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.quantization-cache-amplification
Quantization as Cache Amplification
Trillion-Parameter Mixture-of-Experts Inference on a Commodity Laptop
Kavin Kumar, Neural Metrics
📄 Read the paper — 11 pages
What this is
Weight quantization is usually justified as footprint reduction. This work argues
that for offloaded mixture-of-experts inference that framing misses the leverage.
The binding resource is not storage capacity but the fraction of expert slots
resident in DRAM — and storage traffic depends on… See the full description on the dataset page: https://huggingface.co/datasets/mkvn/quantization-cache-amplification.tamperbench-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.
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.repro-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.fp8-quantizationquantization-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-for-Thai-llm