datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
llama-cpp-wheelsIf you like this please consider liking and donating (https://buymeacoffee.com/aiencoder)
π llama-cpp-python Mega-Factory Wheels
"Stop waiting for pip to compile. Just install and run."
The most complete collection of pre-built llama-cpp-python wheels in existence β 8,333 wheels across every platform, Python version, backend, and CPU optimization level.
No more cmake, gcc, or compilation hell. No more waiting 10 minutes for a build that might fail. Just find your wheel and⦠See the full description on the dataset page: https://huggingface.co/datasets/AIencoder/llama-cpp-wheels.qwen-cpp-agent-0-protocolExperiment in agentic autonomy protocols.
~ everything in this repo was created by Qwen 3.8 27B (Q4) running autonomously inside Deepseek Harness, on a single RTX 3090 GPU, for 3 weeks.
The only human artifacts are:
agents/*
human/*
AGENTS.md
qwentts-cpp-python-wheels
qwentts-cpp-python wheels
Optional backend-specific wheel variants for qwentts-cpp-python.
The public PyPI package provides Linux CUDA 12.8 and macOS Metal wheels:
python -m pip install --upgrade qwentts-cpp-python
Install a backend-specific wheel from this repository with --find-links:
pip install "qwentts-cpp-python==0.5.0+cpu" -f https://huggingface.co/datasets/andito/qwentts-cpp-python-wheels/tree/main/whl/cpu
pip install "qwentts-cpp-python==0.5.0+cu124" -f⦠See the full description on the dataset page: https://huggingface.co/datasets/andito/qwentts-cpp-python-wheels.demucs.cppThis repo stores weights in ggml format that are used to perform music separation.
These are intended to be used with demucs.cpp, https://github.com/sevagh/demucs.cpp
Weights origin:
https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/955717e8-8726e21a.th
https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/5c90dfd2-34c22ccb.th
https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/f7e0c4bc-ba3fe64a.th
https://dl.fbaipublicfiles.com/demucs/hybrid_transformer/d12395a8-e57c48e6.th⦠See the full description on the dataset page: https://huggingface.co/datasets/Retrobear/demucs.cpp.cppe-5
Dataset Card for CPPE - 5
Dataset Summary
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization of medical personal protective equipments, which is not possible with other popular data sets that focus on broad level categories.
Some features of this dataset are:
high quality images and annotations (~4.6 bounding boxes per image)
real-life images unlike any current such dataset
majority⦠See the full description on the dataset page: https://huggingface.co/datasets/rishitdagli/cppe-5.llama.cpp_AlgMor24_github
Ξ©FFFΞ£LLIa β’ llama.cpp β’ AlgMor24
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High-Performance LLM / VLM Inference & Autonomous Agentic Ecosystem⦠See the full description on the dataset page: https://huggingface.co/datasets/Brunobkr/llama.cpp_AlgMor24_github.SWE-smith-cpphuman_eval_cppCpp-Code-LargeCpp-Code-Large
Cpp-Code-Large is a large-scale corpus of C++ source code comprising more than 5 million lines of C++ code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the C++ ecosystem.
By providing a high-volume, language-specific corpus, Cpp-Code-Large enables systematic experimentation in C++-focused model training, domain adaptation, and downstream code⦠See the full description on the dataset page: https://huggingface.co/datasets/ajibawa-2023/Cpp-Code-Large.the-stack-v2-new-cppcppe-5CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
to allow the study of subordinate categorization of medical personal protective equipments,
which is not possible with other popular data sets that focus on broad level categories.SWE-Bench-MultilingualC_CPPFileteredjepa.cpp-fixtures
jepa.cpp parity fixtures
PyTorch golden reference dumps for jepa.cpp, a ggml-based C/C++ inference engine for the
JEPA family. tests/test-parity and tests/test-predictor replay these tensors through the engine and
gate per-token cosine, pooled outputs and classifier top-1/top-5 against per-family thresholds.
Generated from jepa.cpp main @ 00bfd4e with
scripts/dump_reference.py --model all, in float32 eval mode on 32 CPU threads, no autocast.
Contents⦠See the full description on the dataset page: https://huggingface.co/datasets/jepacpp/jepa.cpp-fixtures.SWE-Bench-MultilingualC_CPPFiletered_newllama.cppversion https://git-lfs.github.com/spec/v1
oid sha256:cfc44b7ba25614df70e6b65e3341cae0310163bd32fd31a6b928a542df433faf
size 30786
smollm3-stack-v2-Cpp
Synthetic Pre-pretraining Datasets
This dataset contains the pre-processed synthetic pre-pretraining (PPT) and pre-training (PT) data used in the paper Synthetic Pre-pretraining Survives Scale, but Not as a Grammatical Prior.
It includes a range of PPT tasks (e.g., k-Shuffle Dyck, Set, MP-Struct Core, NCA) and standard pre-training mixtures (e.g., C4, SmolLM3, Olmo3, Marin) used to evaluate PPT at scale.
