datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
cuda-triton-gpu-kernels-2026
⚡ Complete 2026 CUDA & OpenAI Triton High-Performance GPU Kernel Engineering SFT/DPO Suite
The definitive, production-grade synthetic alignment dataset engineered for training and fine-tuning open-weights Large Language Models (Qwen 2.5 Coder, DeepSeek-Coder, Llama 3.1) on ultra-high-throughput GPU kernel programming: NVIDIA Hopper H100 / Blackwell B200 TMA async transfers, OpenAI Triton 3.1+ FlashAttention-3, 32-bank conflict elimination, and low-bit FP8 / INT4 GEMM… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/cuda-triton-gpu-kernels-2026.qwen38-27b-triton-ascend-rl-trajectories
Qwen3.8-27B Triton-Ascend RL Trajectories
This dataset contains 1,000 multi-turn trajectories for Triton-Ascend kernel generation. Every included trajectory passed compilation and correctness validation on one official npu-kernelbench workload. Qwen3.8-27B generated an initial solution and received evaluator feedback for up to five calls.
Dataset Viewer subsets
trajectories (default): one row per sample with only messages. The initial system and task user… See the full description on the dataset page: https://huggingface.co/datasets/Yukki1011/qwen38-27b-triton-ascend-rl-trajectories.Code_Opt_Triton
Overview
This dataset, TEEN-D/Code_Opt_Triton, is an extended version of the publicly available GPUMODE/Inductor_Created_Data_Permissive dataset. It contains pairs of original (PyTorch or Triton) programs and their equivalent Triton code (generated by torch inductor), intended for training models in PyTorch-to-Triton code translation and optimization.
The primary modification in this extended version is that each optimized Triton code snippet is paired with both its original source… See the full description on the dataset page: https://huggingface.co/datasets/Teen-Different/Code_Opt_Triton.kernelbook-triton-reasoning-traces
KernelBench Triton Reasoning Traces
Reasoning traces generated by the gpt-oss-120b model for converting PyTorch modules to Triton GPU kernels.
Dataset Description
This dataset contains 170 reasoning traces around 85% of them are correct where a PyTorch module was successfully converted to a Triton kernel. Each sample includes the original PyTorch code, the model's reasoning process, and the resulting Triton kernel code along with correctness and performance benchmarks.… See the full description on the dataset page: https://huggingface.co/datasets/ppbhatt500/kernelbook-triton-reasoning-traces.autonomous-gpu-kernel-triton-cuda-suite-2026
⚡ Autonomous GPU Kernel, Triton & CUDA Architecture Suite (2026)
A Production-Grade, Verifiable Synthetic Corpus for Training Frontier Coding Models (Qwen 3.8, DeepSeek-V3, Llama 3.3)
⚡ Overview & Industry Problem
Modern deep learning accelerators, custom ASICs, and high-performance computing clusters demand specialized, autonomous GPU kernel infrastructure: OpenAI Triton fused kernels, FlashAttention-3 forward/backward online softmax… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/autonomous-gpu-kernel-triton-cuda-suite-2026.