datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Ornith-1.5-9B-GGUF-metricsQwen3.8-Flash-Next-GGUF-metricsqwen3-5-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.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.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.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-statsJosephgflowers__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.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.HUGGINGFACE_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.RAG12000-LLaMA3.1-8B-gguf_AR-RAG_v2EDIATH-Trador-GGUF