github.com/NVIDIA/cutlass",
     "category": "parallel computing",
     "github_about_section": "CUDA Templates and Python DSLs for High-Performance Linear Algebra",
-    "homepage_link": "https://docs.nvidia.com/cutlass/index.html",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 272,
-    "contributors_2025": 94,
-    "contributors_2024": 64,
-    "contributors_2023": 66,
-    "contributors_2026_q1": 39
+    "homepage_link": "https://docs.nvidia.com/cutlass/index.html"
   },
   {
     "repo_name": "aiter",
     "repo_link": "https://github.com/ROCm/aiter",
     "category": "gpu kernels",
     "github_about_section": "AI Tensor Engine for ROCm",
-    "homepage_link": "https://rocm.blogs.amd.com/software-tools-optimization/aiter-ai-tensor-engine/README.html",
-    "contributors_all": 257,
-    "contributors_2025": 145,
-    "contributors_2024": 10,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 117
+    "homepage_link": "https://rocm.blogs.amd.com/software-tools-optimization/aiter-ai-tensor-engine/README.html"
   },
   {
     "repo_name": "wandb",
     "repo_link": "https://github.com/wandb/wandb",
     "category": "ml visualization",
     "github_about_section": "The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.",
-    "homepage_link": "https://wandb.ai",
-    "contributors_all": 242,
-    "contributors_2025": 46,
-    "contributors_2024": 67,
-    "contributors_2023": 62,
-    "contributors_2026_q1": 24
+    "homepage_link": "https://wandb.ai"
   },
   {
     "repo_name": "ao",
     "repo_link": "https://github.com/pytorch/ao",
     "category": "quantization",
     "github_about_section": "PyTorch native quantization and sparsity for training and inference",
-    "homepage_link": "https://pytorch.org/ao",
-    "github_topic_closest_fit": "quantization",
-    "contributors_all": 230,
-    "contributors_2025": 114,
-    "contributors_2024": 100,
-    "contributors_2023": 5,
-    "contributors_2026_q1": 67
+    "homepage_link": "https://pytorch.org/ao"
   },
   {
     "repo_name": "Mooncake",
     "repo_link": "https://github.com/kvcache-ai/Mooncake",
     "category": "inference",
     "github_about_section": "Mooncake is the serving platform for Kimi, a leading LLM service provided by Moonshot AI.",
-    "homepage_link": "https://kvcache-ai.github.io/Mooncake",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 228,
-    "contributors_2025": 133,
-    "contributors_2024": 13,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 80
+    "homepage_link": "https://kvcache-ai.github.io/Mooncake"
   },
   {
     "repo_name": "ccache",
     "repo_link": "https://github.com/ccache/ccache",
     "category": "compiler",
     "github_about_section": "ccache - a fast compiler cache",
-    "homepage_link": "https://ccache.dev",
-    "contributors_all": 225,
-    "contributors_2025": 20,
-    "contributors_2024": 28,
-    "contributors_2023": 22,
-    "contributors_2026_q1": 10
+    "homepage_link": "https://ccache.dev"
   },
   {
     "repo_name": "unsloth",
     "repo_link": "https://github.com/unslothai/unsloth",
     "category": "fine tuning",
     "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.",
-    "homepage_link": "https://docs.unsloth.ai",
-    "github_topic_closest_fit": "fine-tuning",
-    "contributors_all": 204,
-    "contributors_2025": 108,
-    "contributors_2024": 29,
-    "contributors_2023": 3,
-    "contributors_2026_q1": 55
+    "homepage_link": "https://docs.unsloth.ai"
   },
   {
     "repo_name": "torchtitan",
     "repo_link": "https://github.com/pytorch/torchtitan",
     "category": "training framework",
     "github_about_section": "A PyTorch native platform for training generative AI models",
-    "homepage_link": "https://arxiv.org/abs/2410.06511",
-    "contributors_all": 200,
-    "contributors_2025": 119,
-    "contributors_2024": 43,
-    "contributors_2023": 1,
-    "contributors_2026_q1": 59
+    "homepage_link": "https://arxiv.org/abs/2410.06511"
   },
   {
     "repo_name": "LMCache",
     "repo_link": "https://github.com/LMCache/LMCache",
     "category": "inference",
     "github_about_section": "Supercharge Your LLM with the Fastest KV Cache Layer",
-    "homepage_link": "https://lmcache.ai",
-    "contributors_all": 190,
-    "contributors_2025": 144,
-    "contributors_2024": 18,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 42
+    "homepage_link": "https://lmcache.ai"
   },
   {
     "repo_name": "lapack",
     "repo_link": "https://github.com/Reference-LAPACK/lapack",
     "category": "linear algebra",
     "github_about_section": "LAPACK is a library of Fortran subroutines for solving the most commonly occurring problems in numerical linear algebra.",
-    "homepage_link": "https://netlib.org/lapack",
-    "github_topic_closest_fit": "linear-algebra",
-    "contributors_all": 188,
-    "contributors_2025": 23,
-    "contributors_2024": 25,
-    "contributors_2023": 42,
-    "contributors_2026_q1": 11
+    "homepage_link": "https://netlib.org/lapack"
   },
   {
     "repo_name": "FlagGems",
     "repo_link": "https://github.com/flagos-ai/FlagGems",
-    "github_about_section": "FlagGems is an operator library for large language models implemented in the Triton Language.",
-    "contributors_all": 184,
-    "contributors_2026_q1": 61,
-    "contributors_2025": 92,
-    "contributors_2024": 39,
-    "contributors_2023": 0
+    "github_about_section": "FlagGems is an operator library for large language models implemented in the Triton Language."
