datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.