datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
frontier-model-blindspots
Application to the Fatimata Institute for Global Research, 2026-2027
Technical Challenge: Blind Spots of Frontier Models
Benjamin Maroyi | benjaminjibu@gmail.com
Overview
This dataset documents 10 blind spots of the base language model
Qwen/Qwen3-0.6B-Base
(0.6 billion parameters, released May 2025, Apache-2.0 license).
Each data point contains a prompt, the expected correct output, and the
model's actual output. The prompts were designed to be diverse across… See the full description on the dataset page: https://huggingface.co/datasets/JibuMaroyi/frontier-model-blindspots.Blind_Spots_of_Frontier_Models
Qwen3-0.6B-Base — Blind Spots Dataset
Model Tested
Qwen/Qwen3-0.6B-Base
Type: Causal Language Model (base / pretraining only — not instruction-tuned)
Parameters: 0.6B (0.44B non-embedding)
Released: April–May 2025 by Alibaba Cloud's Qwen Team
Context Length: 32,768 tokens
How the Model Was Loaded
The model was loaded in a Google Colab T4 GPU notebook using HuggingFace transformers >= 4.51.0(required because the qwen3 architecture key was added in… See the full description on the dataset page: https://huggingface.co/datasets/Pidoxy/Blind_Spots_of_Frontier_Models.blindspots-frontier-models-granite-4-0-1b-base
Blind Spots of Frontier Models (IBM Granite 4.0 1B Base)
Model tested: ibm-granite/granite-4.0-1b-baseModel card: https://huggingface.co/ibm-granite/granite-4.0-1b-base
For inference, I ran this model locally, though I also experimented with free models from OpenRouter.
This dataset contains 10 evaluation rows with:
input
expected_output
model_output
notes
is_correct
I loaded the model with transformers and evaluated it using strict concise-answer prompts.
from transformers… See the full description on the dataset page: https://huggingface.co/datasets/Tomodovodoo/blindspots-frontier-models-granite-4-0-1b-base.