   },
   {
     "repo_name": "ROCm",
     "repo_link": "https://github.com/ROCm/ROCm",
     "category": "multi-purpose library",
     "github_about_section": "AMD ROCm Software - GitHub Home",
-    "homepage_link": "https://rocm.docs.amd.com",
-    "contributors_all": 170,
-    "contributors_2025": 67,
-    "contributors_2024": 61,
-    "contributors_2023": 44,
-    "contributors_2026_q1": 25
+    "homepage_link": "https://rocm.docs.amd.com"
   },
   {
     "repo_name": "letta",
     "repo_link": "https://github.com/letta-ai/letta",
     "category": "agent",
     "github_about_section": "Letta is the platform for building stateful agents: open AI with advanced memory that can learn and self-improve over time.",
-    "homepage_link": "https://docs.letta.com",
-    "github_topic_closest_fit": "ai-agents",
-    "contributors_all": 159,
-    "contributors_2025": 57,
-    "contributors_2024": 75,
-    "contributors_2023": 47,
-    "contributors_2026_q1": 16
+    "homepage_link": "https://docs.letta.com"
   },
   {
     "repo_name": "server",
     "repo_link": "https://github.com/triton-inference-server/server",
     "category": "inference server",
     "github_about_section": "The Triton Inference Server provides an optimized cloud and edge inferencing solution.",
-    "homepage_link": "https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 150,
-    "contributors_2025": 24,
-    "contributors_2024": 36,
-    "contributors_2023": 34,
-    "contributors_2026_q1": 7
+    "homepage_link": "https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html"
   },
   {
     "repo_name": "aws-neuron-sdk",
     "repo_link": "https://github.com/aws-neuron/aws-neuron-sdk",
     "category": "sdk",
     "github_about_section": "Powering AWS purpose-built machine learning chips. Blazing fast and cost effective, natively integrated into PyTorch and TensorFlow and integrated with your favorite AWS services",
-    "homepage_link": "https://aws.amazon.com/ai/machine-learning/neuron",
-    "contributors_all": 148,
-    "contributors_2025": 33,
-    "contributors_2024": 37,
-    "contributors_2023": 32,
-    "contributors_2026_q1": 10
+    "homepage_link": "https://aws.amazon.com/ai/machine-learning/neuron"
   },
   {
     "repo_name": "Liger-Kernel",
     "repo_link": "https://github.com/linkedin/Liger-Kernel",
     "category": "kernel examples",
     "github_about_section": "Efficient Triton Kernels for LLM Training",
-    "homepage_link": "https://openreview.net/pdf?id=36SjAIT42G",
-    "github_topic_closest_fit": "triton",
-    "contributors_all": 146,
-    "contributors_2025": 78,
-    "contributors_2024": 61,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 31
+    "homepage_link": "https://openreview.net/pdf?id=36SjAIT42G"
   },
   {
     "repo_name": "tilelang",
     "repo_link": "https://github.com/tile-ai/tilelang",
     "category": "parallel computing dsl",
     "github_about_section": "Domain-specific language designed to streamline the development of high-performance GPU/CPU/Accelerators kernels",
-    "homepage_link": "https://tilelang.com",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 143,
-    "contributors_2025": 89,
-    "contributors_2024": 1,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 50
+    "homepage_link": "https://tilelang.com"
   },
   {
     "repo_name": "metaflow",
     "repo_link": "https://github.com/Netflix/metaflow",
     "category": "container orchestration",
     "github_about_section": "Build, Manage and Deploy AI/ML Systems",
-    "homepage_link": "https://metaflow.org",
-    "contributors_all": 137,
-    "contributors_2025": 37,
-    "contributors_2024": 35,
-    "contributors_2023": 28,
-    "contributors_2026_q1": 23
+    "homepage_link": "https://metaflow.org"
   },
   {
     "repo_name": "litgpt",
     "repo_link": "https://github.com/Lightning-AI/litgpt",
     "github_about_section": "20+ high-performance LLMs with recipes to pretrain, finetune and deploy at scale.",
-    "homepage_link": "https://lightning.ai/",
-    "contributors_all": 135,
-    "contributors_2026_q1": 11,
-    "contributors_2025": 36,
-    "contributors_2024": 54,
-    "contributors_2023": 54
+    "homepage_link": "https://lightning.ai/"
   },
   {
     "repo_name": "monarch",
     "repo_link": "https://github.com/meta-pytorch/monarch",
     "category": "distributed computing",
     "github_about_section": "PyTorch Single Controller",
-    "homepage_link": "https://meta-pytorch.org/monarch",
-    "contributors_all": 119,
-    "contributors_2025": 85,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 45
+    "homepage_link": "https://meta-pytorch.org/monarch"
   },
   {
     "repo_name": "hipBLASLt",
     "repo_link": "https://github.com/AMD-AGI/hipBLASLt",
     "category": "Basic Linear Algebra Subprograms (BLAS)",
     "github_about_section": "hipBLASLt is a library that provides general matrix-matrix operations with a flexible API and extends functionalities beyond a traditional BLAS library",
-    "homepage_link": "https://rocm.docs.amd.com/projects/hipBLASLt",
-    "github_topic_closest_fit": "matrix-multiplication",
-    "contributors_all": 111,
-    "contributors_2025": 69,
-    "contributors_2024": 70,
-    "contributors_2023": 35,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://rocm.docs.amd.com/projects/hipBLASLt"
   },
   {
     "repo_name": "flash-linear-attention",
     "repo_link": "https://github.com/fla-org/flash-linear-attention",
     "category": "gpu kernels",
-    "github_about_section": "Efficient implementations of state-of-the-art linear attention models",
-    "contributors_all": 108,
-    "contributors_2025": 64,
-    "contributors_2024": 22,
