datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
faiss-smollm
FAISS-Based Novelty Detection for SmolLM and SmolLM2
This tutorial demonstrates how to the measure novelty of text queries with respect to the provided SmolLM and SmolLM2 pretraining corpora, with optional ColBERTv2 re-ranking for improved precision.
Overview
The pipeline consists of four main steps:
Generate Embeddings - Encode your queries using a sentence transformer
FAISS Search - Retrieve top-K most similar documents from the pretraining corpus
Combine… See the full description on the dataset page: https://huggingface.co/datasets/stai-tuebingen/faiss-smollm.smollm-corpus
SmolLM-Corpus
This dataset is a curated collection of high-quality educational and synthetic data designed for training small language models.
You can find more details about the models trained on this dataset in our SmolLM blog post.
Dataset subsets
Cosmopedia v2
Cosmopedia v2 is an enhanced version of Cosmopedia, the largest synthetic dataset for pre-training, consisting of over 39 million textbooks, blog posts, and stories generated by… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus.smollm-chunked
FAISS Indices and Chunked Datasets for SmolLM and SmolLM2 corpora
This repository contains part of the FAISS indices and chunked datasets used for novelty detection for SmolLM and SmolLM2, as presented in the paper LLM generation novelty through the lens of semantic similarity.
Full Documentation
For complete usage instructions, installation guide, and tutorial, please refer to:
Main Tutorial README
Data Distribution
Due to Hugging Face storage quota… See the full description on the dataset page: https://huggingface.co/datasets/enguyen/smollm-chunked.smollm-corpus
SmolLM-Corpus: Now shuffled and sharded!
This is a version of the SmolLM-Corpus where the 3 subsets have been interleved, shuffled and sharded as 23698 jsonl.zst files for easy streaming!
The dataset is comprised of the cosmopedia-v2 and fineweb-edu-dedup subsets from the original SmolLM-Corpus repo, with the python-edu subset being pulled from my python-edu repo.
Dataset Structure
The dataset is split into 24 subdirectories, with the first 23 containing 1000 shards… See the full description on the dataset page: https://huggingface.co/datasets/Avelina/smollm-corpus.the-stack-v2-smollm3
The Stack v2 — materialized source code
Upstream dataset:
bigcode/the-stack-v2
Exact upstream commit:
e565caa3a78c2423bd374333a472b049eb090e47
Primary source-content endpoint:
https://softwareheritage.s3.amazonaws.com/content/{blob_id}
Configurations
TypeScript
Swift
Ruby
Rust
Go
Shell
Jupyter_Notebook
HTML
Python
Java
JavaScript
C
C++
C-Sharp
PHP
SQL
Markdown
Added columns
content: decoded source content
download_error: null on successful… See the full description on the dataset page: https://huggingface.co/datasets/jordangong/the-stack-v2-smollm3.smollm-corpus-cleaned
SmolLM-Corpus: Now shuffled and sharded (and Cleaned)!
This is a version of the SmolLM-Corpus where the 3 subsets have been interleved, shuffled and sharded as 23698 jsonl.zst files for easy streaming!
The dataset is comprised of the cosmopedia-v2 and fineweb-edu-dedup subsets from the original SmolLM-Corpus repo, with the python-edu subset being pulled from my python-edu-cleaned repo.
Dataset Structure
The dataset is split into 24 subdirectories, with the first 23… See the full description on the dataset page: https://huggingface.co/datasets/Avelina/smollm-corpus-cleaned.SmolLM2-135M-10BThis dataset is sampled from the SmolLM2 Corpus described in https://arxiv.org/abs/2502.02737. Specifically, we sampled from
the SmolLM2-135M pretraining data, a 2T token mixture consisting of four complete high quality datasets, and selected portions of
DCLM-Edu and FineWeb-Edu sampled at a 6:4 ratio.
This sample is intended to enable fast downloading and training of sparsify models.
FineMath: 34B tokens
Stack-Edu: 125B tokens
InfiMM-WebMath: 40B tokens
Cosmopedia V2: 30B tokens… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/SmolLM2-135M-10B.smollm-12.5-corpus
SmolLM-1/8-Corpus
Around 1/8 upper-quality subset of SmolLM Corpus for training Chinchilla-optimal GPT-2 scale (sub 1.5B) models, which is a good scale for verifying a model architecture under the scaling laws.
