datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Inkling-Small-Multimodal-Calibration
Inkling-Small Multimodal Calibration
The exact 1,663 samples used for BF16 routed-expert importance collection
for Inkling-Small Mixed Quant GGUF.
This is calibration material, not a held-out evaluation benchmark.
The primary balanced pass is:
Category
Samples
Valid decoder tokens
Share
Text / reasoning
462
471,858
44.976%
Code / tool-oriented source text
205
209,715
19.989%
Real image / document
486
262,476
25.018%
Real speech audio
309
105,080
10.016%
Total… See the full description on the dataset page: https://huggingface.co/datasets/Baekpica/Inkling-Small-Multimodal-Calibration.ptq-calibration-corpus
PTQ Calibration Corpus
A compact, high-density corpus built specifically for post-training
quantization of chat, coding, and agentic language models.
This is not random web text and it is not a repackaged benchmark. The corpus
was deliberately assembled to exercise varied scientific and technical prose,
numeric structure, multilingual code, tool schemas, long debugging
trajectories, CUDA/Triton vocabulary, correctness recovery, and
architecture-sensitive optimization.
This is… See the full description on the dataset page: https://huggingface.co/datasets/RESMP-DEV/ptq-calibration-corpus.Qwen2.5-7B-Instruct-Self-Calibration
Efficient Test-Time Scaling via Self-Calibration
This repository contains datasets used in the paper Efficient Test-Time Scaling via Self-Calibration. The datasets are used to evaluate the effectiveness of test-time scaling methods for LLMs. Each config_name in the metadata refers to a different reasoning dataset. More detailed descriptions of each dataset are needed. Consider adding a section for each config_name with a description, statistics, and any other relevant… See the full description on the dataset page: https://huggingface.co/datasets/HINT-lab/Qwen2.5-7B-Instruct-Self-Calibration.less-is-moe-s1-calibration-128-seq8192
Less-is-MoE S1K calibration data — 128 samples, seq_length 8192
This is the fixed calibration artifact used to prune GPT-OSS-120B,
Qwen3.5-122B-A10B, and the Gemma-4-26B-A4B causal language tower. It uses the same 128 source rows as the full-length variant:
yentinglin/s1K-1.1-trl-format revision
58a01564d278477da20ead1bcf1cde8e31f36251, train, followed by
Dataset.shuffle(seed=1234) and the first 128 nonempty messages rows.
For pruning, concatenate messages[].content with one… See the full description on the dataset page: https://huggingface.co/datasets/jayzou3773/less-is-moe-s1-calibration-128-seq8192.less-is-moe-s1-calibration-128
Less-is-MoE S1K calibration data — 128 full-length samples
This repository contains the exact 128 S1K rows selected for Less-is-MoE
full-model pruning. The selection reproduces the released loader:
source: yentinglin/s1K-1.1-trl-format
revision: 58a01564d278477da20ead1bcf1cde8e31f36251
split: train
order: Dataset.shuffle(seed=1234)
samples: first 128 nonempty messages rows
sequence-length limit: none
truncation: disabled
padding: disabled
calibration.jsonl stores every… See the full description on the dataset page: https://huggingface.co/datasets/jayzou3773/less-is-moe-s1-calibration-128.qwen3.5-moe-awq-calibration
Qwen3.5 MoE AWQ Calibration Dataset
Calibration dataset for AWQ (Activation-Aware Weight Quantization) of
Qwen/Qwen3.5-35B-A3B and
Qwen/Qwen3.5-35B-A3B-Base.
Designed for MoE expert routing diversity: Qwen3.5-35B-A3B has 256 experts with 8
active per token, so calibration data needs broad domain coverage to exercise as many
routing paths as possible.
Sampling methodology
Source: PleIAs/common_corpus
(open multi-domain corpus with labeled collections)
Filtering:
Token… See the full description on the dataset page: https://huggingface.co/datasets/Lambent/qwen3.5-moe-awq-calibration.