datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tiny-aya-global-evaluation
Tiny-Aya-Global Reasoning Blind Spots (TAG-RBS)
This diagnostic dataset identifies the logical, mathematical, and constraint-satisfaction "blind spots" of the Tiny-Aya-Global (3.35B) model. It was manually constructed to test the boundary conditions of compact multilingual models and evaluate their susceptibility to post-hoc rationalization.
Dataset Overview
Dataset Size: 50 hand-crafted prompts.
Evaluation Target: CohereLabs/tiny-aya-global (3.35B parameters).… See the full description on the dataset page: https://huggingface.co/datasets/yonasachule/tiny-aya-global-evaluation.tiny-aya-blind-spots
Dataset: Tiny-Aya-Base Blind Spots
This dataset was created as part of a technical challenge to identify the blind spots of the models. It specifically targets CohereLabs/tiny-aya-base. The model is a 3.35B parameter multilingual base model released in early 2026.
Model Tested
Model: CohereLabs/tiny-aya-base
Parameters: 3.35 Billion
Modality: Text
How the Model was Loaded
The model was loaded using the transformers library on a Google Colab T4 GPU.… See the full description on the dataset page: https://huggingface.co/datasets/osamaahmed17/tiny-aya-blind-spots.ignatius-tiny-aya-analysis
Tiny Aya Base — Failure Analysis Dataset
Model Tested
Model: CohereLabs/tiny-aya-base
Parameters: 3.35B
Released: February 17, 2026
Architecture: Dense decoder-only Transformer, pretrained on 6T tokens across 70+ languages
How I Loaded the Model
I used Modal (free tier, T4 GPU — 16GB VRAM) to run inference.
import modal
app = modal.App("tiny-aya-probe")
image = modal.Image.debian_slim(python_version="3.11").pip_install(
"torch", "transformers>=4.51.0"… See the full description on the dataset page: https://huggingface.co/datasets/IgnatiusBalayo2024/ignatius-tiny-aya-analysis.