datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Muse-Glimmer-30B-GGUF-metrics
Muse Glimmer 30B GGUF — raw metrics
Every log behind the numbers in
AtomicChat/Muse-Glimmer-30B-GGUF.
Published unfiltered, so any figure in the model card can be checked or disputed.
Layout
Path
Contents
kld/
llama-perplexity --kl-divergence output, per build and per corpus
bench/
llama-bench -o json
speculative/
llama-server logs with and without the drafter
layouts/
per-tensor type map of every GGUF
conversion/
convert_hf_to_gguf.py logs… See the full description on the dataset page: https://huggingface.co/datasets/AtomicChat/Muse-Glimmer-30B-GGUF-metrics.gguf-models
GGUF Models Collection - 多格式版本
這個倉庫包含多種量化格式的 GGUF 模型檔案。
格式說明
格式
描述
品質
檔案大小
推薦用途
FP16
16位浮點
最高
最大
高精度推理、微調基準
Q8_0
8位量化
高
中等
高品質推理、伺服器部署
Q4_K_M
4位混合量化
良好
最小
本地部署、快速推理
轉換摘要
📊 成功轉換: 3/4 個模型
📈 成功率: 75.0%
🔧 支援格式: FP16, Q8_0, Q4_K_M
🕒 更新時間: 2025-08-29 04:34:43
模型列表
模型名
FP16
Q8_0
Q4_K_M
狀態
deepseek-1.3b-sql-final-t4x2
2569.5MB
N/A
N/A
✅ 成功
codegemma-2b-sql-coder-finetuned
4786.0MB
N/A
N/A
✅ 成功… See the full description on the dataset page: https://huggingface.co/datasets/Paul720810/gguf-models.wikipedia-viewerwikipedia dataset, now with viewer enabled! :D
Dataset Card for Wikipedia
Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
The articles are parsed using the mwparserfromhell tool, which can be… See the full description on the dataset page: https://huggingface.co/datasets/GGUFGuy/wikipedia-viewer.Ornith-1.5-9B-GGUF-metricsQwen3.8-Flash-Next-GGUF-metricsagentic-safety-gguf
agentic-safety-gguf: Training & Evaluation Datasets
Model: guerilla7/agentic-safety-ggufPaper: (https://arxiv.org/abs/2601.00848)Total: 80,992 examples (80,851 after deduplication)
Overview
Complete training and evaluation datasets for agentic-safety-gguf, a specialized Llama 3.1 8B model for agentic AI security analysis. Supports iterative continuation training methodology (V2→V3→V4) for full reproducibility.
Dataset Files
File
Examples
Size
Purpose… See the full description on the dataset page: https://huggingface.co/datasets/guerilla7/agentic-safety-gguf.qwen3-5-gguf-tiny-fidelity-root-v1
qwen35-gguf random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/qwen3-5-gguf-tiny-random-bf16.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/qwen3-5-gguf-tiny-fidelity-root-v1.qfs-qwen-gguf-tiny-cpu-format-v1
qwen-gguf tiny CPU format fixture reproducibility
Complete tiny random FORMAT fixture evidence. Round-to-nearest (RTN) storage/reader exercise only; optimizer-not-run. No GPTQ/AWQ/AutoRound optimization, calibrated ModelOpt/CT/QAT quality, trained-model quality ranking, GPU parity, or native serving-kernel correctness claim.
Reconstructed weights are evaluated by the captured native forward. KL is own-head, full-vocabulary on the recorded panel, not a benchmark of training… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/qfs-qwen-gguf-tiny-cpu-format-v1.glm53-fidelity-gguf-unsloth-udq4kxl-v1
fidelity--glm53.malaiwah.quant.gguf-unsloth-udq4kxl
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from unsloth/GLM-5.3-GGUF.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm53-fidelity-gguf-unsloth-udq4kxl-v1.translategemma-4b-it-Q4_K_M-GGUF
TranslateGemma 4B IT Q4_K_M GGUF
This repository contains a GGUF Q4_K_M conversion of Google TranslateGemma 4B IT.