-    "contributors_2023": 3,
-    "contributors_2026_q1": 18
+    "github_about_section": "Efficient implementations of state-of-the-art linear attention models"
   },
   {
     "repo_name": "TensorRT",
     "repo_link": "https://github.com/NVIDIA/TensorRT",
     "category": "inference engine",
     "github_about_section": "NVIDIA TensorRT is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.",
-    "homepage_link": "https://developer.nvidia.com/tensorrt",
-    "contributors_all": 104,
-    "contributors_2025": 10,
-    "contributors_2024": 18,
-    "contributors_2023": 19,
-    "contributors_2026_q1": 4
+    "homepage_link": "https://developer.nvidia.com/tensorrt"
   },
   {
     "repo_name": "AReal",
     "repo_link": "https://github.com/inclusionAI/AReaL",
     "category": "reinforcement learning",
     "github_about_section": "The RL Bridge for LLM-based Agent Applications. Made Simple & Flexible.",
-    "homepage_link": "https://www.inclusion-ai.org/AReaL",
-    "contributors_all": 100,
-    "contributors_2026_q1": 37,
-    "contributors_2025": 63,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://www.inclusion-ai.org/AReaL"
   },
   {
     "repo_name": "terminal-bench",
     "repo_link": "https://github.com/laude-institute/terminal-bench",
     "category": "benchmark",
     "github_about_section": "A benchmark for LLMs on complicated tasks in the terminal",
-    "homepage_link": "https://tbench.ai",
-    "github_topic_closest_fit": "benchmark",
-    "contributors_all": 96,
-    "contributors_2025": 96,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 2
+    "homepage_link": "https://tbench.ai"
   },
   {
     "repo_name": "warp",
     "repo_link": "https://github.com/NVIDIA/warp",
     "category": "spatial computing",
     "github_about_section": "A Python framework for accelerated simulation, data generation and spatial computing.",
-    "homepage_link": "https://nvidia.github.io/warp",
-    "github_topic_closest_fit": "physics-simulation",
-    "contributors_all": 94,
-    "contributors_2025": 40,
-    "contributors_2024": 29,
-    "contributors_2023": 17,
-    "contributors_2026_q1": 24
+    "homepage_link": "https://nvidia.github.io/warp"
   },
   {
     "repo_name": "OpenRLHF",
     "repo_link": "https://github.com/OpenRLHF/OpenRLHF",
     "category": "reinforcement learning",
     "github_about_section": "An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)",
-    "homepage_link": "https://openrlhf.readthedocs.io",
-    "contributors_all": 94,
-    "contributors_2026_q1": 9,
-    "contributors_2025": 42,
-    "contributors_2024": 45,
-    "contributors_2023": 19
+    "homepage_link": "https://openrlhf.readthedocs.io"
   },
   {
     "repo_name": "truss",
     "repo_link": "https://github.com/basetenlabs/truss",
     "category": "inference engine",
     "github_about_section": "The simplest way to serve AI/ML models in production",
-    "homepage_link": "https://truss.baseten.co",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 89,
-    "contributors_2025": 44,
-    "contributors_2024": 30,
-    "contributors_2023": 21,
-    "contributors_2026_q1": 30
+    "homepage_link": "https://truss.baseten.co"
   },
   {
     "repo_name": "RLinf",
     "repo_link": "https://github.com/RLinf/RLinf",
     "category": "reinforcement learning",
     "github_about_section": "RLinf: Reinforcement Learning Infrastructure for Embodied and Agentic AI",
-    "homepage_link": "https://rlinf.readthedocs.io",
-    "contributors_all": 86,
-    "contributors_2026_q1": 52,
-    "contributors_2025": 35,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://rlinf.readthedocs.io"
   },
   {
     "repo_name": "nccl",
     "repo_link": "https://github.com/NVIDIA/nccl",
     "category": "distributed computing",
     "github_about_section": "Optimized primitives for collective multi-GPU communication",
-    "homepage_link": "https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html",
-    "contributors_all": 85,
-    "contributors_2026_q1": 26,
-    "contributors_2025": 10,
-    "contributors_2024": 5,
-    "contributors_2023": 6
+    "homepage_link": "https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/index.html"
   },
   {
     "repo_name": "SkyRL",
     "repo_link": "https://github.com/NovaSky-AI/SkyRL",
     "category": "reinforcement learning",
     "github_about_section": "SkyRL: A Modular Full-stack RL Library for LLMs",
-    "homepage_link": "https://docs.skyrl.ai/docs",
-    "contributors_all": 83,
-    "contributors_2026_q1": 29,
-    "contributors_2025": 57,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://docs.skyrl.ai/docs"
   },
   {
     "repo_name": "ROLL",
     "repo_link": "https://github.com/alibaba/ROLL",
     "category": "reinforcement learning",
     "github_about_section": "An Efficient and User-Friendly Scaling Library for Reinforcement Learning with Large Language Models",
-    "homepage_link": "https://alibaba.github.io/ROLL/",
-    "contributors_all": 82,
-    "contributors_2026_q1": 30,
-    "contributors_2025": 60,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://alibaba.github.io/ROLL/"
   },
   {
     "repo_name": "lightning-thunder",
     "repo_link": "https://github.com/Lightning-AI/lightning-thunder",
     "category": "model compiler",
-    "github_about_section": "PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own.",
-    "contributors_all": 79,
-    "contributors_2025": 44,
-    "contributors_2024": 47,
-    "contributors_2023": 29,
-    "contributors_2026_q1": 7
+    "github_about_section": "PyTorch compiler that accelerates training and inference. Get built-in optimizations for performance, memory, parallelism, and easily write your own."
