datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sutra-1B
Sutra 1B Pretraining Dataset
A high-quality pedagogical dataset designed for LLM pretraining, containing 948,709 educational entries totaling over 1 billion tokens.
Dataset Description
This dataset was generated using the Sutra framework, which creates structured educational content optimized for language model pretraining. Each entry is designed to maximize learning efficiency through:
Clear pedagogical structure: Content follows proven educational patterns
Cross-domain… See the full description on the dataset page: https://huggingface.co/datasets/codelion/sutra-1B.minicpm5-1b-quantization-benchmark
openbmb/MiniCPM5-1B 次世代量子化（Quanto FP8 / INT4 vs BNB 4bit）実測ベンチマークレポート
対象モデル: openbmb/MiniCPM5-1B (1.16B parameters, 128k context, LlamaForCausalLM)
検証ハードウェア: NVIDIA GeForce RTX 4070 Ti (12GB GDDR6X, Ada Lovelace, Compute Capability 8.9, 第4世代Tensor Core)
実行環境: Windows / Python 3.13 / PyTorch 2.6.0+cu124 / transformers 4.57.6 / optimum-quanto 0.2.7 / bitsandbytes 0.50.0
検証日: 2026-09-19 12:12:34
1. エグゼクティブサマリー（全体比較）
NVIDIA GeForce RTX 4070 Ti 実機環境において、標準ネイティブ… See the full description on the dataset page: https://huggingface.co/datasets/aoiandroid/minicpm5-1b-quantization-benchmark.comma_v0.1_training_dataset_sample_1B
Comma v0.1 Training Dataset (1 Billion Token Sample)
This is a 1 billion token subset of the Comma v0.1 Training Set intended
as a convenience for small deep learning experiments. It is similar in spirit to the 1 billion token RedPajama sample
which is no longer functioning with HuggingFace transformers due to involving the execution of arbitrary code at load time.
Method
The subset was created using a single item batch version of the following script which I no… See the full description on the dataset page: https://huggingface.co/datasets/jdpressman/comma_v0.1_training_dataset_sample_1B.tw-apple-daily-1B
Dataset Card for tw-apple-daily-1B
本資料集收錄 2003–2022 年間繁體中文公開新聞報導文本，總 token 數約 1B（10 億），可作為大規模臺灣新聞語料的補充預訓練來源。每筆樣本以結構化文字呈現新聞分類、發布時間、標題、關鍵字、描述與正文。
Dataset Details
Dataset Description
資料以 2003–2022 年間之繁中新聞報導為主，內容涵蓋政治、社會、財經、論壇專欄等多元主題。本資料集將原報導頁面解析後輸出為純文字，並在每篇前段保留以下結構化欄位：
新聞分類：...
新聞發佈時間：YYYY-MM-DDTHH:MM:SSZ
新聞標題：...
關鍵字：...
新聞描述：...
新聞內容：
（正文）
可作為新聞文體、時事語境、論壇專欄寫作風格的繁中模型訓練資料。資料夾中另附 post-processing.py 提供基本後處理邏輯。
Curated by: Huang Liang Hsun
Language(s) (NLP):… See the full description on the dataset page: https://huggingface.co/datasets/lianghsun/tw-apple-daily-1B.1b-model-eval
1B Model Eval — 16 Sub-2B LLMs Benchmark
A comprehensive G-Eval (Liu et al., EMNLP 2023) benchmark of 16 small language models under 2B parameters, covering 6 dimensions across 33 test items.
Judge model: DeepSeek-v4-pro
Files
File
Description
nightly_raw_<model>.json
Raw outputs for each model — 33 generated items across 6 dimensions
nightly_eval_results.json
Judge scores and detailed reasoning for all 16 models
summary.csv
One-row-per-model score… See the full description on the dataset page: https://huggingface.co/datasets/aaaaaaapluto/1b-model-eval.lfm2.5-8b-a1b-506xTrace of LFM2.5 8B A1B LLM made by LiquidAI.
Data is presented in ChatML format and each conversation split by newline. Ready to be used for fine-tuning.
Example:
{"messages":[{"role":"user", "content":"Hello!"}, {"role":"assistant", "content":"Hello!"}]}
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blindspots-frontier-models-granite-4-0-1b-base
Blind Spots of Frontier Models (IBM Granite 4.0 1B Base)
Model tested: ibm-granite/granite-4.0-1b-baseModel card: https://huggingface.co/ibm-granite/granite-4.0-1b-base
For inference, I ran this model locally, though I also experimented with free models from OpenRouter.
This dataset contains 10 evaluation rows with:
input
expected_output
model_output
notes
is_correct
I loaded the model with transformers and evaluated it using strict concise-answer prompts.
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/Tomodovodoo/blindspots-frontier-models-granite-4-0-1b-base.TevDistilled_1B