datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
qwen36-27b-gguf-bfcl-v4-quantization-pilot-corrected-v3
Qwen3.6-27B GGUF quantization on a bounded BFCL V4 pilot
Q4_K_M matched Q8_0 on both tested categories: each scored 94 of 100 selected cases correct. Q5_K_M also scored 94/100; Q3_K_M scored 92/100.
Read the results page · Inspect all 400 scored rows
This is a post-result-corrected exploratory analysis of two selected non-live BFCL V4 categories, not a full leaderboard result.
Inspect the scored rows without cloning
The Hub Dataset Viewer does not render this… See the full description on the dataset page: https://huggingface.co/datasets/CyberNative-AI/qwen36-27b-gguf-bfcl-v4-quantization-pilot-corrected-v3.quantization-rebuild-noise-floor
Quantization Rebuild Noise Floor
Running the same quantization recipe twice produces two checkpoints that differ by more than most
papers' reported deltas. This dataset is the measurement.
We quantized Qwen3.8-27B to W4A16 with GPTQ, then ran the exact same recipe a second time —
same model, same settings, same calibration set, only a different quantization run. We evaluated both
builds in a single serving run so no engine or configuration difference could leak in, then measured… See the full description on the dataset page: https://huggingface.co/datasets/ThakiCloud/quantization-rebuild-noise-floor.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.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.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.
The release contains the authored task prompts, selected local execution
metadata, aggregate blind-judge results, reliability metadata, and the policy
used to select records when a condition was run more than once.
What is in the dataset
File
Rows
Purpose
data/train.jsonl
184
Mixed rows. Filter record_type for… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-benchmark-v1.