   },
   {
     "repo_name": "helion",
     "repo_link": "https://github.com/pytorch/helion",
     "category": "parallel computing dsl",
     "github_about_section": "A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.",
-    "homepage_link": "https://helionlang.com",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 77,
-    "contributors_2025": 49,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 41
+    "homepage_link": "https://helionlang.com"
   },
   {
     "repo_name": "ort",
     "repo_link": "https://github.com/pykeio/ort",
     "category": "machine learning interoperability",
     "github_about_section": "Fast ML inference & training for ONNX models in Rust",
-    "homepage_link": "https://ort.pyke.io",
-    "contributors_all": 74,
-    "contributors_2025": 25,
-    "contributors_2024": 20,
-    "contributors_2023": 21,
-    "contributors_2026_q1": 11
+    "homepage_link": "https://ort.pyke.io"
   },
   {
     "repo_name": "deepinv",
     "repo_link": "https://github.com/deepinv/deepinv",
     "github_about_section": "DeepInverse: a PyTorch library for solving imaging inverse problems using deep learning",
-    "homepage_link": "https://deepinv.github.io/deepinv/",
-    "contributors_all": 74,
-    "contributors_2026_q1": 16,
-    "contributors_2025": 40,
-    "contributors_2024": 27,
-    "contributors_2023": 14
+    "homepage_link": "https://deepinv.github.io/deepinv/"
   },
   {
     "repo_name": "dstack",
     "repo_link": "https://github.com/dstackai/dstack",
     "category": "container orchestration",
     "github_about_section": "dstack is an open-source control plane for running development, training, and inference jobs on GPUs-across hyperscalers, neoclouds, or on-prem.",
-    "homepage_link": "https://dstack.ai",
-    "github_topic_closest_fit": "orchestration",
-    "contributors_all": 70,
-    "contributors_2025": 28,
-    "contributors_2024": 42,
-    "contributors_2023": 14,
-    "contributors_2026_q1": 9
+    "homepage_link": "https://dstack.ai"
   },
   {
     "repo_name": "doctr",
     "repo_link": "https://github.com/mindee/doctr",
     "github_about_section": "docTR (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.",
-    "homepage_link": "https://mindee.github.io/doctr/",
-    "contributors_all": 69,
-    "contributors_2026_q1": 3,
-    "contributors_2025": 18,
-    "contributors_2024": 19,
-    "contributors_2023": 19
+    "homepage_link": "https://mindee.github.io/doctr/"
   },
   {
     "repo_name": "SWE-bench",
     "repo_link": "https://github.com/SWE-bench/SWE-bench",
     "category": "benchmark",
     "github_about_section": "SWE-bench: Can Language Models Resolve Real-world Github Issues?",
-    "homepage_link": "https://swebench.com",
-    "github_topic_closest_fit": "benchmark",
-    "contributors_all": 66,
-    "contributors_2025": 33,
-    "contributors_2024": 37,
-    "contributors_2023": 9,
-    "contributors_2026_q1": 2
+    "homepage_link": "https://swebench.com"
   },
   {
     "repo_name": "torchchat",
     "repo_link": "https://github.com/pytorch/torchchat",
-    "github_about_section": "Run PyTorch LLMs locally on servers, desktop and mobile",
-    "contributors_all": 66,
-    "contributors_2026_q1": 0,
-    "contributors_2025": 18,
-    "contributors_2024": 56,
-    "contributors_2023": 0
+    "github_about_section": "Run PyTorch LLMs locally on servers, desktop and mobile"
   },
   {
     "repo_name": "mcp-agent",
     "repo_link": "https://github.com/lastmile-ai/mcp-agent",
     "category": "mcp",
-    "github_about_section": "Build effective agents using Model Context Protocol and simple workflow patterns",
-    "github_topic_closest_fit": "mcp",
-    "contributors_all": 64,
-    "contributors_2025": 63,
-    "contributors_2024": 1,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 1
+    "github_about_section": "Build effective agents using Model Context Protocol and simple workflow patterns"
   },
   {
     "repo_name": "prime-rl",
     "repo_link": "https://github.com/PrimeIntellect-ai/prime-rl",
     "category": "reinforcement learning",
-    "github_about_section": "Agentic RL Training at Scale",
-    "contributors_all": 62,
-    "contributors_2026_q1": 29,
-    "contributors_2025": 40,
-    "contributors_2024": 8,
-    "contributors_2023": 0
+    "github_about_section": "Agentic RL Training at Scale"
   },
   {
     "repo_name": "cuda-python",
     "repo_link": "https://github.com/NVIDIA/cuda-python",
     "category": "middleware",
     "github_about_section": "CUDA Python: Performance meets Productivity",
-    "homepage_link": "https://nvidia.github.io/cuda-python",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 60,
-    "contributors_2025": 41,
-    "contributors_2024": 12,
-    "contributors_2023": 1,
-    "contributors_2026_q1": 16
+    "homepage_link": "https://nvidia.github.io/cuda-python"
   },
   {
     "repo_name": "open-instruct",
     "repo_link": "https://github.com/allenai/open-instruct",
     "category": "reinforcement learning",
