datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.jupyter-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.smollm3-3b-base-blind-spots
SmolLM3-3B-Base Blind Spots Dataset
This dataset contains 10 test cases where I explored the failure modes of
SmolLM3-3B-Base,
a 3 billion parameter base language model released by HuggingFace in 2025.
The goal was to find diverse cases where the model makes clearly incorrect
or unexpected completions its "blind spots."
Model Tested
Model: HuggingFaceTB/SmolLM3-3B-Base
Parameters: 3B
Type: Base pretrained model
License: Apache 2.0
How I Loaded the Model
I… See the full description on the dataset page: https://huggingface.co/datasets/FatimaAfzal01/smollm3-3b-base-blind-spots.Blind_Spots_Dataset_SmolLM3-3B-Base
SmolLM3-3B-Base Blind Spots Dataset
Dataset Summary
This dataset documents 13 failure cases of the HuggingFaceTB/SmolLM3-3B-Base model, a 3-billion parameter base language model pretrained on 11.2 trillion tokens. Each row contains a text completion prompt, the expected correct output, the model's actual output, and the category. The dataset spans 13 distinct categories in which the SmolLM3-3B-Base model fails to work as expected.
Model Tested
Model:… See the full description on the dataset page: https://huggingface.co/datasets/Yanmife/Blind_Spots_Dataset_SmolLM3-3B-Base.smollm3-3b-base-blindspots
SmolLM3-3B-Base — Blind Spots Dataset
This dataset contains 10 diverse input-output pairs where the base language model
HuggingFaceTB/SmolLM3-3B-Base
produces incorrect predictions under greedy decoding. Each row records the exact prompt fed
to the model, the correct expected answer, and what the model actually generated — along with
a description of the error type.
Model Tested
Field
Value
Model
HuggingFaceTB/SmolLM3-3B-Base
Parameters
3 billion… See the full description on the dataset page: https://huggingface.co/datasets/Dhruba461/smollm3-3b-base-blindspots.smollm3-base-blind-spots
SmolLM3-3B-Base — Blind Spots Dataset
Overview
This dataset documents 12 diverse blind spots of the base language model
HuggingFaceTB/SmolLM3-3B-Base
(3 billion parameters, Apache-2.0, released 2025).
Each row contains:
Field
Description
id
Integer index
category
Type of reasoning required
input_prompt
Partial text fed to the model
expected_output
Correct continuation
model_output
What SmolLM3-3B-Base actually generated
notes
Explanation of why the… See the full description on the dataset page: https://huggingface.co/datasets/NiazTahi/smollm3-base-blind-spots.smollm3-3b-base-blind-spots
SmolLM3-3B-Base Blind Spots
Title & Overview
A curated set of failure cases for HuggingFaceTB/SmolLM3-3B-Base, showcasing blind spots discovered while probing the 3B-parameter base pre-training checkpoint released in July 2025. Each entry captures a prompt, the expected aligned behaviour, and the model's actual output. The dataset illustrates common failure patterns observed when probing the base model without any instruction tuning, RLHF, or safety fine-tuning applied.… See the full description on the dataset page: https://huggingface.co/datasets/aneeshadas02/smollm3-3b-base-blind-spots.smollm3-blind-spots
SmolLM3-3B-Base Blind Spots
This dataset documents 10 failure cases observed while testing the model HuggingFaceTB/SmolLM3-3B-Base in Google Colab.
Model Tested
Model: HuggingFaceTB/SmolLM3-3B-Base
How the Model Was Loaded
The model was loaded in Google Colab using the transformers library with 4-bit quantization to run on limited GPU resources.
Dataset Description
This dataset contains 10 examples where the model produced incorrect outputs or failed… See the full description on the dataset page: https://huggingface.co/datasets/Sgobir/smollm3-blind-spots.smollm3-base-blindspots
SmolLM3-3B-Base Blind Spots Evaluation Dataset
Dataset Summary
This dataset documents 10 diverse failure cases discovered while evaluating
HuggingFaceTB/SmolLM3-3B-Base,
a 3-billion parameter decoder-only base language model released by Hugging Face in July 2025.
The evaluation was conducted as part of the Fatima Fellowship technical challenge on Blind Spots of Frontier Models.
Model Tested
Model: HuggingFaceTB/SmolLM3-3B-Base
Parameters: 3 billion… See the full description on the dataset page: https://huggingface.co/datasets/habibahabchi/smollm3-base-blindspots.smollm3-blindspots
Blind Spots of SmolLM3-3B-Base
This dataset documents systematic failure cases ("blind spots") observed
when evaluating the SmolLM3-3B-Base model.
The goal of this dataset is to identify patterns where a small base
language model struggles with reasoning tasks that require precise
symbolic or character-level manipulation.
The dataset contains prompts where the model produces incorrect answers
compared to the expected output.
Model Tested
Model:… See the full description on the dataset page: https://huggingface.co/datasets/hans1337/smollm3-blindspots.smollm3-3b-base-blindspots
SmolLM3-3B-Base Blind Spots Dataset
This dataset documents systematic failure cases ("blind spots") observed while evaluating the base pretrained model:
Model tested: https://huggingface.co/HuggingFaceTB/SmolLM3-3B-Base
This is the base model after pretraining (not instruction-tuned).
1. How the Model Was Loaded
The model was evaluated in Google Colab using transformers>=4.53.0.
!pip install -U transformers accelerate datasets huggingface_hub
import torch
from… See the full description on the dataset page: https://huggingface.co/datasets/Karimsh/smollm3-3b-base-blindspots.smollm3-blind-spots
Blind spots of SmolLM3-3B-Base on simple instructions
Dataset description
This dataset collects failure cases (“blind spots”) of the HuggingFaceTB/SmolLM3-3B-Base language model on very simple prompts.The prompts cover factual questions, arithmetic, comparisons, translation, and strict instruction-following such as “answer with one word only” or “yes or no only”.
Each row records:
the input prompt,
the expected output,
the model’s actual output, and
a short error type… See the full description on the dataset page: https://huggingface.co/datasets/yaqeenalradi/smollm3-blind-spots.