https://www.youtube.com/watch?v=wDNfcv_fbWs&list=PL_lsbAsL_o2BUUxo6coMBFwQE31U4Eb2q&index=11
f036615 | [ | |
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| "github_about_section": "An Open Source Machine Learning Framework for Everyone", | |
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| "repo_name": "transformers", | |
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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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| "repo_link": "https://github.com/facebook/hhvm", | |
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| "github_about_section": "A high-throughput and memory-efficient inference and serving engine for LLMs", | |
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| "category": "inference engine", | |
| "github_about_section": "LLM inference in C/C++", | |
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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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| "repo_link": "https://github.com/sgl-project/sglang", | |
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| "category": "machine learning interoperability", | |
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| "github_about_section": "The Modular Platform (includes MAX & Mojo)", | |
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| "repo_name": "accelerate", | |
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| "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" | |
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| "github_about_section": "CUDA Templates and Python DSLs for High-Performance Linear Algebra", | |
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| "github_about_section": "PyTorch native quantization for training and inference", | |
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| "repo_link": "https://github.com/kvcache-ai/Mooncake", | |
| "category": "inference", | |
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| "github_about_section": "A PyTorch native platform for training generative AI models", | |
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| "category": "inference", | |
| "github_about_section": "Supercharge Your LLM with the Fastest KV Cache Layer", | |
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| "category": "multi-purpose library", | |
| "github_about_section": "AMD ROCm Software - GitHub Home", | |
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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.", | |
| "homepage_link": "https://docs.letta.com" | |
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| { | |
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| "repo_link": "https://github.com/triton-inference-server/server", | |
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| "github_about_section": "The Triton Inference Server provides an optimized cloud and edge inferencing solution.", | |
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| "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" | |
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| "github_about_section": "Efficient Triton Kernels for LLM Training", | |
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| { | |
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| "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" | |
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| { | |
| "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" | |
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| { | |
| "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/" | |
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| { | |
| "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" | |
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| { | |
| "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" | |
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| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
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| { | |
| "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" | |
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| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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/" | |
| }, | |
| { | |
| "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." | |
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| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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/" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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/" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "repo_name": "torchchat", | |
| "repo_link": "https://github.com/pytorch/torchchat", | |
| "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" | |
| }, | |
| { | |
| "repo_name": "prime-rl", | |
| "repo_link": "https://github.com/PrimeIntellect-ai/prime-rl", | |
| "category": "reinforcement learning", | |
| "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" | |
| }, | |
| { | |
| "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/" | |
| }, | |
| { | |
| "repo_name": "openevolve", | |
| "repo_link": "https://github.com/codelion/openevolve", | |
| "category": "evolutionary algorithm", | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "repo_name": "quack", | |
| "repo_link": "https://github.com/Dao-AILab/quack", | |
| "category": "kernel examples", | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "repo_name": "kernels", | |
| "repo_link": "https://github.com/huggingface/kernels", | |
| "category": "gpu kernels", | |
| "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." | |
| }, | |
| { | |
| "repo_name": "ome", | |
| "repo_link": "https://github.com/sgl-project/ome", | |
| "category": "container orchestration", | |
| "github_about_section": "Open Model Engine (OME) — Kubernetes operator for LLM serving, GPU scheduling, and model lifecycle management.", | |
| "homepage_link": "https://ome-projects.github.io/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/" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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." | |
| }, | |
| { | |
| "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." | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "repo_name": "kraken", | |
| "repo_link": "https://github.com/meta-pytorch/kraken", | |