Firstly filtered samples with int_score >=4 from FineWeb-edu-dedup, then keep the training mixture with the same distribution from SmolLM.
In which FineWeb-Edu-dedup occupies around 70% of the corpus. Then sample other dataset based on the mixture ratios respectively. For… See the full description on the dataset page: https://huggingface.co/datasets/chengjunyan1/smollm-12.5-corpus.seq2seq-mixed-pretraining-SmolLM2smollm-cosmopedia-v2MHA2MLA-corpus-smollmjupyter-scripts-smollm3
The Stack v2 Jupyter Notebooks as Scripts
This dataset contains script representations of the Jupyter notebooks in
The Stack v2. It was
created from the materialized Jupyter_Notebook split in
jordangong/the-stack-v2-smollm3.
The output schema follows the Jupyter-script schema used by
bigcode/starcoderdata,
but this release is not deduplicated, PII-filtered, or otherwise equivalent
to StarCoderData's filtered split.
Relationship to the SmolLM3 training mix
This… See the full description on the dataset page: https://huggingface.co/datasets/jordangong/jupyter-scripts-smollm3.apigen-smollm-trl-FC
Dataset card for argilla-warehouse/apigen-smollm-trl-FC
This dataset is a merge of argilla/Synth-APIGen-v0.1
and Salesforce/xlam-function-calling-60k, and was prepared for training using the script
prepare_for_sft.py that can be found in the repository files.
References
@article{liu2024apigen,
title={APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets},
author={Liu, Zuxin and Hoang, Thai and Zhang, Jianguo and Zhu, Ming and… See the full description on the dataset page: https://huggingface.co/datasets/argilla-warehouse/apigen-smollm-trl-FC.smollm-10SmolLM2-135M-gradients-10Mmath-rlvr-mini-smollm2-0.4b-v2smollm3-configs
SmolLM3 Training Configs
[IMPORTANT NOTE]: for the latest configs go to this repo: https://github.com/huggingface/smollm/tree/main/text/pretraining/smollm3
Here you can find the training configs for SmoLLM3-3B-Base using nanotron with exact training details and data mixtures.
The model was trained on 11.2T tokens in 3 stages on 4k context:
stage 1 config
stage 2 config
stage 3 config
And then we trained on an additional 2 stages to extend the contetx length to 64k:
stage 4… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceTB/smollm3-configs.c4-rewritten-14b-retok-smollm360mSmolLM2-1.7B-stage-4-20BSmolLM2-1.7B-stage-4-100Bbooks3-SmolLM2-sortedsmollm3-baseline_v3dclm-14b-c4-rewritten-14b-retok-smollm360msmollm-corpus-2percentdclm-6.7b-c4-rewritten-6.7b-retok-smollm360msmollm-corpus-3.5M
A very small version of smollm-corpus (+finemath-4plus) for experimenting llm pre-training.
cosmopedia-v2 (1M rows)
fineweb-edu-dedup (1M rows)
python-edu (0.5M rows)
finemath-4plus (1M rows)
smol-liberoThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": null,
"total_episodes": 50,
"total_frames": 13021,
"total_tasks": 1,
"total_videos": 0,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 20,
"splits": {
"train": "0:50"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": null,
"features": {… See the full description on the dataset page: https://huggingface.co/datasets/jadechoghari/smol-libero.books3-SmolLM2SmolLM-lmsys-mixturessmollm-corpus-fineweb-edu-enPurified-openai-messages
📖 smollm-corpus-fineweb-edu-enPurified-openai-messages
smollm-corpus-fineweb-edu-enPurified is a highly curated, "prose-first" subset of the fineweb-edu-dedup subset found in HuggingFaceTB/smollm-corpus.
The enPurified collection is built on a specific philosophy: Specialization. While the original dataset is excellent for general pre-training, high-quality fluent English prose often gets diluted when mixed with syntax-heavy code, rigid math formulas, or low-information web junk.… See the full description on the dataset page: https://huggingface.co/datasets/enPurified/smollm-corpus-fineweb-edu-enPurified-openai-messages.