     "github_about_section": "AllenAI's post-training codebase",
-    "homepage_link": "https://allenai.github.io/open-instruct/",
-    "contributors_all": 59,
-    "contributors_2026_q1": 12,
-    "contributors_2025": 28,
-    "contributors_2024": 24,
-    "contributors_2023": 8
+    "homepage_link": "https://allenai.github.io/open-instruct/"
   },
   {
     "repo_name": "openevolve",
     "repo_link": "https://github.com/codelion/openevolve",
     "category": "evolutionary algorithm",
-    "github_about_section": "Open-source implementation of AlphaEvolve",
-    "github_topic_closest_fit": "genetic-algorithm",
-    "contributors_all": 51,
-    "contributors_2025": 46,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 7
+    "github_about_section": "Open-source implementation of AlphaEvolve"
   },
   {
     "repo_name": "litserve",
     "repo_link": "https://github.com/Lightning-AI/litserve",
     "github_about_section": "A minimal Python framework for building custom AI inference servers with full control over logic, batching, and scaling.",
-    "homepage_link": "https://lightning.ai/litserve",
-    "contributors_all": 49,
-    "contributors_2026_q1": 6,
-    "contributors_2025": 27,
-    "contributors_2024": 29,
-    "contributors_2023": 2
+    "homepage_link": "https://lightning.ai/litserve"
   },
   {
     "repo_name": "openzl",
     "repo_link": "https://github.com/facebook/openzl",
     "category": "data compression",
     "github_about_section": "A novel data compression framework",
-    "homepage_link": "https://openzl.org",
-    "contributors_all": 44,
-    "contributors_2026_q1": 24,
-    "contributors_2025": 22,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://openzl.org"
   },
   {
     "repo_name": "torchforge",
     "repo_link": "https://github.com/meta-pytorch/torchforge",
     "category": "reinforcement learning",
     "github_about_section": "PyTorch-native post-training at scale",
-    "homepage_link": "https://meta-pytorch.org/torchforge",
-    "contributors_all": 43,
-    "contributors_2026_q1": 12,
-    "contributors_2025": 36,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://meta-pytorch.org/torchforge"
   },
   {
     "repo_name": "torchcodec",
     "repo_link": "https://github.com/meta-pytorch/torchcodec",
     "github_about_section": "PyTorch media decoding and encoding",
-    "homepage_link": "https://meta-pytorch.org/torchcodec/stable/index.html",
-    "contributors_all": 42,
-    "contributors_2026_q1": 12,
-    "contributors_2025": 27,
-    "contributors_2024": 11,
-    "contributors_2023": 0
+    "homepage_link": "https://meta-pytorch.org/torchcodec/stable/index.html"
   },
   {
     "repo_name": "quack",
     "repo_link": "https://github.com/Dao-AILab/quack",
     "category": "kernel examples",
-    "github_about_section": "A Quirky Assortment of CuTe Kernels",
-    "contributors_all": 40,
-    "contributors_2025": 17,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 14
+    "github_about_section": "A Quirky Assortment of CuTe Kernels"
   },
   {
     "repo_name": "Triton-distributed",
     "repo_link": "https://github.com/ByteDance-Seed/Triton-distributed",
     "category": "distributed computing",
     "github_about_section": "Distributed Compiler based on Triton for Parallel Systems",
-    "homepage_link": "https://triton-distributed.readthedocs.io",
-    "contributors_all": 37,
-    "contributors_2025": 30,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 11
+    "homepage_link": "https://triton-distributed.readthedocs.io"
   },
   {
     "repo_name": "ThunderKittens",
     "repo_link": "https://github.com/HazyResearch/ThunderKittens",
     "category": "parallel computing",
     "github_about_section": "Tile primitives for speedy kernels",
-    "homepage_link": "https://hazyresearch.stanford.edu/blog/2024-10-29-tk2",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 37,
-    "contributors_2025": 29,
-    "contributors_2024": 13,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 6
+    "homepage_link": "https://hazyresearch.stanford.edu/blog/2024-10-29-tk2"
   },
   {
     "repo_name": "kernels",
     "repo_link": "https://github.com/huggingface/kernels",
     "category": "gpu kernels",
-    "github_about_section": "Load compute kernels from the Hub",
-    "contributors_all": 34,
-    "contributors_2025": 14,
-    "contributors_2024": 2,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 10
+    "github_about_section": "Load compute kernels from the Hub"
   },
   {
     "repo_name": "GEAK-agent",
     "repo_link": "https://github.com/AMD-AGI/GEAK-agent",
     "category": "agent",
-    "github_about_section": "It is an LLM-based AI agent, which can write correct and efficient gpu kernels automatically.",
-    "github_topic_closest_fit": "ai-agents",
-    "contributors_all": 34,
-    "contributors_2025": 9,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 12
+    "github_about_section": "It is an LLM-based AI agent, which can write correct and efficient gpu kernels automatically."
