validation
Qwen3-30B-A3B-ShapleyMCG-K34-Validation-ReconstructionKielGen-Codex-4B-Validation-GGUFDocument-Validation-Qwen2.5-VL-Simple-V2-i1-GGUFDocument-Validation-Qwen2.5-VL-Resize-2048-Simple-V2-i1-GGUFDocument-Validation-Qwen2.5-VL-Simple-V2-GGUFNorth-Mini-Code-1.0-ik-llama-validation-GGUFDocument-Validation-Qwen2.5-VL-Resize-2048-Simple-V2-GGUFTinyStories-validationset
aimo-validation-aime
Dataset Card for AIMO Validation AIME
All 90 problems come from AIME 22, AIME 23, and AIME 24, and have been extracted directly from the AOPS wiki page https://artofproblemsolving.com/wiki/index.php/AIME_Problems_and_Solutions
This dataset serves as an internal validation set during our participation in the AIMO progress prize competition. Using data after 2021 is to avoid potential overlap with the MATH training set.
Here are the different columns in the dataset:
problem: the… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/aimo-validation-aime.quantum-like-attention-framework-1.3b-untuned-validation
Quantum Like Attention Framework (Q.L.A.F) 1.3b untuned
This repository contains the model checkpoints, downstream evaluation scores, and pretraining convergence logs for the Quantum Like Attention Framework (Q.L.A.F) 1.3B configuration.
Key Specifications & Architecture
Model Name: Q.L.A.F 1.3b untuned (Quantum Like Attention Framework - Hybrid Architecture)
Parameters: 1.3B parameters total configuration (327M active parameter student subset)
Layer Count: 12… See the full description on the dataset page: https://huggingface.co/datasets/IgnisCogitationis/quantum-like-attention-framework-1.3b-untuned-validation.aimo-validation-amc
Dataset Card for AIMO Validation AMC
All 83 come from AMC12 2022, AMC12 2023, and have been extracted from the AOPS wiki page https://artofproblemsolving.com/wiki/index.php/AMC_12_Problems_and_Solutions
This dataset serves as an internal validation set during our participation in the AIMO progress prize competition. Using data after 2021 is to avoid potential overlap with the MATH training set.
Here are the different columns in the dataset:
problem: the modified problem statement… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/aimo-validation-amc.esb-datasets-earnings22-validation-tiny-filteredA filtered (<=30s duration) slice (512 samples) of the Earnings22 dataset.
def add_duration(sample):
y, sr = sample['audio']["array"], sample['audio']["sampling_rate"]
sample['duration_ms']=librosa.get_duration(y=y, sr=sr) * 1000
return sample
tedlium = load_dataset("esb/datasets", "earnings22", split='validation', trust_remote_code=True)
# compute duration to filter
tedlium = tedlium.map(add_duration)
tedlium = tedlium.select(range(512))
# Whisper max supported duration
tedlium… See the full description on the dataset page: https://huggingface.co/datasets/D4nt3/esb-datasets-earnings22-validation-tiny-filtered.Imagenet-1k_validationaimo-validation-math-level-5
Dataset Card for AIMO Validation MATH Level 5
A subset of level 5 problems from https://huggingface.co/datasets/lighteval/MATH
We have extracted the final answer from boxed, and only keep those with integer outputs.