datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
hemmingway-1-omlx-quantization-evidence-v2
Hemmingway-1 Quantization Evidence v2
This package records two local evidence lanes for the Hemmingway-1 oQ4e build: teacher-forced numerical fidelity against a BF16 reference, and controlled runtime telemetry on Apple Silicon. It complements the frozen blind-preference study in Hemmingway-1 oMLX Quantization Benchmark v1.
This dataset is sixstringzen/hemmingway-1-omlx-quantization-evidence-v2. The quality dataset remains unchanged because blind preference, distribution fidelity… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-evidence-v2.hemmingway-1-omlx-quantization-benchmark-v1
Hemmingway-1 oMLX Quantization Benchmark
This is the public-safe benchmark package for the Hemmingway-1 oMLX
quantization study on Apple Silicon.
Altworld developed and published
Hemmingway-1. Bobby Pierce
published these quantizations and the evaluation package. The
collection
links the upstream model and all six builds.
Analysis revision 2, corrected on 2026-09-22, fixes A/B attribution and matching
across reversed packets. Read CORRECTION.md before using the
aggregate… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-benchmark-v1.p2026-002-quantization-context-compression-results
Deployed Quantization Tier and Lossy Context Compression in Extractive QA
This result dataset mirrors the version-1.0.0 reproducibility artifact:
10.5281/zenodo.22847291.
The versioned report and full replication sources
are maintained together in the research-artifacts repository. Cite the exact
Zenodo version for the frozen evidence; this Hugging Face copy is a discovery
mirror.
Matthew Schwartz — ORCID 0009-0009-4171-7247
This dataset is the aggregate-only evidence for "No… See the full description on the dataset page: https://huggingface.co/datasets/mv1137/p2026-002-quantization-context-compression-results.quantization-benchmarksr3al-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.
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.diffusers-quantization-benchmarksh200-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.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.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.fp8-quantizationEXAONE-4.0-1.2B-Quantization-MMLUquantization_experiment_results