datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
1218_imu1_base_stable_corpus
IMU-1 Stage 1 Training Corpus (Stable Phase)
Pre-tokenized training data for Stage 1 (stable phase) of IMU-1, a sample-efficient 430M parameter language model.
Dataset Details
Property
Value
Tokens
~29B
Format
Memory-mapped NumPy (.npy)
Tokenizer
SmolLM2-360M
Vocab size
49,152
Data Sources
High-quality filtered web data including:
DCLM-edu (educational content filtered from DCLM)
FineWeb-edu
Curated web sources
Download… See the full description on the dataset page: https://huggingface.co/datasets/thepowerfuldeez/1218_imu1_base_stable_corpus.stable-diffusion-prompts-2.47M
Source
Combined text-only dataset from
poloclub/diffusiondb
Gustavosta/Stable-Diffusion-Prompts
bartman081523/stable-diffusion-discord-prompts
FredZhang7/krea-ai-prompts
For preprocessing methods, please see Fast GPT2 PromptGen.
Python
Download and save the dataset to all_prompts.txt locally.
pip install datasets
import datasets
dataset = datasets.load_dataset("FredZhang7/stable-diffusion-prompts-2.47M")
train = dataset["train"]
prompts = train["text"]
with… See the full description on the dataset page: https://huggingface.co/datasets/FredZhang7/stable-diffusion-prompts-2.47M.Stable-Code-Python-SFT
Stable Code Python SFT
The Stable Code Python SFT dataset is a high-quality synthetic dataset derived from the
stabilityai/stable-code-instruct-3b model for the purpose of supervised fine-tuning (SFT). Please refer to the
Versioning section for dataset versions.
Note: If you would like to contribute to this repository,
please read the CONTRIBUTING first.
TableofContents
Features
File Structure
Metadata
Usage
Versioning
License
TeamContact
Reference
Citation… See the full description on the dataset page: https://huggingface.co/datasets/bunyaminergen/Stable-Code-Python-SFT.maze-17x17-500k-shortest
Maze 17x17 500k — Shortest Path
Supervised fine-tuning corpus of 17x17 mazes where the target trajectory is the unique shortest path from START to GOAL. Mazes are generated with Prim's algorithm so there is a single solution path.
Splits and configs
train: 450,000 examples
test: 512 examples
Three reward configurations are provided — they share the same prompts but
differ in the reward-model metadata used by downstream RL:
config
reward signal
binary
1 if the… See the full description on the dataset page: https://huggingface.co/datasets/stablegradients/maze-17x17-500k-shortest.Stable-Diffusion-Prompts-Deduped-2.008M
Original Dataset by FredZhang7
Deduped from 2,473,022 down to 2,007,998.
Changed anything that had [ prompt text ], ( prompt text ), or < prompt text >, to [prompt text], (prompt text), and <prompt text>.
2 or more spaces converted to a single space.
Removed all "
Removed spaces at beginnings.
pie-gem5-pairs
PIE gem5-timed code optimization (src,tgt pairs)
C++ program-optimization data derived from the PIE dataset
("Learning Performance-Improving Code Edits"),
re-timed end-to-end with gem5 (x86 Skylake, syscall-emulation mode) at
per-test-case granularity. One row per official (source, target) program pair.
This dataset is reward-agnostic: it ships the full per-test-case reference timings and
case manifests so a downstream RL / eval pipeline decides at runtime how many cases to use… See the full description on the dataset page: https://huggingface.co/datasets/stablegradients/pie-gem5-pairs.maze-17x17-500k-general
Maze 17x17 500k — General Paths
Supervised fine-tuning corpus of 17x17 mazes with the target being a valid (not necessarily shortest) path from START to GOAL. Mazes are generated with Prim's algorithm.
Splits and configs
train: 450,000 examples
test: 512 examples
Three reward configurations are provided — they share the same prompts but
differ in the reward-model metadata used by downstream RL:
config
reward signal
binary
1 if the path reaches the goal, else… See the full description on the dataset page: https://huggingface.co/datasets/stablegradients/maze-17x17-500k-general.stable_diffusion_female_prompts
stable_diffusion_female_prompts
⚠ PRECAUTION : This dataset contains NSFW and SFW prompts. ⚠
Stable Diffusion female prompts and some informations about the generated images
Dataset Details
Dataset Description
Enhancing the dataset progress is going on and going to be updated frequently.