   },
   {
     "repo_name": "ome",
     "repo_link": "https://github.com/sgl-project/ome",
     "category": "container orchestration",
     "github_about_section": "OME is a Kubernetes operator for enterprise-grade management and serving of Large Language Models (LLMs)",
-    "homepage_link": "http://docs.sglang.ai/ome",
-    "github_topic_closest_fit": "k8s",
-    "contributors_all": 32,
-    "contributors_2025": 28,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 13
+    "homepage_link": "http://docs.sglang.ai/ome"
   },
   {
     "repo_name": "OLMo-core",
     "repo_link": "https://github.com/allenai/OLMo-core",
     "category": "training framework",
     "github_about_section": "PyTorch building blocks for the OLMo ecosystem",
-    "homepage_link": "https://olmo-core.readthedocs.io/en/latest/",
-    "contributors_all": 32,
-    "contributors_2026_q1": 9,
-    "contributors_2025": 23,
-    "contributors_2024": 9,
-    "contributors_2023": 0
+    "homepage_link": "https://olmo-core.readthedocs.io/en/latest/"
   },
   {
     "repo_name": "mistral-inference",
     "repo_link": "https://github.com/mistralai/mistral-inference",
     "category": "inference engine",
     "github_about_section": "Official inference library for Mistral models",
-    "homepage_link": "https://mistral.ai",
-    "github_topic_closest_fit": "inference",
-    "contributors_all": 31,
-    "contributors_2025": 2,
-    "contributors_2024": 17,
-    "contributors_2023": 14,
-    "contributors_2026_q1": 2
+    "homepage_link": "https://mistral.ai"
   },
   {
     "repo_name": "triSYCL",
     "repo_link": "https://github.com/triSYCL/triSYCL",
     "category": "parallel computing",
     "github_about_section": "Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group",
-    "homepage_link": "https://trisycl.github.io/triSYCL/Doxygen/triSYCL/html/index.html",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 31,
-    "contributors_2025": 0,
-    "contributors_2024": 1,
-    "contributors_2023": 3,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://trisycl.github.io/triSYCL/Doxygen/triSYCL/html/index.html"
   },
   {
     "repo_name": "tritonparse",
     "repo_link": "https://github.com/meta-pytorch/tritonparse",
     "category": "performance testing",
     "github_about_section": "TritonParse: A Compiler Tracer, Visualizer, and Reproducer for Triton Kernels",
-    "homepage_link": "https://meta-pytorch.org/tritonparse",
-    "contributors_all": 29,
-    "contributors_2025": 15,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 15
+    "homepage_link": "https://meta-pytorch.org/tritonparse"
   },
   {
     "repo_name": "StreamDiffusion",
     "repo_link": "https://github.com/cumulo-autumn/StreamDiffusion",
     "category": "image generation",
     "github_about_section": "StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation",
-    "homepage_link": "https://arxiv.org/abs/2312.12491",
-    "github_topic_closest_fit": "diffusion-models",
-    "contributors_all": 29,
-    "contributors_2025": 0,
-    "contributors_2024": 9,
-    "contributors_2023": 25,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://arxiv.org/abs/2312.12491"
   },
   {
     "repo_name": "reference-kernels",
     "repo_link": "https://github.com/gpu-mode/reference-kernels",
     "category": "kernel examples",
     "github_about_section": "Official Problem Sets / Reference Kernels for the GPU MODE Leaderboard!",
-    "homepage_link": "https://gpumode.com",
-    "contributors_all": 25,
-    "contributors_2025": 16,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 13
+    "homepage_link": "https://gpumode.com"
   },
   {
     "repo_name": "hatchet",
     "repo_link": "https://github.com/LLNL/hatchet",
     "category": "performance testing",
     "github_about_section": "Graph-indexed Pandas DataFrames for analyzing hierarchical performance data",
-    "homepage_link": "https://llnl-hatchet.readthedocs.io",
-    "github_topic_closest_fit": "profiling",
-    "contributors_all": 25,
-    "contributors_2025": 3,
-    "contributors_2024": 6,
-    "contributors_2023": 8,
-    "contributors_2026_q1": 1
+    "homepage_link": "https://llnl-hatchet.readthedocs.io"
   },
   {
     "repo_name": "kernelbot",
     "repo_link": "https://github.com/gpu-mode/kernelbot",
     "category": "kernel examples",
     "github_about_section": "Write a fast kernel and see how you compare against the best humans and AI on gpumode.com",
-    "homepage_link": "https://www.gpumode.com",
-    "contributors_all": 25,
-    "contributors_2026_q1": 9,
-    "contributors_2025": 19,
-    "contributors_2024": 8,
-    "contributors_2023": 0
+    "homepage_link": "https://www.gpumode.com"
   },
   {
     "repo_name": "cutile-python",
     "repo_link": "https://github.com/NVIDIA/cutile-python",
     "category": "parallel computing",
     "github_about_section": "cuTile is a programming model for writing parallel kernels for NVIDIA GPUs",
-    "homepage_link": "https://docs.nvidia.com/cuda/cutile-python",
-    "contributors_all": 23,
-    "contributors_2025": 10,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 14
+    "homepage_link": "https://docs.nvidia.com/cuda/cutile-python"
   },
   {
     "repo_name": "FTorch",
     "repo_link": "https://github.com/Cambridge-ICCS/FTorch",
     "category": "middleware",
     "github_about_section": "A library for directly calling PyTorch ML models from Fortran.",
-    "homepage_link": "https://cambridge-iccs.github.io/FTorch",
-    "github_topic_closest_fit": "machine-learning",
-    "contributors_all": 22,
-    "contributors_2025": 12,
-    "contributors_2024": 8,
-    "contributors_2023": 9,
-    "contributors_2026_q1": 4
+    "homepage_link": "https://cambridge-iccs.github.io/FTorch"
   },
   {
     "repo_name": "KernelBench",
     "repo_link": "https://github.com/ScalingIntelligence/KernelBench",
     "category": "benchmark",
     "github_about_section": "KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems",
-    "homepage_link": "https://scalingintelligence.stanford.edu/blogs/kernelbench",
-    "github_topic_closest_fit": "benchmark",
-    "contributors_all": 21,
-    "contributors_2025": 16,
-    "contributors_2024": 3,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 6
+    "homepage_link": "https://scalingintelligence.stanford.edu/blogs/kernelbench"
   },
   {
     "repo_name": "nvshmem",
     "repo_link": "https://github.com/NVIDIA/nvshmem",
     "category": "distributed computing",
     "github_about_section": "NVIDIA NVSHMEM is a parallel programming interface for NVIDIA GPUs based on OpenSHMEM. NVSHMEM can significantly reduce multi-process communication and coordination overheads by allowing programmers to perform one-sided communication from within CUDA kernels and on CUDA streams.",
-    "homepage_link": "https://docs.nvidia.com/nvshmem/api/index.html",
-    "contributors_all": 20,
-    "contributors_2026_q1": 10,
-    "contributors_2025": 19,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://docs.nvidia.com/nvshmem/api/index.html"
   },
   {
     "repo_name": "flashinfer-bench",
     "repo_link": "https://github.com/flashinfer-ai/flashinfer-bench",
     "category": "benchmark",
     "github_about_section": "Building the Virtuous Cycle for AI-driven LLM Systems",
-    "homepage_link": "https://bench.flashinfer.ai",
-    "github_topic_closest_fit": "benchmark",
-    "contributors_all": 18,
-    "contributors_2025": 11,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 9
+    "homepage_link": "https://bench.flashinfer.ai"
   },
   {
     "repo_name": "Primus-Turbo",
     "repo_link": "https://github.com/AMD-AGI/Primus-Turbo",
     "category": "training framework",
-    "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.",
-    "contributors_all": 17,
-    "contributors_2025": 12,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 6
+    "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."
