datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.kernelbook-glm4-evalskernelbook-kimi_k2_thinking-evals-synthetic-promptskernelbook-kimi_k2_thinking-evalskernelbook-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-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-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.KernelBook-messageskernelbook-synthetic-taskskernelbook-glm4_7-evalskernelbook-kimi_k2_thinking-evals-filteredkernelbook-kimi_k2_thinking-evals-uniquekernel-book-z-ai-glm-4.6-traceskernelbook-kimi_k2_thinking-evals-filtered-mergedkernelbook-glm4-evals-filteredkernelbook-kimi_k2_thinking-synthetic-tasksKernelBook-varied-input-v4kernelbook-kimi_k2_thinking-evals-unique-synthetic-promptsKernelBook-conversationalKernelBook-QAkernelbook-glm4-evals-unique-no-reasoningkernelbook-kimi_k2_thinking-evals-filtered-synthetic-promptsKernelBook-varied-input-v2kernelbook-glm4-evals-uniqueKernelBook
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:
Repository… See the full description on the dataset page: https://huggingface.co/datasets/yiyz/KernelBook.KernelBook-varied-input-v2-conversationalKernelBook-varied-input-v4-conversationalkernelbook-glm4_7-evals-filteredkernelbook-verifiedKernelBook-conversational-test