These Huggingface datasets are used for enhancing the dataset:
FredZhang7/anime-prompts-180K
FredZhang7/stable-diffusion-prompts-2.47M… See the full description on the dataset page: https://huggingface.co/datasets/WoWoWoWololo/stable_diffusion_female_prompts.civitai-stable-diffusion-2.5minspired by thefcraft/civitai-stable-diffusion-337k.
collected using civitai api to get all prompts.
prompt_description_stable_diffusion_3k
The Synthetic Description from Prompts Dataset
This dataset is created using the Phi 2 3B Q4_K_S quantized model, using 3k random samples from training set of a base dataset of about 80,000 prompts from the Stable Diffusion dataset on Lexica.art. This dataset is designed to explore the capabilities of language models in generating creative and expanded descriptions from concise prompts.
Source Data… See the full description on the dataset page: https://huggingface.co/datasets/gokaygokay/prompt_description_stable_diffusion_3k.pie-gem5-bysrc
PIE gem5-timed code optimization (per source program)
C++ program-optimization data derived from the PIE dataset
("Learning Performance-Improving Code Edits"),
re-timed end-to-end with gem5 (x86 Skylake, syscall-emulation mode) at
per-test-case granularity. One row per unique (problem, source) program; the best surviving target is kept as an oracle ceiling.
This dataset is reward-agnostic: it ships the full per-test-case reference timings and
case manifests so a downstream RL /… See the full description on the dataset page: https://huggingface.co/datasets/stablegradients/pie-gem5-bysrc.StableHLO-OutOfGrammar-25
StableHLO-OutOfGrammar-25
Hand-authored NL→StableHLO pairs using ops outside our 10-op grammar scope (n=25, stress set).
Composition
Instances: 25
Format: one JSON record per line in data/test.jsonl
Schema: fields = dialect, id, mlir, nl, op, source
Verifier: stablehlo-opt v1.4.0 (upstream truth) and iree-compile --compile-to=input (substitute, 50/50 concordant on a stratified n=50 sample)
License: Apache-2.0 (SPDX: Apache-2.0). No third-party IP restrictions.… See the full description on the dataset page: https://huggingface.co/datasets/plawanrath/StableHLO-OutOfGrammar-25.StableHLO-Spec-30
StableHLO-Spec-30
Hand-authored NL→StableHLO pairs across 10 op families (n=30).
Composition
Instances: 30
Format: one JSON record per line in data/test.jsonl
Schema: fields = dialect, difficulty, id, mlir, nl, notes
Verifier: stablehlo-opt v1.4.0 (upstream truth) and iree-compile --compile-to=input (substitute, 50/50 concordant on a stratified n=50 sample)
License: Apache-2.0 (SPDX: Apache-2.0). No third-party IP restrictions.
Loading
from datasets… See the full description on the dataset page: https://huggingface.co/datasets/plawanrath/StableHLO-Spec-30.StableHLO-Held-Out-200
StableHLO-Held-Out-200
Programmatic parametric sweep over 7 StableHLO op families × 6 dtypes × 3 shape ranks (n=200, verifier-clean).
Composition
Instances: 200
Format: one JSON record per line in data/test.jsonl
Schema: fields = dialect, difficulty, id, mlir, nl, notes, source
Verifier: stablehlo-opt v1.4.0 (upstream truth) and iree-compile --compile-to=input (substitute, 50/50 concordant on a stratified n=50 sample)
License: Apache-2.0 (SPDX: Apache-2.0). No… See the full description on the dataset page: https://huggingface.co/datasets/plawanrath/StableHLO-Held-Out-200.Stable-Diffusion-Prompts-Deduped-2.008M
Original Dataset by FredZhang7
Deduped from 2,473,022 down to 2,007,998.
Changed anything that had [ prompt text ], ( prompt text ), or < prompt text >, to [prompt text], (prompt text), and <prompt text>.
2 or more spaces converted to a single space.
Removed all "
Removed spaces at beginnings.
stable-diffusion-prompts-2.47M
Source
Combined text-only dataset from
poloclub/diffusiondb
Gustavosta/Stable-Diffusion-Prompts
bartman081523/stable-diffusion-discord-prompts
FredZhang7/krea-ai-prompts
For preprocessing methods, please see Fast GPT2 PromptGen.
Python
Download and save the dataset to all_prompts.txt locally.
pip install datasets
import datasets
dataset = datasets.load_dataset("FredZhang7/stable-diffusion-prompts-2.47M")
train = dataset["train"]
prompts = train["text"]
with… See the full description on the dataset page: https://huggingface.co/datasets/Pratofeitoo/stable-diffusion-prompts-2.47M.