SAVRN Model Hub
AI Training Datasets
Each dataset with its card, its structure (every split, row count and column type), its files, its license, and the models that disclose training on it.
11,122Models
3,164Datasets
285Papers
6,792Publishers
11,852Sourced relationships
Updated 2026-10-18 · How the library is built
3,164 datasets, sorted by most downloaded.
This is a JPEG-decompressed spatial corpus. It is NOT comparable to BOSSbase, whose covers were never JPEG compressed. Detector numbers measured here and numbers measured on BOSSbase cannot be placed in the same table. A steganalysis corpus of 10,000 permissively licensed cover photographs and 344,357 matched stego pairs across 35 arms, where every image carries its own licence and every file carries its own checksum. A further 4 arms hold the 38,119 clean halves those pairs are measured against. Nothing is downloaded until you ask for a sample, so looking costs seconds rather than 48 GB. Each arm is its own config, named exactly as the arm table below names it, and covers is the default.…
Precomputed attribution maps and faithfulness metrics for the torchvision convnexttiny model (default pretrained weights, no fine-tuning) on imagenet-pico, a 3000-image subset of ImageNet-1k with three images for each of the 1000 classes. This repository is part of Neural Atlas, a web tool for comparing attribution methods across vision architectures on the same image, developed as an undergraduate thesis at the Facultad de Informática, Universidad Nacional de La Plata (Argentina). The viewer reads these files directly; they can also be used on their own, without the tool. Methods: CB-RISE, Deconvolution, DeepLift, GradientShap, GuidedBackprop, GuidedGradCam, InputXGradient…
Structured benchmark results for local hardware and hosted-provider language model evaluations. Rows include both local-device runs and provider results; recorded host and accelerator fields may be absent for provider rows. Results are published as immutable Parquet shards by the The default configuration opens data/recent/latest.parquet: the canonical shards published within 30 days of the newest one, newest run first. The publish workflow regenerates it from the canonical shards; it adds no rows of its own. Select history to browse every canonical shard, including older hosted-provider evaluations. Optional result and system fields use nulls when a run did not record them. Historical tag…
Training rollouts of one verl run, one parquet per trainer step (data/step-.parquet), rows in the order the trainer consumed them; the rollout.n samples of a prompt are contiguous and share uid. prompttokenids / responsetokenids are the exact token ids (special tokens kept, no padding) and rolloutlogprobs the sampling-time log-prob of every response token. input / output are decoded with skipspecialtokens=True and are for reading only. score is the raw reward.
840 multiple-choice video questions from 76 public video benchmarks, one item per video, four capability groups x 210 (Perception, Temporal, Spatial / physical, Reasoning / knowledge). The set is the difficulty-first census of the BenchCheck screened pool: items that none of the cheap attackers of the pool screening (text-only, single frame, 32-frame 2B model, options-only, shuffled frames) could solve, ranked by the worst-case attacker percentile, with the marked answers verified against the frames; no cap on any model's accuracy (v6, 2026-09-19: the earlier v5 composition capped the reference model's share of correct answers at 45 %, the cap is dropped so that every row of the leaderboard…
All products and their information from Denner Switzerland. The entire catalogue and the current special discounts are all included. Check out similar datasets for other grocery stores - https://huggingface.co/datasets/Yelinz/lidl-ch-products - https://huggingface.co/datasets/Yelinz/migros-ch-products - https://huggingface.co/datasets/Yelinz/coop-ch-products
Please use the below bibtex for citation if you use our pre-processed h5 dataset in samples/radarawshdf5v1: Thank you so much.
Model Collections
Hand-picked starting points, each with the reason it exists.
Collection · 4 entries
Embedding models for retrieval
Sentence and document embedding models used to build retrieval systems. Dimension and sequence length matter more than size here, and both come from the publisher.
Collection · 4 entries
Models that fit on one accelerator
Models whose publisher-reported parameter count puts them within reach of a single accelerator at common precisions. Memory needed depends on precision and serving configuration, so treat the parameter count as the starting point, not the answer.
Collection · 6 entries
Open-weight text models worth knowing
Widely used open-weight language models, chosen because each one is a distinct family rather than a variant of the one above it. Selection, not a ranking.
Collection · 3 entries
Speech and audio models
Recognition and synthesis models, grouped so the two directions are easy to compare.
Related SAVRN Research
The hub sits beside SAVRN's market data and infrastructure research: what models cost to run, and what it takes to run them.
SAVRN Index
What open models cost to run
The same open-weight model priced by every host that serves it, per million tokens.
Research Hub
Data center trackers and maps
Moratoriums, permits, power, water and capital behind the facilities that run these models.
Method
How the Model Hub is built
Sources, evidence labels, refresh behaviour, and the limits of every comparison here.