datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
KernelBench
KernelBench
A benchmark designed to evaluate the ability of LLMs to generate efficient GPU kernels for optimizing neural network performance
Version
[07-21-2025] This HF dataset version has been updated to v0.1
Citation
@misc{ouyang2024kernelbench,
title={KernelBench: Can LLMs Write GPU Kernels?},
author={Anne Ouyang and Simon Guo and Azalia Mirhoseini},
year={2024},
url={https://scalingintelligence.stanford.edu/blogs/kernelbench/},
}
QwQ-32B_enable-liger-kernel_False_OpenThoughts3_3k_eval_5554
mlfoundations-dev/QwQ-32B_enable-liger-kernel_False_OpenThoughts3_3k_eval_5554
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
MMLUPro
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
AIME25
HLE
LiveCodeBenchv5
HMMT
Accuracy
75.7
98.8
90.4
58.1
73.7
68.2
41.9
46.8
47.2
67.7
13.9
64.3
52.0
AIME24
Average Accuracy: 75.67% ± 1.57%
Number of Runs: 10
Run
Accuracy
Questions… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/QwQ-32B_enable-liger-kernel_False_OpenThoughts3_3k_eval_5554.Kernel-Smith-SFT-71KIf this work is useful to you, please cite:
@article{DBLP:journals/corr/abs-2603-28342,
author = {He Du and
Qiming Ge and
Jiakai Hu and
Aijun Yang and
Zheng Cai and
Zixian Huang and
Sheng Yuan and
Qinxiu Cheng and
Xinchen Xie and
Yicheng Chen and
Yining Li and
Jiaxing Xie and… See the full description on the dataset page: https://huggingface.co/datasets/CoopReason/Kernel-Smith-SFT-71K.KernelBook
Overview
dataset_permissive{.json/.parquet} is a curated collection of pairs of pytorch programs and equivalent triton code (generated by torch inductor) which can be used to train models to translate pytorch code to triton code.
The triton code was generated using PyTorch 2.5.0 so for best results during evaluation / running the triton code we recommend using that version of pytorch.
Dataset Creation
The dataset was created through the following process:… See the full description on the dataset page: https://huggingface.co/datasets/GPUMODE/KernelBook.KernelLLM-2
KernelLLM-2 Dataset
A high-quality raw dataset for training and fine-tuning LLMs on Operating System Development, significantly expanded for the second version.
Overview
This dataset combines raw source code from a variety of mature and hobbyist operating system kernels with thousands of expert-level technical discussions and critiques from the Linux Kernel Mailing List, plus unique Git logic-diff traces.
[!NOTE]
The source code included in this dataset represents the… See the full description on the dataset page: https://huggingface.co/datasets/frisk2137/KernelLLM-2.kernel-vuln-dataset-full
Linux Kernel Vulnerability-Introducing Commits Dataset
Dataset Description
A labeled dataset of 1,426,202 Linux kernel git commits with full metadata, diffs, and binary labels indicating whether each commit introduced a vulnerability that was later fixed.
Intended use: Training and evaluating models for vulnerability-introducing commit detection — predicting whether a given code change will later require a security or bug fix.
How the Data Was Collected… See the full description on the dataset page: https://huggingface.co/datasets/pebblebed/kernel-vuln-dataset-full.kernel-vuln-dataset-full
Linux Kernel Vulnerability-Introducing Commits Dataset
Dataset Description
A labeled dataset of 1,426,202 Linux kernel git commits with full metadata, diffs, and binary labels indicating whether each commit introduced a vulnerability that was later fixed.
Intended use: Training and evaluating models for vulnerability-introducing commit detection — predicting whether a given code change will later require a security or bug fix.
How the Data Was Collected… See the full description on the dataset page: https://huggingface.co/datasets/quguanni/kernel-vuln-dataset-full.KernelBenchX
KernelBenchX
Reproducible evaluation benchmark for Triton GPU-kernel code generation by LLMs — measures buildability, numerical correctness against a deterministic test suite, and end-to-end speedup vs. a GPU-matched golden reference.
Paper: arXiv:2605.04956 · hf.co/papers/2605.04956
Evaluation harness: https://github.com/BonnieW05/KernelBenchX
Configs
Config
Rows
What it is
tasks
176
Benchmark task specs + PyTorch reference + deterministic test harness… See the full description on the dataset page: https://huggingface.co/datasets/BonnieWang/KernelBenchX.QwQ-32B_enable-liger-kernel_False_OpenThoughts3_1k_eval_5554KernelLLM-1
KernelLLM-1 Dataset
A high-quality raw dataset for training and fine-tuning LLMs on Operating System Development.
Overview
This dataset combines raw source code from a variety of mature and hobbyist operating system kernels with thousands of expert-level technical discussions and critiques from the Linux Kernel Mailing List.
