datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
prolong-smollm2-validation
ProLong SmolLM2 validation
Two validation sets for evaluating DCLM-trained language models, retokenized from the code, books, and textbooks subsets of princeton-nlp/prolong-data-64K.
Folder
Context length
Sequences
Usable tokens
Stored tokens
Trailing EOS filler (unused)
4k/
4,096
24,414
99,999,744
100,000,000
256
32k/
32,768
3,051
99,975,168
100,000,000
24,832
Each folder contains prolong_val_100m.bin, per-sequence source metadata in prolong_val_100m.json, and… See the full description on the dataset page: https://huggingface.co/datasets/sycmucmu/prolong-smollm2-validation.ai-vs-human-HuggingFaceTB-SmolLM2-1.7B-Instruct
AI vs Human dataset on the CNN Daily mails
Dataset Description
This dataset showcases pairs of truncated articles and their respective completions, crafted either by humans or an AI language model.
Each article was randomly truncated between 25% and 50% of its length.
The language model was then tasked with generating a completion that mirrored the characters count of the original human-written continuation.
Data Fields
'human': The original human-authored… See the full description on the dataset page: https://huggingface.co/datasets/zcamz/ai-vs-human-HuggingFaceTB-SmolLM2-1.7B-Instruct.hagi-fineweb-edu-smollm2
HAGI - Tokenized HuggingFaceFW/fineweb-edu (SmolLM2-135M tokenizer)
Pre-tokenized token-id shards used to train the HAGI
Stage 0 baseline and the Grade-Decomposed Recurrence ablation (models A/B/C/D). A
tokenized derivative of HuggingFaceFW/fineweb-edu subset sample-10BT,
published so the exact training corpus loads identically in any environment (Colab,
Kaggle, local) with no re-tokenization and no Google Drive access.
Format - read before using
Files:… See the full description on the dataset page: https://huggingface.co/datasets/NAME0x0/hagi-fineweb-edu-smollm2.traffic-accidents-reports-kd-smollm2-360M-7k
Accident Reporting KD Dataset (One-Paragraph)
Short description.A training/evaluation dataset for generating one-paragraph accident/incident reports from structured facts.This dataset mixes gold human targets from zBotta/traffic-accidents-reports-5k with teacher-generated soft targets produced by the model zBotta/smollm2-accident-reporter-360m to support knowledge distillation (KD) of a smaller student.
Output style: a single paragraph, neutral tone, covering What, When, Where, Who… See the full description on the dataset page: https://huggingface.co/datasets/DSTI/traffic-accidents-reports-kd-smollm2-360M-7k.ai-vs-human-HuggingFaceTB-SmolLM2-360M-Instruct
AI vs Human dataset on the CNN Daily mails
Dataset Description
This dataset showcases pairs of truncated articles and their respective completions, crafted either by humans or an AI language model.
Each article was randomly truncated between 25% and 50% of its length.
The language model was then tasked with generating a completion that mirrored the characters count of the original human-written continuation.
Data Fields
'human': The original human-authored… See the full description on the dataset page: https://huggingface.co/datasets/zcamz/ai-vs-human-HuggingFaceTB-SmolLM2-360M-Instruct.smollm2-blind-spots
SmolLM2-1.7B Blind Spots Dataset
This dataset contains 10 diverse examples where the SmolLM2-1.7B base model makes incorrect predictions or demonstrates "blind spots".
Model Tested
Model: SmolLM2-1.7B
Parameters: 1.7 Billion
Type: Base (Pre-trained)
How to Load the Model
The model was loaded using the transformers library in Python.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "HuggingFaceTB/SmolLM2-1.7B"
tokenizer =… See the full description on the dataset page: https://huggingface.co/datasets/Alhibb/smollm2-blind-spots.gemma-3n-4b-distill-smollm2-360m-instruct-425xTrace of Gemma 3n 4B Distill SmolLM2 360M Instruct LLM by sapbot (me).
Data count (Total: 425):
English - 209
Russian - 216
Data is presented in ShareGPT format and each conversation split by newline.
Note: This was added more as a "examples" of this model's outputs. Of course you will not distill a distilled model (I hope).
Brought to you by sapbot from Romarchive
smollm2-1.7b-blind-spots
SmolLM2-1.7B Blind Spots Dataset
A curated evaluation dataset documenting specific failure modes of HuggingFaceTB/SmolLM2-1.7B — a 1.7 billion parameter pretrained (base) language model. Each entry contains a completion-style prompt, the verified correct answer, and the model's actual incorrect output produced via deterministic greedy decoding.
This dataset was created as part of the "Blind Spots of Frontier Models" technical challenge to systematically identify where small… See the full description on the dataset page: https://huggingface.co/datasets/Shinzmann/smollm2-1.7b-blind-spots.smollm2-1.7b-blindspots
SmolLM2-1.7B Blind Spots Dataset
A curated dataset of 12 diverse probe examples where the base language model
HuggingFaceTB/SmolLM2-1.7B
makes incorrect or unreliable predictions. Each row contains the raw prompt, the
expected correct answer, the model's actual output (greedy decoding), the error
category, and an explanation.
Tested Model
HuggingFaceTB/SmolLM2-1.7B
Property
Value
Parameters
1.7 billion
Type
Pure base model (pretrained only — no… See the full description on the dataset page: https://huggingface.co/datasets/Shah-4-8-1-2/smollm2-1.7b-blindspots.smollm2-blind-spots
Model Tested
HuggingFaceTB/SmolLM2-1.7B
How I loaded it
Used HuggingFace Transformers with AutoModelForCausalLM on Google Colab (T4 GPU, float16). Greedy decoding (do_sample=False) for reproducibility.
View Colab Notebook
Blind Spots Found
The model struggled with: multi-step arithmetic, low-resource languages (Yoruba), African geographic knowledge, code generation, and logical reasoning.
Fine-tuning Dataset Recommendation
GSM8K / MATH for… See the full description on the dataset page: https://huggingface.co/datasets/mirackchuks/smollm2-blind-spots.smollm2-blindspots
Blind Spots Dataset for SmolLM2-1.7B
Model Tested
Model: SmolLM2-1.7B
Parameters: 1.7B
Release Date: February 2025
How I Loaded the Model
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "HuggingFaceTB/SmolLM2-1.7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype="auto"
)
def test_model(prompt, max_new_tokens=100):
inputs =… See the full description on the dataset page: https://huggingface.co/datasets/Muhammad0981/smollm2-blindspots.smollm2-dpo-preferences
DPO Preferences Dataset (Restricted Access)
Access Policy (Restricted)
This dataset repo is public with manual gated access.
Only approved users (from lums.edu.pk) will be granted access.
Intended Use
Preference optimization / DPO experiments for model alignment.
Research and controlled evaluation.
Out-of-Scope Use
Any harmful, abusive, or policy-violating application.
Safety-critical deployment without additional safeguards.
Files… See the full description on the dataset page: https://huggingface.co/datasets/Amin-AQ/smollm2-dpo-preferences.