GPAI Ledger › SmolLM3-3B (HuggingFace) › Capture 11 Aug 2026

SmolLM3-3B — capture 20260811T110051Z

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SmolLM3-3B
Public Summary of Training Content
Summary v1.0: https://huggingface.co/spaces/hfmlsoc/
smollm3-eu-data-transparency/commits/main - Last updated:
25/07/2025
This Space contains the transparency report for the SmolLM3-3B:
https://huggingface.co/HuggingFaceTB/SmolLM3-3B GPAI model
developed by Hugging Face: https://huggingface.co/ following the
guidelines provided by the AI O�ice.
It may serve as an example for open-source GPAI trained exclusively
on public datasets. For more information, see the Explanatory Notice
and Template: https://digital-strategy.ec.europa.eu/en/library/
explanatory-notice-and-template-public-summary-training-content-
general-purpose-ai-models

 TL;DR
SmolLM3-3B is a state-of-the-art 3-billion parameter language model
by Hugging Face trained on 10+ trillion tokens from publicly available
datasets including web documents, scientific articles, and code.
Training focused on 6 EU languages plus others. The model uses only
public datasets (no commercial licensing, user data, or other private
data). Data processing was done by the original component dataset
curators with varied approaches to TDM and filtering that typically
include compliance with robots.txt and other opt-out mechanisms,
and educational content classifiers.
1. General information
TL;DR: Provider: Hugging Face | Model: SmolLM3-3B | Training: 10+
trillion tokens, 6 EU languages + others
 Click for full information ▼
1.1. Provider identification
◦ Provider name and contact details:
◦ Hugging Face Inc., Email: legal@huggingface.co
1.2. Model identification
◦ Versioned model name(s):
◦ SmolLM3-3B
◦ Model dependencies:
◦ None
1.3. Modalities, overall training data size and other
characteristics
◦ TEXT
◦ Size: [more than 10 trillion tokens]
◦ The training corpus is made up of 11 trillion tokens as
tokenized by the Llama-3.2-1B: https://huggingface.co/
meta-llama/Llama-3.2-1B tokenizer.
◦ The training corpus for SmolLM3 is made up of several
publicly accessible large datasets containing web documents,
scientific articles, so�ware code, and synthetically generated
textbooks and mathematical data for pre-training in addition to
several mid-training and fine-tuning datasets to enable chat
interactions, instruction-following and task-solving behaviors.
◦ Latest date of data acquisition/collection for model training:
◦ The training dataset is made up of di�erent subsets with
di�erent publication and cuto� dates. For pre-training, the
earliest dataset was last updated on 4/3/2024 (Stack v2), and
the latest on 2/19/2025 (FineWeb2-HQ)
◦ Description of the linguistic characteristics of the overall
training data:
◦ The overall training process focuses on 6 languages that are
all o�icial EU languages: English, French, Spanish, German,
Italian, and Portuguese. In addition, pre-training intentionally
included smaller quantities of data in Mandarin Chinese,
Russian, Persian, Japanese, Korean, Vietnamese, Hindi, Thai,
and Greek (also an o�icial EU language). Other languages may
have been included due to the limitations of automatic
language identification in filtering stages.
◦ Other relevant characteristics of the overall training data:
◦ The training data also includes so�ware code in the
programming languages included in the Stack v2 dataset (16
languages including C, C++, C-Sharp, Python, Java, JavaScript,
Markdown, HTML, Shell, PHP , TypeScript, Swi�, SQL, Ruby, Go,
and Rust).
2. List of data sources
TL;DR:
  Publicly available datasets including synthetic data |
  No
commercial licensing, crawling, user data, or private data
 Click for full information ▼
2.1. Publicly available datasets
◦ Have you used publicly available datasets to train the model?
◦ ☑ Yes
◦ If yes, specify the modality(ies) of the content covered by the
datasets concerned:
◦ ☑ Text
◦ List of large publicly available datasets:
◦ DCLM: https://hf.co/datasets/mlfoundations/dclm-
baseline-1.0: https://hf.co/datasets/mlfoundations/dclm-
baseline-1.0
◦ FineWeb-Edu: https://hf.co/datasets/HuggingFaceFW/
fineweb-edu: https://huggingface.co/datasets/HuggingFaceFW/
fineweb-edu
◦ FineWeb2: https://hf.co/datasets/epfml/FineWeb2-HQ:
https://huggingface.co/datasets/epfml/FineWeb2-HQ
◦ Stack V2: https://hf.co/datasets/bigcode/the-stack-v2:
https://hf.co/datasets/bigcode/the-stack-v2
◦ pes2o: https://hf.co/datasets/allenai/peS2o: https://hf.co/
datasets/allenai/peS2o
◦ FineMath : https://hf.co/datasets/HuggingFaceTB/finemath:
https://hf.co/datasets/HuggingFaceTB/finemath