   },
   {
     "repo_name": "BitBLAS",
     "repo_link": "https://github.com/microsoft/BitBLAS",
     "category": "Basic Linear Algebra Subprograms (BLAS)",
-    "github_about_section": "BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.",
-    "github_topic_closest_fit": "matrix-multiplication",
-    "contributors_all": 17,
-    "contributors_2025": 5,
-    "contributors_2024": 14,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 0
+    "github_about_section": "BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment."
   },
   {
     "repo_name": "Wan2.2",
     "repo_link": "https://github.com/Wan-Video/Wan2.2",
     "category": "video generation",
     "github_about_section": "Wan: Open and Advanced Large-Scale Video Generative Models",
-    "homepage_link": "https://wan.video",
-    "github_topic_closest_fit": "diffusion-models",
-    "contributors_all": 16,
-    "contributors_2025": 14,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 3
+    "homepage_link": "https://wan.video"
   },
   {
     "repo_name": "kernels-community",
     "repo_link": "https://github.com/huggingface/kernels-community",
     "category": "gpu kernels",
     "homepage_link": "https://huggingface.co/kernels-community",
-    "github_about_section": "Kernel sources for https://huggingface.co/kernels-community",
-    "contributors_all": 16,
-    "contributors_2025": 9,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 11
+    "github_about_section": "Kernel sources for https://huggingface.co/kernels-community"
   },
   {
     "repo_name": "omnitrace",
     "repo_link": "https://github.com/ROCm/omnitrace",
     "category": "performance testing",
     "github_about_section": "Omnitrace: Application Profiling, Tracing, and Analysis",
-    "homepage_link": "https://rocm.docs.amd.com/projects/omnitrace",
-    "github_topic_closest_fit": "profiling",
-    "contributors_all": 16,
-    "contributors_2025": 2,
-    "contributors_2024": 12,
-    "contributors_2023": 2,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://rocm.docs.amd.com/projects/omnitrace"
   },
   {
     "repo_name": "synthetic-data-kit",
     "repo_link": "https://github.com/meta-llama/synthetic-data-kit",
     "category": "synthetic data generation",
     "github_about_section": "Tool for generating high quality Synthetic datasets",
-    "homepage_link": "https://pypi.org/project/synthetic-data-kit",
-    "github_topic_closest_fit": "synthetic-dataset-generation",
-    "contributors_all": 15,
-    "contributors_2025": 15,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://pypi.org/project/synthetic-data-kit"
   },
   {
     "repo_name": "cudnn-frontend",
     "repo_link": "https://github.com/NVIDIA/cudnn-frontend",
     "category": "parallel computing",
     "github_about_section": "cudnn_frontend provides a c++ wrapper for the cudnn backend API and samples on how to use it",
-    "homepage_link": "https://developer.nvidia.com/cudnn",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 15,
-    "contributors_2025": 6,
-    "contributors_2024": 5,
-    "contributors_2023": 1,
-    "contributors_2026_q1": 3
+    "homepage_link": "https://developer.nvidia.com/cudnn"
   },
   {
     "repo_name": "PipelineRL",
     "repo_link": "https://github.com/ServiceNow/PipelineRL",
     "category": "reinforcement learning",
     "github_about_section": "A scalable asynchronous reinforcement learning implementation with in-flight weight updates.",
-    "homepage_link": "https://arxiv.org/abs/2509.19128",
-    "contributors_all": 15,
-    "contributors_2026_q1": 4,
-    "contributors_2025": 13,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://arxiv.org/abs/2509.19128"
   },
   {
     "repo_name": "cosmos-predict2.5",
     "repo_link": "https://github.com/nvidia-cosmos/cosmos-predict2.5",
     "category": "world model",
     "github_about_section": "Cosmos-Predict2.5, the latest version of the Cosmos World Foundation Models (WFMs) family, specialized for simulating and predicting the future state of the world in the form of video.",
-    "homepage_link": "https://research.nvidia.com/labs/cosmos-lab/cosmos-predict2.5",
-    "contributors_all": 14,
-    "contributors_2026_q1": 7,
-    "contributors_2025": 9,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "homepage_link": "https://research.nvidia.com/labs/cosmos-lab/cosmos-predict2.5"
   },
   {
     "repo_name": "kraken",
     "repo_link": "https://github.com/meta-pytorch/kraken",
     "category": "kernel examples",
-    "github_about_section": "Triton-based Symmetric Memory operators and examples",
-    "contributors_all": 11,
-    "contributors_2026_q1": 1,
-    "contributors_2025": 11,
-    "contributors_2024": 0,
-    "contributors_2023": 0
+    "github_about_section": "Triton-based Symmetric Memory operators and examples"
   },
   {
     "repo_name": "TileIR",
     "repo_link": "https://github.com/microsoft/TileIR",
     "category": "parallel computing dsl",
-    "github_about_section": "TileIR (tile-ir) is a concise domain-specific IR designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, TileIR allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance.",
-    "github_topic_closest_fit": "parallel-programming",
-    "contributors_all": 10,
-    "contributors_2025": 10,
-    "contributors_2024": 1,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 0
+    "github_about_section": "TileIR (tile-ir) is a concise domain-specific IR designed to streamline the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, TileIR allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance."