Model information
Base model: google/translategemma-4b-it
Source revision: 10042cb0e6e7fdce748996a71dc3dc432a4e0c89
llama.cpp revision used in the project validation: 2048b5913d51beab82dfe29955f9008130b936c0
Artifact filename: translategemma-4b-it-Q4_K_M.gguf
Size: 2489909120 bytes
SHA-256:… See the full description on the dataset page: https://huggingface.co/datasets/ctc88haha/translategemma-4b-it-Q4_K_M-GGUF.glm52-fidelity-gguf-unsloth-udq4kxl-v1
fidelity--glm52.malaiwah.quant.gguf-unsloth-udq4kxl
A quant fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from unsloth/GLM-5.2-GGUF.
The cut
the final hidden state handed to lm_head -- after the text model's final norm and immediately before the head matmul -- captured as the head module's input via torch.nn.Module.register_forward_pre_hook; replay applies the head ONLY (no final norm at replay time: the capture already sits after it).… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm52-fidelity-gguf-unsloth-udq4kxl-v1.gguf-statseval_gua-a_v0.1-ft_mistral-7b_GGUFaquiles-gguf-registry
Aquiles GGUF Registry
Manifest of GGUF models tested and compatible with Aquiles-Image.
This repository does not host weights. It only contains a manifest (registry.json) that points to GGUF checkpoints already published by third parties (city96, QuantStack, etc.) along with the exact configuration needed to load them in Aquiles-Image: base diffusers repo, transformer class, and pipeline class.
Josephgflowers__Cinder-Phi-2-V1-F16-gguf-details
Dataset Card for Evaluation run of Josephgflowers/Cinder-Phi-2-V1-F16-gguf
Dataset automatically created during the evaluation run of model Josephgflowers/Cinder-Phi-2-V1-F16-gguf
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Josephgflowers__Cinder-Phi-2-V1-F16-gguf-details.MultivexAI__Phi-3.5-Mini-Instruct-MultiVex-v0.25-GGUFQwen3.6-27B-OTQ-GGUF-benchmarks
Qwen3.6-27B OTQ GGUF Benchmark Reproducibility
This dataset contains the compact paired benchmark evidence used by zlaabsi/Qwen3.6-27B-OTQ-GGUF.
It is a reproducibility dataset, not a leaderboard dataset. The rows are small practical release signals run on pinned task IDs with prompt format qwen3-no-think, deterministic decoding and local scoring rules.
Contents
Path
Meaning
data/paired_samples.jsonl
Flattened 232-row paired sample table with prompts, task… See the full description on the dataset page: https://huggingface.co/datasets/zlaabsi/Qwen3.6-27B-OTQ-GGUF-benchmarks.eval_gua-a_v0.2-dpo_mistral-7b_GGUFgguf-indexjarvis_lLAMA3-7b-GGUFgguf-name-agg-truegpt-oss-20b-gguf-datasetMultivexAI__Phi-3.5-Mini-Instruct-MultiVex-v0.25-GGUF-details
Dataset Card for Evaluation run of MultivexAI/Phi-3.5-Mini-Instruct-MultiVex-v0.25-GGUF
Dataset automatically created during the evaluation run of model MultivexAI/Phi-3.5-Mini-Instruct-MultiVex-v0.25-GGUF
The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task.
The dataset has been created from 2 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train"… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/MultivexAI__Phi-3.5-Mini-Instruct-MultiVex-v0.25-GGUF-details.LFM2.5-8B-A1B-GGUF-MoQ-COMPARISON-CGGUF_TestDataHUGGINGFACE_TRENDING_GGUFS_LIST
Dataset Card for Dataset Name
This dataset provides an easier way to find useful models and get their repo and direct access to GGUF Q5_K_M model files.
The purpose is to easily set up local model with llama-cpp-python.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More… See the full description on the dataset page: https://huggingface.co/datasets/alihmaou/HUGGINGFACE_TRENDING_GGUFS_LIST.LFM2.5-8B-A1B-GGUF-MoQ-COMPARISON-BLFM2.5-8B-A1B-GGUF-MoQ-COMPARISON-Dgguf-agg-countscalm3-22b_gguf_eval