◦ MegaMath: https://hf.co/datasets/LLM360/MegaMath: https://
hf.co/datasets/LLM360/MegaMath
◦ SmolTalk2: https://hf.co/datasets/HuggingFaceTB/smoltalk2:
https://huggingface.co/datasets/HuggingFaceTB/smoltalk2
◦ General description of other publicly available datasets not
listed above:
◦ In addition to the large datasets cited above, many
additional publicly available datasets were added to target
specific domains, including several math datasets made up of
both web-filtered and synthetic data, Wikipedia data,
"reasoning data" generated by selected large models on diverse
problems, Jupyter notebooks for code, and synthetically
generated textbooks; all in English language or so�ware code.
The full list of pre-training datasets is available at the following
URL: https://hf.co/collections/HuggingFaceTB/smollm3-
pretraining-datasets-685a7353fdc01aecde51b1d9: https://
hf.co/collections/HuggingFaceTB/smollm3-pretraining-
datasets-685a7353fdc01aecde51b1d9
2.2. Private non-publicly available datasets obtained
from third parties
2.2.1. Datasets commercially licensed by rightsholders
or their representatives
◦ Have you concluded transactional commercial licensing
agreement(s) with rightsholder(s) or with their representatives?
◦ ☐ No
2.2.2. Private datasets obtained from other third
parties
parties
◦ Have you obtained private datasets from third parties that are
not licensed as described in Section 2.2.1, such as data obtained
from providers of private databases, or data intermediaries?
◦ ☐ No
2.3. Data crawled and scraped from online sources
◦ Were crawlers used by the provider or on behalf of?
◦ ☐ No
2.4. User data
◦ Was data from user interactions with the AI model (e.g. user
input and prompts) used to train the model?
◦ ☐ No
◦ Was data collected from user interactions with the provider's
other services or products used to train the model?
◦ ☐ No
2.5. Synthetic data
◦ Was synthetic AI-generated data created by the provider or on
their behalf to train the model?
◦ ☑ Yes
◦ If yes, modality of the synthetic data:
◦ ☑ Text
◦ Information about other AI models, including providerʼs own AI
model(s) not available on the market, used to generate synthetic
data to train the model to which this Summary applies:
◦ Additional data was generated using the Qwen3-32B: https://
huggingface.co/Qwen/Qwen3-32B and Qwen3-0.6B: https://
huggingface.co/Qwen/Qwen3-Embedding-0.6B open-weight
models.
◦ Additional comments (optional):
◦ Some parts of the SmolTalk2: https://hf.co/datasets/
HuggingFaceTB/smoltalk2 dataset mentioned in Section 2.1
were synthetically generated for the purpose of training this
model. See License information in the dataset description:
https://huggingface.co/datasets/HuggingFaceTB/
smoltalk2#license.
2.6. Other sources of data
◦ Have data sources other than those described in Sections 2.1 to
2.5 been used to train the model?
◦ ☐ No
3. Data processing aspects
TL;DR: TDM rights: robots.txt baseline otherwise dataset-dependent |
Content filtering: Dataset-dependent including educational classifiers
 Click for full information ▼
3.1. Respect of reservation of rights from text and data
mining exception or limitation
◦ Describe the measures implemented before model training to
respect reservations of rights from the TDM exception or limitation
before and during data collection, including the opt-out protocols
and solutions honoured by the provider or, as applicable, by third
parties from which datasets have been obtained:
◦ The training corpus for SmolLM3-3B is made up of diverse
pre-existing public datasets maintained by various
organizations who still have their own approach to managing
the TDM exception. All crawl-based data in the datasets uses
the CommonCrawl archives which comply with robots.txt.
Some datasets such as the Stack v2 additionally o�er general
opt-out mechanisms. For each dataset, the latest publicly
available version was used to ensure propagation of any rights
reservation expressed to the dataset custodian.
3.2. Removal of illegal content
◦ General description of measures taken:
◦  Each of the component datasets leveraged is the product of
a distinct curation e�ort by its custodians to select the most
desirable content. The specific approaches can typically be
found in the dataset documentation. Among other factors,
most of the datasets take the approach of using classifiers to
identify "highly educational" samples that lowers the likelihood
of illegal content. Datasets like The Stack v2 for example
additionally filtered out so�ware code with copyle� licenses
and applied automatic redaction of categories of personal data.
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