validation
quantum-like-attention-framework-1.3b-untuned-validation
Quantum-Like Attention Framework (QLAF) 1.3B Untuned Pretraining & Scaling Proof
This repository hosts the pretraining checkpoints, scaling logs, and downstream evaluation benchmarks for the 1.3B parameter Quantum-Like Attention Framework (QLAF) with Hybrid FlashAttention (75% recurrent QLAF / 25% causal FlashAttention) across a 3-seed validation campaign on dedicated A100 Large GPU hardware.
Multi-Seed Pretraining & Downstream Evaluation Leaderboard
Seed… See the full description on the dataset page: https://huggingface.co/datasets/IgnisCogitationis/quantum-like-attention-framework-1.3b-untuned-validation.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.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.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.Imagenet-1k_validationQuantiPhy-validation
QuantiPhy (Validation Set)
Dataset Summary
QuantiPhy is a benchmark for evaluating whether vision–language models (VLMs) can perform quantitative physical inference from visual evidence, rather than producing plausible but ungrounded numerical guesses.
This repository contains the official validation set of QuantiPhy, released to support model development, ablation studies, and preliminary evaluation.The validation set represents approximately 4% of the full… See the full description on the dataset page: https://huggingface.co/datasets/PaulineLi/QuantiPhy-validation.