datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
CUDA-L2
CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning
🥳 Introduction
CUDA-L2 is a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels. CUDA-L2 systematically outperforms major matmul baselines to date, from the widely-used torch.matmul to state-of-the-art NVIDIA closed-source libraries (cuBLAS… See the full description on the dataset page: https://huggingface.co/datasets/ornith-ai/CUDA-L2.ga104-cuda-kernels
GA104 Hand-Optimized CUDA Kernel Corpus
A measurement corpus of hand-optimized CUDA / SASS kernels targeting the
RTX 3070 Ti (GA104, sm_86, Ampere). Every kernel is written without
cuBLAS, cuDNN, or PyTorch in the optimized path; vendor libraries are
linked only for measured comparison under kernels/reference/. This
dataset is for SASS and GPU-optimization researchers — it pairs each
.cu source with its compiled machine code and its disassembly, so the
exact instruction stream a… See the full description on the dataset page: https://huggingface.co/datasets/pjt222/ga104-cuda-kernels.Nemotron-SFT-CUDA-v1-prompt-only
Nemotron-SFT-CUDA-v1-prompt-only
Prompt-only extraction from nvidia/Nemotron-SFT-CUDA-v1.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.
null_or_empty_rows.md: row indexes where prompt extraction produced a… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-SFT-CUDA-v1-prompt-only.