| "category": "kernel examples", | |
| "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." | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "repo_name": "gemlite", | |
| "repo_link": "https://github.com/dropbox/gemlite", | |
| "category": "gpu kernels", | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "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" | |
| }, | |
| { | |
| "repo_name": "torchdendrite", | |
| "repo_link": "https://github.com/sandialabs/torchdendrite", | |
| "category": "machine learning framework", | |
| "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" | |
| }, | |
| { | |
| "repo_name": "distributed-training-guide", | |
| "repo_link": "https://github.com/LambdaLabsML/distributed-training-guide", | |
| "github_about_section": "Best practices & guides on how to write distributed pytorch training code" | |
| }, | |
| { | |
| "repo_name": "Megatron-LM", | |
| "repo_link": "https://github.com/NVIDIA/Megatron-LM", | |
| "github_about_section": "Ongoing research training transformer models at scale", | |
| "homepage_link": "https://docs.nvidia.com/megatron-core/developer-guide/latest/index.html" | |
| }, | |
| { | |
| "repo_name": "fairscale", | |
| "repo_link": "https://github.com/facebookresearch/fairscale", | |
| "github_about_section": "PyTorch extensions for high performance and large scale training." | |
| }, | |
| { | |
| "repo_name": "ColossalAI", | |
| "repo_link": "https://github.com/hpcaitech/ColossalAI", | |
| "github_about_section": "Making large AI models cheaper, faster and more accessible", | |
| "homepage_link": "https://colossalai.org/" | |
| }, | |
| { | |
| "repo_name": "NeMo RL", | |
| "repo_link": "https://github.com/nvidia-nemo/rl", | |
| "github_about_section": "Scalable toolkit for efficient model reinforcement", | |
| "homepage_link": "https://docs.nvidia.com/nemo/rl/latest/index.html" | |
| }, | |
| { | |
| "repo_name": "slime", | |
| "repo_link": "https://github.com/THUDM/slime", | |
| "github_about_section": "slime is an LLM post-training framework for RL Scaling.", | |
| "homepage_link": "https://thudm.github.io/slime/" | |
| }, | |
| { | |
| "repo_name": "RAGEN", | |
| "repo_link": "https://github.com/mll-lab-nu/RAGEN", | |
| "github_about_section": "RAGEN leverages reinforcement learning to train LLM reasoning agents in interactive, stochastic environments.", | |
| "homepage_link": "https://ragen-ai.github.io/" | |
| }, | |
| { | |
| "repo_name": "slurm", | |
| "repo_link": "https://github.com/SchedMD/slurm", | |
| "github_about_section": "Slurm: A Highly Scalable Workload Manager", | |
| "homepage_link": "https://slurm.schedmd.com/" | |
| }, | |
| { | |
| "repo_name": "Spurious Rewards", | |
| "repo_link": "https://github.com/ruixin31/Spurious_Rewards", | |
| "github_about_section": "Spurious Rewards: Rethinking Training Signals in RLVR", | |
| "homepage_link": "https://arxiv.org/pdf/2506.10947" | |
| }, | |
| { | |
| "repo_name": "Qwen Code", | |
| "repo_link": "https://github.com/QwenLM/qwen-code", | |
| "github_about_section": "An open-source AI coding agent that lives in your terminal.", | |
| "homepage_link": "https://qwen.ai/qwencode" | |
| }, | |
| { | |
| "repo_name": "Open Thoughts", | |
| "repo_link": "https://github.com/open-thoughts/open-thoughts", | |
| "github_about_section": "Fully open data curation for reasoning models", | |
| "homepage_link": "https://www.open-thoughts.ai/" | |
| }, | |
| { | |
| "repo_name": "OLMOS", | |
| "repo_link": "https://github.com/allenai/olmes", | |
| "github_about_section": "Reproducible, flexible LLM evaluations" | |
| }, | |
| { | |
| "repo_name": "SmolLM", | |
| "repo_link": "https://github.com/huggingface/smollm", | |
| "github_about_section": "Everything about the SmolLM and SmolVLM family of models" | |
| }, | |
| { | |
| "repo_name": "smolagents", | |
| "repo_link": "https://github.com/huggingface/smolagents", | |
| "github_about_section": "smolagents: a barebones library for agents that think in code.", | |
| "homepage_link": "https://huggingface.co/docs/smolagents/" | |
| }, | |
| { | |
| "repo_name": "Delta Learning", | |
| "repo_link": "https://github.com/scottgeng00/delta_learning", | |
| "github_about_section": "Code release for the paper \"The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains\"", | |
| "homepage_link": "https://arxiv.org/pdf/2507.06187" | |
| }, | |
| { | |
| "repo_name": "DeepSeek-V3", | |
| "repo_link": "https://github.com/deepseek-ai/DeepSeek-V3", | |
| "homepage_link": "https://www.deepseek.com" | |
| }, | |
| { | |
| "repo_name": "Optimum", | |
| "repo_link": "https://github.com/huggingface/optimum", | |
| "github_about_section": "Accelerate inference and training of Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools", | |
| "homepage_link": "https://huggingface.co/docs/optimum/main/en/index" | |
| }, | |
| { | |
| "repo_name": "ultralytics", | |
| "repo_link": "https://github.com/ultralytics/ultralytics", | |
| "github_about_section": "Ultralytics YOLO26, YOLO11, YOLOv8 — object detection, instance segmentation, semantic segmentation, image classification, pose estimation, object tracking", | |
| "homepage_link": "https://platform.ultralytics.com" | |
| }, | |
| { | |
| "repo_name": "NVIDIA FLARE", | |
| "repo_link": "https://github.com/nvidia/nvflare", | |
| "github_about_section": "NVIDIA Federated Learning Application Runtime Environment", | |
| "homepage_link": "https://nvidia.github.io/NVFlare/" | |
| }, | |
| { | |
| "repo_name": "pip", | |
| "repo_link": "https://github.com/pypa/pip", | |
| "github_about_section": "The Python package installer", | |
| "homepage_link": "https://pip.pypa.io/" | |
| }, | |
| { | |
| "repo_name": "Maven", | |
| "repo_link": "https://github.com/apache/maven", | |
| "github_about_section": "Apache Maven is a software project management and comprehension tool. Based on the concept of a project object model (POM), Maven can manage a project's build, reporting and documentation from a central piece of information.", | |
| "homepage_link": "https://maven.apache.org" | |
| }, | |
| { | |
| "repo_name": "SpecForge", | |
| "repo_link": "https://github.com/sgl-project/SpecForge", | |
| "github_about_section": "Train speculative decoding models effortlessly and port them smoothly to SGLang serving.", | |
| "homepage_link": "https://sgl-project.github.io/SpecForge/" | |
| } | |
| ] |