   },
   {
     "repo_name": "intelliperf",
     "repo_link": "https://github.com/AMDResearch/intelliperf",
     "category": "performance testing",
     "github_about_section": "Automated bottleneck detection and solution orchestration",
-    "homepage_link": "https://arxiv.org/html/2508.20258v1",
-    "github_topic_closest_fit": "profiling",
-    "contributors_all": 7,
-    "contributors_2025": 7,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 2
+    "homepage_link": "https://arxiv.org/html/2508.20258v1"
   },
   {
     "repo_name": "streamv2v",
     "repo_link": "https://github.com/Jeff-LiangF/streamv2v",
     "category": "video generation",
     "github_about_section": "Official Pytorch implementation of StreamV2V.",
-    "homepage_link": "https://jeff-liangf.github.io/projects/streamv2v",
-    "github_topic_closest_fit": "diffusion-models",
-    "contributors_all": 7,
-    "contributors_2025": 3,
-    "contributors_2024": 6,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://jeff-liangf.github.io/projects/streamv2v"
   },
   {
     "repo_name": "tilus",
     "repo_link": "https://github.com/NVIDIA/tilus",
     "category": "parallel computing",
     "github_about_section": "Tilus is a tile-level kernel programming language with explicit control over shared memory and registers.",
-    "homepage_link": "https://nvidia.github.io/tilus",
-    "contributors_all": 7,
-    "contributors_2025": 4,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 3
+    "homepage_link": "https://nvidia.github.io/tilus"
   },
   {
     "repo_name": "gemlite",
     "repo_link": "https://github.com/dropbox/gemlite",
     "category": "gpu kernels",
-    "github_about_section": "Fast low-bit matmul kernels in Triton",
-    "contributors_all": 5,
-    "contributors_2025": 1,
-    "contributors_2024": 5,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 1
+    "github_about_section": "Fast low-bit matmul kernels in Triton"
   },
   {
     "repo_name": "Self-Forcing",
     "repo_link": "https://github.com/guandeh17/Self-Forcing",
     "category": "video generation",
     "github_about_section": "Official codebase for \"Self Forcing: Bridging Training and Inference in Autoregressive Video Diffusion\" (NeurIPS 2025 Spotlight)",
-    "homepage_link": "https://self-forcing.github.io",
-    "github_topic_closest_fit": "diffusion-models",
-    "contributors_all": 4,
-    "contributors_2025": 4,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://self-forcing.github.io"
   },
   {
     "repo_name": "TritonBench",
     "repo_link": "https://github.com/thunlp/TritonBench",
     "category": "benchmark",
     "github_about_section": "TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators",
-    "homepage_link": "https://arxiv.org/abs/2502.14752",
-    "github_topic_closest_fit": "benchmark",
-    "contributors_all": 3,
-    "contributors_2025": 3,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://arxiv.org/abs/2502.14752"
   },
   {
     "repo_name": "IMO2025",
     "repo_link": "https://github.com/harmonic-ai/IMO2025",
     "category": "formal mathematical reasoning",
     "github_about_section": "Harmonic's model Aristotle achieved gold medal performance, solving 5 problems. This repository contains the lean statement files and proofs for Problems 1-5.",
-    "homepage_link": "https://harmonic.fun",
-    "github_topic_closest_fit": "lean",
-    "contributors_all": 2,
-    "contributors_2025": 2,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 0
+    "homepage_link": "https://harmonic.fun"
   },
   {
     "repo_name": "RaBitQ",
     "repo_link": "https://github.com/gaoj0017/RaBitQ",
     "category": "quantization",
     "github_about_section": "[SIGMOD 2024] RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search",
-    "homepage_link": "https://github.com/VectorDB-NTU/RaBitQ-Library",
-    "github_topic_closest_fit": "nearest-neighbor-search",
-    "contributors_all": 2,
-    "contributors_2025": 2,
-    "contributors_2024": 1,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 1
+    "homepage_link": "https://github.com/VectorDB-NTU/RaBitQ-Library"
   },
   {
     "repo_name": "torchdendrite",
     "repo_link": "https://github.com/sandialabs/torchdendrite",
     "category": "machine learning framework",
-    "github_about_section": "Dendrites for PyTorch and SNNTorch neural networks",
-    "contributors_all": 2,
-    "contributors_2025": 1,
-    "contributors_2024": 1,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 0
+    "github_about_section": "Dendrites for PyTorch and SNNTorch neural networks"
   },
   {
     "repo_name": "triton-runner",
     "repo_link": "https://github.com/toyaix/triton-runner",
     "category": "debugger",
     "github_about_section": "Multi-Level Triton Runner supporting Python, IR, PTX, and cubin.",
-    "homepage_link": "https://triton-runner.org",
-    "contributors_all": 2,
-    "contributors_2025": 1,
-    "contributors_2024": 0,
-    "contributors_2023": 0,
-    "contributors_2026_q1": 2
+    "homepage_link": "https://triton-runner.org"
   }
 ]

README.md

@@ -5,6 +5,3 @@ pretty_name: PyTorch Conference 2025 GitHub Repos
 ---
 # PyTorch Conference 2025 GitHub Repos
 I created a list of every GitHub repo mentioned during PyTorch Conference 2025 and Open Source AI Week.  
-
-# Usage
-Script used to update unique contributor count: https://github.com/Tyler-Hilbert/Update_GitHub_Dataset_PyTorchConference2025