datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tinystories-icr-data-v2glm-moe-dsa-tiny-cpu-repro-v1
Tiny GLM MoE DSA: two CPU captures, forced zero-KL replay
Reproducibility evidence for
malaiwah/glm-moe-dsa-tiny-random-bf16,
checkpoint/config/tokenizer revision 45563636ef723acfb826755493447dc40c7a0c37.
This is a synthetic pipeline test, not a quality benchmark, quantization measurement,
qualified production reference, or registry submission. The model is random-init.
No GPU or paid cloud job was used.
Observed result
Two fresh capture processes, two CPU… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm-moe-dsa-tiny-cpu-repro-v1.tinystories_dataset_arabicqwen3-5-tiny-fidelity-root-v1
qwen3_5 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/qwen3-5-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). Same cut… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/qwen3-5-tiny-fidelity-root-v1.qwen4-exp-tiny-fidelity-root-v1
qwen4_exp random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/qwen4-exp-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). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/qwen4-exp-tiny-fidelity-root-v1.deepseek-v4-tiny-fidelity-root-v1
deepseek-v4 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/deepseek-v4-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/deepseek-v4-tiny-fidelity-root-v1.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.k2-horizon-tiny-fidelity-root-v1
k2-horizon random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/k2-horizon-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/k2-horizon-tiny-fidelity-root-v1.glm5-next-tiny-fidelity-root-v1
glm5_next random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/glm5-next-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). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/glm5-next-tiny-fidelity-root-v1.minimax-m2-tiny-fidelity-root-v1
minimax-m2 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/minimax-m2-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/minimax-m2-tiny-fidelity-root-v1.kimi-k25-tiny-fidelity-root-v1
kimi-k25 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/kimi-k25-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). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/kimi-k25-tiny-fidelity-root-v1.minimax-m3-tiny-fidelity-root-v1
minimax-m3 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/minimax-m3-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/minimax-m3-tiny-fidelity-root-v1.glm-moe-dsa-tiny-fidelity-root-v1
glm_moe_dsa random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/glm-moe-dsa-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/glm-moe-dsa-tiny-fidelity-root-v1.kimi-k3-tiny-fidelity-root-v1
kimi-k3 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/kimi-k3-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). Same cut… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/kimi-k3-tiny-fidelity-root-v1.origami-direct-tiny
Origami Direct Crease Pattern Dataset
A multiview image dataset for training models to predict complete origami crease patterns from 3D visualizations.
Task
Given 14 camera views of a folded origami shape, predict the complete crease pattern as a FOLD JSON (vertices, edges, mountain/valley assignments).
Dataset Structure
Each example contains:
Field
Type
Description
id
string
Unique sample ID (e.g., grid4_4c_0000)
images
list[string]
14 PNG paths —… See the full description on the dataset page: https://huggingface.co/datasets/Origametry/origami-direct-tiny.varianthound-data
VariantHound Research Snapshot
VariantHound is an explainable, phenotype-aware research system for ranking candidate genes in canine inherited disease. This dataset repository contains the compact, redistributable artifacts generated from the public VariantHound source repository.
Research and educational use only. This snapshot is not a diagnostic dataset or medical device, and its rankings require expert review and experimental validation.
Current contents
The… See the full description on the dataset page: https://huggingface.co/datasets/Tinyants21/varianthound-data.spark2-5-tiny-fidelity-root-v1
spark2-5 random CPU fixture root
A root fidelity dataset in hidden form, produced by engines/tools/hf_capture.py from malaiwah/spark2-5-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). Same… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/spark2-5-tiny-fidelity-root-v1.Josephgflowers__TinyLlama_v1.1_math_code-world-test-1-details
Dataset Card for Evaluation run of Josephgflowers/TinyLlama_v1.1_math_code-world-test-1
Dataset automatically created during the evaluation run of model Josephgflowers/TinyLlama_v1.1_math_code-world-test-1
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"… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/Josephgflowers__TinyLlama_v1.1_math_code-world-test-1-details.tinycompany__ShawtyIsBad-ib-details
Dataset Card for Evaluation run of tinycompany/ShawtyIsBad-ib
Dataset automatically created during the evaluation run of model tinycompany/ShawtyIsBad-ib
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 the latest results.
An… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/tinycompany__ShawtyIsBad-ib-details.tiny-vintage-completions
Tiny vintage completions
Synthetic vintage texts, with a cutoff date for year 1900.
Based on unique 2-3 word seeds, extracted from croqaz/Vintage-v1, croqaz/Vintage-v2 and Haykgrigorian/English-historical-corpus-1800-1875.
Check the files seeds1.txt and seeds2.txt.
Generated by TypeWriter-7B-base and Talkie-13B-base completions.
Citation
If you find this dataset valuable, please consider citing:
@misc{Tiny-vintage-completions,
title = {Tiny vintage completions}… See the full description on the dataset page: https://huggingface.co/datasets/croqaz/tiny-vintage-completions.TinyNarrator-agent-tracesSpaces link: https://huggingface.co/spaces/build-small-hackathon/TinyNarrator
TinyLlama__TinyLlama-1.1B-Chat-v0.1-details
Dataset Card for Evaluation run of TinyLlama/TinyLlama-1.1B-Chat-v0.1
Dataset automatically created during the evaluation run of model TinyLlama/TinyLlama-1.1B-Chat-v0.1
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 the latest… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/TinyLlama__TinyLlama-1.1B-Chat-v0.1-details.tinyperson-yolov8n-oacp-load-adaptive-022e5d9mathpile_arxiv_subset_tiny
MathPile ArXiv (subset)
Description
This dataset consists of a toy subset of 8834 (5000 training + 3834 testing) TeX files found in the arXiv subset of MathPile, used for testing. You should not use this dataset. Training and testing sets are already split
Source
The data was obtained from the training + validation portion of the arXiv subset of MathPile.
Format
Given as JSONL files of JSON dicts each containing the single key: "text"
Usage… See the full description on the dataset page: https://huggingface.co/datasets/aluncstokes/mathpile_arxiv_subset_tiny.tinygiant-omni-test-featuresorigami-step-by-step-tiny
Origami Step-by-Step Crease Pattern Dataset
A multiview image dataset for training models to infer origami crease patterns from 3D visualizations, one fold at a time.
Task
Given 14 camera views of a partially-folded origami sheet, predict the next crease line to add (edge position + mountain/valley assignment).
This mirrors a step-by-step folding process: starting from a blank sheet, each step adds one crease and the model must predict the next one from the current 3D… See the full description on the dataset page: https://huggingface.co/datasets/Origametry/origami-step-by-step-tiny.tinyperson-yolov8n-p2p3p4-m5-cp3-runstinyperson-yolov8n-p2p3p4-oacp-mass-cp3-mosaictinyperson-yolov8n-p2p3p4-oacp-fixedsplit42-0a2ca54-seed43tinyperson-yolov8n-oacp-mass-adaptive-022e5d9