Code: GitHub repository
Project page: Hugging Face Organization
For a⦠See the full description on the dataset page: https://huggingface.co/datasets/verify-ppt/smollm3-stack-v2-Cpp.stack-v2-cpp-2019whisper.cpp
whisper.cpp
Stable: v1.8.1 / Roadmap
High-performance inference of OpenAI's Whisper automatic speech recognition (ASR) model:
Plain C/C++ implementation without dependencies
Apple Silicon first-class citizen - optimized via ARM NEON, Accelerate framework, Metal and Core ML
AVX intrinsics support for x86 architectures
VSX intrinsics support for POWER architectures
Mixed F16 / F32 precision
Integer quantization support
Zero memory allocations at runtime
Vulkan support
Support⦠See the full description on the dataset page: https://huggingface.co/datasets/echodict/whisper.cpp.LiveCodeBench-CPP
LiveCodeBench-CPP: An Extension of LiveCodeBench for Contamination Free Evaluation in C++
Overview
LiveCodeBench-CPP includes 454 problems from the release_v6 of LiveCodeBench, covering the period from October 2024 to May 2025. These problems are sourced from AtCoder (287 problems) and LeetCode (167 problems).
AtCoder Problems: These require generated solutions to read inputs from standard input (stdin) and write outputs to standard output (stdout). For unit testing, the⦠See the full description on the dataset page: https://huggingface.co/datasets/nvidia/LiveCodeBench-CPP.mmlu-redux-2.0-for-llama.cppMMLU-redux-v2.0 converted for the llama.cpp perplexity multiple choice tool.
Only valid entries where kept, there is no error based prompting included.
Dataset Card for MMLU-Redux-2.0
MMLU-Redux is a subset of 5,700 manually re-annotated questions across 57 MMLU subjects.
Citation
BibTeX:
@misc{gema2024mmlu,
title={Are We Done with MMLU?},
author={Aryo Pradipta Gema and Joshua Ong Jun Leang and Giwon Hong and Alessio Devoto and Alberto Carlo Maria⦠See the full description on the dataset page: https://huggingface.co/datasets/Green-Sky/mmlu-redux-2.0-for-llama.cpp.magenta-realtime-mlx-cpp
Magenta RealTime β C++ MLX runtime bundle
This dataset is a re-packaging of
Google's Magenta RealTime weights
for the C++ MLX runtime in
rhymeswithlion/magenta-realtime-mlx-cpp.
It contains exactly what mlx-stream needs at startup; nothing more, nothing
less. The upstream .pt / .npy checkpoints are intentionally not
mirrored here β they're only useful for the (Python) re-export tooling on the
project's main distribution.
Contents
.
ββββ¦ See the full description on the dataset page: https://huggingface.co/datasets/rhymeswithlion/magenta-realtime-mlx-cpp.the-stack-v2-cppvalidation-datasets-for-llama.cppThis repository contains validation datasets for use with the perplexity tool from the llama.cpp project.
Note: PR #5047 is required to be able to use these datasets.
The simple program in demo.cpp shows how to read these files and can be used to combine two files into one.
The simple program in convert.cpp shows how to convert the data to JSON. For instance:
g++ -o convert convert.cpp
./convert arc-easy-validation.bin arc-easy-validation.json
arc-stack-cppMD-trajectories-CPPF-tubulin-heterodimer-and-monomers
MD-trajectories-CPPF-tubulin-heterodimer-and-monomers
Copy this file into the Hugging Face dataset βREADMEβ (Dataset card).Source of truth in Git: https://github.com/jasperyeoh/integrative-ai-assisted-modeling-of-cppf-tubulin-interactions β see docs/DIMER_TRAJECTORY_NAMING.md.
What this dataset contains
All-atom GROMACS production trajectories (.xtc) for CPPF with human tubulin:
5IJ0 / soluble curved dimer (main text): three heterodimer replicates extended to⦠See the full description on the dataset page: https://huggingface.co/datasets/jasperyeoh2/MD-trajectories-CPPF-tubulin-heterodimer-and-monomers.TopoBox-3D
TopoBox-3D
Paper (arXiv:2609.05860) | Code (GitHub)
TopoBox-3D is the dataset accompanying Beyond Arbitrary Geometry: Topology
Generalization in Neural PDE Operators. It is a controlled three-dimensional
benchmark for separating fixed-topology geometry shift from generalization to
unseen homological support.
The benchmark contains 5,280 connected box-minus-void geometries and 63,360
fixed-time Hodge-heat instances. Through-tunnels and enclosed cavities control
the first and⦠See the full description on the dataset page: https://huggingface.co/datasets/cppyyy/TopoBox-3D.stocks-CPPLUS-1D-candlesLangMap-TheStack-cpp-100M
LangMap-TheStack-cpp-100M
Code finetuning dataset for cpp streamed from bigcode/the-stack.
Tokens collected: 100,000,000 (target: 100,000,000)
Tokenizer: allenai/OLMo-3-1025-7B
Schema: {"text": [...]} (sanitised source code)
code_contest_instruct_cppIDD_Detection_CPPE5The IDD Object Detection dataset containing 40K images with CPPE-5 like (or YOLO) dataset annotation format.Refer to the original dataset: https://idd.insaan.iiit.ac.in