scaling
mlfoundations-dev_-_oh_teknium_scaling_down_random_0.5-ggufmlfoundations-dev_-_oh_teknium_scaling_down_random_0.4-ggufmlfoundations-dev_-_oh_teknium_scaling_down_random_0.7-ggufmlfoundations-dev_-_oh_teknium_scaling_down_random_0.6-ggufmlfoundations-dev_-_oh_teknium_scaling_down_random_0.9-ggufmlfoundations-dev_-_oh_teknium_scaling_down_random_0.8-ggufmlfoundations-dev_-_oh_teknium_scaling_down_random_1.0-ggufmlfoundations-dev_-_oh_teknium_scaling_down_ratiocontrolled_0.5-gguf
Datasets
All datasets matching “scaling”colinear_scaling_models
license: gpl-2.0
Collinear Scaling Models
Checkpoint repository for scaling law experiments comparing collinear (CO) and non-collinear (NC) experimental designs.
Directory Structure
{dataset}/{design}/N_{param_count}/
Dataset: wikipedia, pes2o, cosmopedia, redpajama, c4 (plus _fp16 and _bigtpp variants)
Design: colinear or non_colinear
N: Model parameter count (one of 14 canonical sizes from ~5M to ~70M)
Experimental Designs
Collinear (CO):… See the full description on the dataset page: https://huggingface.co/datasets/leibnitz-lab/colinear_scaling_models.kernelbench-samples
KernelBench Samples
Samples from experiments for KernelBench, described in our arxiv
Learn more about KernelBench from our
Paper
Github Repo
The samples are organized as such
baseline_eval (Section 4 Baseline)
repeated_sampling (Section 5.1.1 Repeated Sampling)
iterative_refinement (Section 5.1.2 Iterative Refinement of Generations)
Within each folder, we organize the results by /level/model/problem_{id}/sample_{id}.
The inner most .json file contains the generated kernel and… See the full description on the dataset page: https://huggingface.co/datasets/ScalingIntelligence/kernelbench-samples.colinear_scaling_models
Collinear/Non-Collinear Scaling Models
Checkpoint repository for scaling law experiments comparing collinear (CO) and non-collinear (NC) experimental designs for the paper Tokens-per-Parameter Coverage Is Critical for Robust LLM Scaling Law Extrapolation under review for NeurIPS 2026.
Code
Anonymized code repository (reproduces all tables): anonymous.4open.science
Directory Structure
{dataset}/{design}/N_{param_count}/
Dataset: wikipedia, pes2o, cosmopedia… See the full description on the dataset page: https://huggingface.co/datasets/TPPIsCriticalFor/colinear_scaling_models.TSFM-ScalingLaws-Dataset
TSFM-ScalingLaws-Dataset
This is the dataset for the paper Towards Neural Scaling Laws for Time Series Foundation Models.
Code: https://github.com/Qingrenn/TSFM-ScalingLaws
Well-trained models: https://huggingface.co/PeacefulData/TSFM-ScalingLaws-Checkpoints
Dataset Summary
Domain
Transport
Climate
Energy
Cloud
Health
Sales
Web
Total
Datasets
8
2
14
3
9
1
2
39
Time Points
4.82B
4.73B4.76B
2.15B
232M
140M
40M
16.8B
Proportion
28.52%
28.06%
28.21%
12.76%… See the full description on the dataset page: https://huggingface.co/datasets/Qingren/TSFM-ScalingLaws-Dataset.scaling-laws-cacheThis dataset is my cache for the scaling-laws related to the gemstone models.
In data_cache is the approach 3 data cache with the mins for delta=1e-4, the mins for delta=1e-3 are in mins_1e-3.
This is the code I used to upload it:
import pandas as pd
from datasets import Dataset
import os
import gc
def get_data_dict(path):
contents = os.listdir(path)
ds_store = {}
for i, file in enumerate(contents):
gc.collect()
df = pd.read_parquet(f"{path}{file}")
for… See the full description on the dataset page: https://huggingface.co/datasets/smcleish/scaling-laws-cache.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/},
}