[!NOTE]
The source code included in this dataset represents the latest stable versions of the respective repositories as of February 2nd 2026.… See the full description on the dataset page: https://huggingface.co/datasets/frisk2137/KernelLLM-1.kernel_synth_annotated
KernelSynth (annotated)
One million synthetic univariate time series, each 1024 points long, drawn from a Gaussian
process prior whose kernel is a random composition of up to five base kernels. This is the
KernelSynth procedure from Chronos with one addition:
the generating kernel is kept alongside each series. The ground-truth structure behind
every series is therefore known, which makes the corpus usable for interpretability work
rather than only for pretraining.… See the full description on the dataset page: https://huggingface.co/datasets/felixdivo/kernel_synth_annotated.daVinci-kernel-sftdr-kernel-RLlinux-kernel-bugfixes-diffs
🐧 Linux Kernel Bugfixes & Patches Dataset (Instruction-Tuned)
📖 Dataset Description
This dataset is a highly curated, instruction-tuned collection of problem-solution pairs extracted directly from the official Linux Kernel Git repository (torvalds/linux). It is specifically designed to train Large Language Models (LLMs) on low-level C programming, kernel architecture, memory management, and security vulnerability patching.
Unlike raw commit histories, this… See the full description on the dataset page: https://huggingface.co/datasets/switlydev/linux-kernel-bugfixes-diffs.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.kernelbook-glm4-evalsinstruct2action
Dataset Card for "instruct2action"
More Information needed
ParallelKernelBench_Kernels
ParallelKernelBench Kernels
Net-new multi-GPU CUDA kernels generated by LLMs for ParallelKernelBench.
Each subdirectory under solutions/ is one model run. File names match the benchmark problem stems (e.g. 17_rope_allgather_cuda.py ↔ problem 17_rope_allgather in willychan21/ParallelKernelBench_Problems).
Layout
solutions/
<run_id>/
<stem>_cuda.py
...
Runs (1 run(s), 87 kernel files)
run_id
kernels
path… See the full description on the dataset page: https://huggingface.co/datasets/willychan21/ParallelKernelBench_Kernels.autonomous-linux-kernel-ebpf-xdp-suite
⚡ Autonomous Linux Kernel, eBPF & XDP Programmable Dataplane Suite (2026)
A Production-Grade, Verifiable Synthetic Corpus for Training Autonomous Linux Kernel & eBPF Systems Agents
⚡ Overview & Industry Problem
Modern hyperscale cloud datacenters, bare-metal Kubernetes clusters, and low-latency financial trading nodes rely on in-kernel programmable dataplanes: eBPF, AF_XDP zero-copy rings, Traffic Control (TC) shapers, BPF LSM security hooks… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/autonomous-linux-kernel-ebpf-xdp-suite.deepseek-r1-systems-kernel-reasoning
🧠 DeepSeek-R1 Low-Level Systems & Kernel Reasoning Suite (2026)
🛒 Commercial Full Suite Available:
The full production suite with 10,000 SFT Hardware Reasoning Traces + 2,500 High-Contrast DPO Alignment Pairs across all 20 domains is available on Gumroad:
👉 Download Full Commercial Dataset on Gumroad (Starter \ / Pro \ / Enterprise )
A Tier-1 Commercial Dataset Suite engineered specifically for fine-tuning DeepSeek-R1, DeepSeek-R1-Distill-Qwen-14B/32B, and frontier… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/deepseek-r1-systems-kernel-reasoning.android-kernel-security-datasetkernelbook-opus4.8-multiturn-traces
KernelBook → Triton: Multi-Turn Generation Traces (Opus 4.8)
Multi-turn agentic traces of Claude Opus 4.8 converting PyTorch modules into
Triton GPU kernels. Each row is one problem from
GPUMODE/KernelBook: the model
writes a kernel, runs it on a GPU against the reference, reads the
correctness + speedup feedback, and iterates — so every trace is a grounded,
tool-using optimization loop, not a single-shot completion.
How it was generated
Model: claude-opus-4-8… See the full description on the dataset page: https://huggingface.co/datasets/ppbhatt500/kernelbook-opus4.8-multiturn-traces.kernelbook-kimi_k2_thinking-evals-synthetic-promptskernelbook-kimi_k2_thinking-evalsiclr_kernel_steering_self_awareness_general_llama3.2-1B-it_layer10_activationskernelbook-triton-multiturn-reasoning-traces
KernelBench Triton Multi-Turn Reasoning Traces
A dataset of multi-turn reasoning traces for Triton GPU kernel generation from PyTorch reference implementations. Each trace captures the full iterative refinement loop — model reasoning, generated kernel code, execution feedback, and benchmark results.
Generation Setup
Model & Serving
Problems were sent to Qwen3-235B-A22B-Thinking-2507 (FP8) served via vLLM on H100 GPUs (tensor parallel, 131k context window). Reasoning… See the full description on the dataset page: https://huggingface.co/datasets/ppbhatt500/kernelbook-triton-multiturn-reasoning-traces.KernelBook-messageskernelbook-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.KernelBench
Dataset Card for Dataset Name
This is the copy from Stanford's KernelBench (https://huggingface.co/datasets/ScalingIntelligence/KernelBench).
Dataset Details
Level 1: 100 Problems
Level 2: 100 Problems
Level 3: 50 Problems
Level 4: 20 Problems
Plan:
We want to try and tackle the dataset as well at MBZUAI / Imperial College London.
kernelbook-glm4_7-evals