datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
quantization-as-a-transfer-constraint
Quantization as a Transfer Constraint: Zero-Shot Learning-Rate Transfer Survives Low Precision, but muP's Stability Margin Collapses
Author: Shubhankar Kahali - Trumbo Labs, Inc - shubhankar@trumbo.dev
License: CC BY 4.0
Paper: paper/quant_transfer_arxiv.pdf
Abstract
Maximal update parametrization (muP) licenses zero-shot hyperparameter transfer in exact arithmetic, but low-precision training perturbs precisely the coordinate magnitudes muP is designed to keep… See the full description on the dataset page: https://huggingface.co/datasets/xedro98/quantization-as-a-transfer-constraint.tessera-quantization-research-evidence
Tessera Quantization Research Evidence
This dataset is the primary-source measurement evidence from an ongoing research
program studying calibrated low-bit quantization (ternary, int4, vector-quantized
codebooks) for LLM inference on heterogeneous AMD hardware (RDNA3 iGPU, XDNA1/2
NPU, Zen 4/5 CPU). The work is done in a fork of llama.cpp (project name
"Tessera") that adds calibrated per-tensor ternary/payload4/VQ quantization,
NPU offload, and RDNA3-native GPU kernels.
This is… See the full description on the dataset page: https://huggingface.co/datasets/Tribunus-dev/tessera-quantization-research-evidence.quantization-benchmarkstamperbench-quantization-qwen3-4b
TamperBench + Quantization: Does Compression Act as Implicit Tampering?
Motivation
TamperBench evaluates explicit tampering attacks (LoRA fine-tuning, jailbreak-tuning, etc.) on LLM safety guards. Catastrophic Failure of LLM Unlearning via Quantization shows that quantization can undo safety-trained behaviors.
This experiment bridges these two lines of work by adding quantization as a deployment-realistic perturbation to the TamperBench evaluation protocol. We… See the full description on the dataset page: https://huggingface.co/datasets/b0sungk1m/tamperbench-quantization-qwen3-4b.multi-turn-aware-quantization-llama-3.3-rp-testI added role headers and tokens for each turn in the LLaMA 3 Instruct format. The purpose is to test whether formatted multi-turn data can improve multi-turn performance after quantization.
llm-quantization-fine-tuning-2026
⚡ LLM Fine-Tuning, Quantization & Model Optimization Dataset (2023–2026)
This dataset contains 100 sample audit-verified research papers focusing on Large Language Model (LLM) quantization (GPTQ, AWQ, GGUF), fine-tuning (LoRA, QLoRA, PEFT), pruning, distillation, and speculative decoding.
📊 Features:
384-dimensional PyTorch Embeddings (all-MiniLM-L6-v2) for instant Vector Search
NLP Sentence Extraction: Real extracted core problems & key technical innovations… See the full description on the dataset page: https://huggingface.co/datasets/beatsprom/llm-quantization-fine-tuning-2026.r3al-vit-quantization-codex-trace
R3AL ViT Quantization — Codex Agent Trace
Codex session trace for installing the R3AL CLI and agent skill, exporting
google/vit-base-patch16-224 to ONNX, performing dynamic INT8 post-training
quantization on R3AL, and evaluating model size, Apple-arm64 CPU latency, and
prediction fidelity on a 100-image ImageNet validation sample.
The original Codex JSONL format is preserved for Hugging Face's native Agent
Trace viewer. Credential values, email addresses, unrelated Gmail/Slack… See the full description on the dataset page: https://huggingface.co/datasets/nielsr/r3al-vit-quantization-codex-trace.EXAONE-4.0-1.2B-Quantization-MMLUrepro-robuq-pushing-dits-to-w1-58a2-via-robust-activation-quantization-traces
Agent traces
Agent sessions published from a Trackio Logbook.
minicpm5-1b-quantization-benchmark
openbmb/MiniCPM5-1B 次世代量子化（Quanto FP8 / INT4 vs BNB 4bit）実測ベンチマークレポート
対象モデル: openbmb/MiniCPM5-1B (1.16B parameters, 128k context, LlamaForCausalLM)
検証ハードウェア: NVIDIA GeForce RTX 4070 Ti (12GB GDDR6X, Ada Lovelace, Compute Capability 8.9, 第4世代Tensor Core)
実行環境: Windows / Python 3.13 / PyTorch 2.6.0+cu124 / transformers 4.57.6 / optimum-quanto 0.2.7 / bitsandbytes 0.50.0
検証日: 2026-09-19 12:12:34
1. エグゼクティブサマリー（全体比較）
NVIDIA GeForce RTX 4070 Ti 実機環境において、標準ネイティブ… See the full description on the dataset page: https://huggingface.co/datasets/aoiandroid/minicpm5-1b-quantization-benchmark.Eval_dataset_quantizationfp8-quantizationh200-quantization-benchmarks
H200 Quantization Benchmarks
Benchmark results for 40 quantized and non-quantized instruction-tuned LLMs evaluated on an NVIDIA H200 MIG (Multi-Instance GPU) setup. This dataset supports reproducible comparison of quantization methods (AWQ, GPTQ, fp8, bf16) across accuracy and throughput dimensions.
Dataset Configs
Config
Description
Rows
accuracy
Per-task accuracy results from lm-eval across all models
~240
accuracy_leaderboard
Aggregated accuracy… See the full description on the dataset page: https://huggingface.co/datasets/ssakethch/h200-quantization-benchmarks.RuadaptQwen-Quantization-Dataset
Датасет для квантизации RuadaptQwen2.5-32B-instruct с помощью loss-based методов квантизации
Датасет был собран посредством препроцессинга оригинального Vikhrmodels/Grounded-RAG-RU-v2 датасета,a именно: очисткой от HTML, Markdown, лишних пробелов и т.п. с помощью Qwen2.5-14B-Instruct-GPTQ-Int8.
Также после очистки данные обрезаны так, чтобы количество токенов для каждого предложения было строго 512.Токенизация производилась с помощью токенизатора от целевой модели… See the full description on the dataset page: https://huggingface.co/datasets/pomelk1n/RuadaptQwen-Quantization-Dataset.diffusers-quantization-benchmarksquantization_experiment_resultsquantization-guide
Quantization Guide
Reference for choosing the right GGUF quantization level for mobile deployment.
Q4_K_M is the recommended sweet spot — 40% of FP16 size, 92% quality.
🚀 dispatchAI
quantization_samples
Dataset Card for Dataset Name
Calibration dataset for quantization with GPTQ.
Dataset Details
128 2048-token samples from the RedPajama-2 dataset.
quantization-for-Thai-llmquantization-energy-datasetp2-etf-bv-quantization-resultsmmlu_stem_and_health_quantization_calibration