datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ChartNet
ChartNet: A Million-Scale Multimodal Dataset for Chart Understanding
🌐 Homepage | 📖 arXiv
📝 Changelog
June 3, 2026 — Release of grounded_qa subset and completed reasoning subset (both subject to Notice Regarding Data Availability)
May 15, 2026 — Added link to 30K real-world charts and detailed captions dataset released by our collaborators Abaka AI/2077AI.
April 29, 2026 — Release of an additional 2.5 million row subset core_permissive (subject to… See the full description on the dataset page: https://huggingface.co/datasets/ibm-granite/ChartNet.zelo-scores-10kx100-granite-4.1-30b
Dataset Card for tomaarsen/zelo-scores-10kx100-granite-4.1-30b
Dataset Summary
Synthetic data generated by DataForge:
Model: ibm-granite/granite-4.1-30b (main)
Source dataset: tomaarsen/zelo-pairs-10kx100-quantile-anchor (train split).
Generation config: temperature=None, top_p=None, top_k=None, max_tokens=4096, model_max_context=32768
Speculative decoding: disabled
System prompt: `You are a relevance scoring system. Given a query and two documents (A and B), your job… See the full description on the dataset page: https://huggingface.co/datasets/tomaarsen/zelo-scores-10kx100-granite-4.1-30b.granite-4-blind-spots
I used ChatGpt to get some examples and help me for arrange DatasetCard
🔍 Granite-4.0-Micro-Base — Blind Spots Dataset
A curated evaluation dataset of 25 diverse prompts designed to expose failure patterns
("blind spots") in the ibm-granite/granite-4.0-micro-base
language model. Each row contains the input prompt, the correct expected output, and the
model's actual output.
📦 Model Tested
Field
Details
Model
ibm-granite/granite-4.0-micro-base… See the full description on the dataset page: https://huggingface.co/datasets/ahmedshark/granite-4-blind-spots.Granite-v4.1-Distilled-15K
⛰️ Granite-v4.1-Distilled-15K
Dataset Summary
Granite-v4.1-Distilled-15k is a supervised fine-tuning dataset for logic-oriented distillation. The prompts to the questions come from Jackrong/GLM-5.1-Reasoning-1M-Cleaned, and the answers were generated using the only granite-4.1-8b teaching model.
Dataset Details
Dataset
constructai/Granite-v4.1-Distilled-15K
Source questions
Jackrong/GLM-5.1-Reasoning-1M-Cleaned
Teacher model
Granite-4.1-8b… See the full description on the dataset page: https://huggingface.co/datasets/constructai/Granite-v4.1-Distilled-15K.granite-base-model-errors
Granite-1B Base Model Errors
Overview
This dataset contains 10 examples where the Granite-4.0-1B-Base language model produces incorrect or awkward outputs. Each row includes:
id: a unique identifier for each example
input: the prompt given to the model
expected_output: what the correct answer or completion should be
model_output: what the model actually produced
The dataset demonstrates common blind spots of a base causal language model, including factual errors, logic… See the full description on the dataset page: https://huggingface.co/datasets/thatgirltomiie/granite-base-model-errors.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.granite-1b-base-blindspots
Blind Spots of ibm-granite/granite-4.0-h-1b-base
Model tested
Model: https://huggingface.co/ibm-granite/granite-4.0-h-1b-base
This dataset contains inputs where the model produced incorrect outputs, along with the expected output and the model's output.
How the model was loaded (Colab / Transformers)
!pip -q install -U transformers accelerate datasets huggingface_hub
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id =… See the full description on the dataset page: https://huggingface.co/datasets/hanaandargie/granite-1b-base-blindspots.