datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
agentic-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.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.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.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.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.aquiles-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.
Qwen3.6-27B-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.MultivexAI__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.calm3-22b_gguf_evalEDIATH-Trador-GGUFdream-catcher-llama3-GGUF