# Datasets, Page 135 of 136
Source: https://savrn.com/datasets/page/135
Markdown alternate of the page above; the site index is https://savrn.com/llms.txt

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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.

11,442Models

3,247Datasets

285Papers

6,976Publishers

12,091Sourced relationships

Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Datasets

Task

- [Text generation127](https://savrn.com/datasets?task=Text+generation)
- [Robotics108](https://savrn.com/datasets?task=Robotics)
- [Other48](https://savrn.com/datasets?task=Other)
- [Text classification47](https://savrn.com/datasets?task=Text+classification)
- [Text retrieval44](https://savrn.com/datasets?task=Text+retrieval)
- [Question answering38](https://savrn.com/datasets?task=Question+answering)
- [Speech recognition33](https://savrn.com/datasets?task=Speech+recognition)
- [Tabular classification21](https://savrn.com/datasets?task=Tabular+classification)
- [Time series forecasting21](https://savrn.com/datasets?task=Time+series+forecasting)
- [Image to text20](https://savrn.com/datasets?task=Image+to+text)
- [Image classification16](https://savrn.com/datasets?task=Image+classification)
- [Visual question answering15](https://savrn.com/datasets?task=Visual+question+answering)
- [Object detection14](https://savrn.com/datasets?task=Object+detection)
- [Text to speech14](https://savrn.com/datasets?task=Text+to+speech)
- [Image to image13](https://savrn.com/datasets?task=Image+to+image)
- [Image segmentation9](https://savrn.com/datasets?task=Image+segmentation)
- [Image to 3d9](https://savrn.com/datasets?task=Image+to+3d)
- [Translation9](https://savrn.com/datasets?task=Translation)
- [Video classification9](https://savrn.com/datasets?task=Video+classification)
- [Feature extraction8](https://savrn.com/datasets?task=Feature+extraction)
- [Audio classification7](https://savrn.com/datasets?task=Audio+classification)
- [Reinforcement learning7](https://savrn.com/datasets?task=Reinforcement+learning)
- [Text to image7](https://savrn.com/datasets?task=Text+to+image)
- [Text to video7](https://savrn.com/datasets?task=Text+to+video)

License

- [other268](https://savrn.com/datasets?license=other)
- [apache-2.0238](https://savrn.com/datasets?license=apache-2.0)
- [mit237](https://savrn.com/datasets?license=mit)
- [cc-by-4.0232](https://savrn.com/datasets?license=cc-by-4.0)
- [cc-by-nc-4.078](https://savrn.com/datasets?license=cc-by-nc-4.0)
- [cc0-1.030](https://savrn.com/datasets?license=cc0-1.0)
- [cc-by-sa-4.029](https://savrn.com/datasets?license=cc-by-sa-4.0)
- [odc-by24](https://savrn.com/datasets?license=odc-by)
- [cc-by-nc-sa-4.021](https://savrn.com/datasets?license=cc-by-nc-sa-4.0)
- [unknown14](https://savrn.com/datasets?license=unknown)
- [cc-by-sa-3.06](https://savrn.com/datasets?license=cc-by-sa-3.0)
- [odbl6](https://savrn.com/datasets?license=odbl)
- [gpl-3.05](https://savrn.com/datasets?license=gpl-3.0)
- [cc-by-3.04](https://savrn.com/datasets?license=cc-by-3.0)
- [cc3](https://savrn.com/datasets?license=cc)
- [cc-by-nc-nd-4.03](https://savrn.com/datasets?license=cc-by-nc-nd-4.0)
- [gpl3](https://savrn.com/datasets?license=gpl)
- [cc-by-2.02](https://savrn.com/datasets?license=cc-by-2.0)
- [cc-by-nc-sa-3.02](https://savrn.com/datasets?license=cc-by-nc-sa-3.0)
- [cc-by-nd-4.02](https://savrn.com/datasets?license=cc-by-nd-4.0)
- [cdla-sharing-1.02](https://savrn.com/datasets?license=cdla-sharing-1.0)
- [agpl-3.01](https://savrn.com/datasets?license=agpl-3.0)
- [bsl-1.01](https://savrn.com/datasets?license=bsl-1.0)
- [c-uda1](https://savrn.com/datasets?license=c-uda)

Access

- [Publicly accessible2,994](https://savrn.com/datasets?access=open_access)
- [Access requested at publisher253](https://savrn.com/datasets?access=gated)

Publisher

- [Benjamin J Barber35](https://savrn.com/datasets?publisher=endomorphosis)
- [Chang Yu24](https://savrn.com/datasets?publisher=g1n0st)
- [Ji Xie24](https://savrn.com/datasets?publisher=sanaka87)
- [Justicedao18](https://savrn.com/datasets?publisher=justicedao)
- [LeDuy13](https://savrn.com/datasets?publisher=duyle2408)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn11)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn12)
- [Nguyen13](https://savrn.com/datasets?publisher=icdn13)
- [National Engineering Research Center for Big Data Software13](https://savrn.com/datasets?publisher=thulab)
- [Nguyen12](https://savrn.com/datasets?publisher=icdn10)
- [Nguyen12](https://savrn.com/datasets?publisher=icdn14)
- [Byte I12](https://savrn.com/datasets?publisher=icdn15)
- [Rebrowser.net12](https://savrn.com/datasets?publisher=rebrowser)
- [Shufan Jiang12](https://savrn.com/datasets?publisher=tsumugii)
- [Sota11](https://savrn.com/datasets?publisher=sotaagi2030)
- [Ai210](https://savrn.com/datasets?publisher=allenai)
- [Ps Yo10](https://savrn.com/datasets?publisher=atokforps)
- [Dougall DeepMind10](https://savrn.com/datasets?publisher=dougalldeepmind)
- [D1610](https://savrn.com/datasets?publisher=dsixteen)
- [Deesced9](https://savrn.com/datasets?publisher=ameluce)
- [Jiahao Zhang9](https://savrn.com/datasets?publisher=hi-todayis-jh)
- [Julia Dollis9](https://savrn.com/datasets?publisher=juliadollis)
- [Massive Text Embedding Benchmark9](https://savrn.com/datasets?publisher=mteb)
- [Pranab Pattanaik9](https://savrn.com/datasets?publisher=p2samapa)

Size

- [10K<n<100K126](https://savrn.com/datasets?size=10K%3Cn%3C100K)
- [1K<n<10K106](https://savrn.com/datasets?size=1K%3Cn%3C10K)
- [n<1K104](https://savrn.com/datasets?size=n%3C1K)
- [100K<n<1M71](https://savrn.com/datasets?size=100K%3Cn%3C1M)
- [1M<n<10M52](https://savrn.com/datasets?size=1M%3Cn%3C10M)
- [10M<n<100M33](https://savrn.com/datasets?size=10M%3Cn%3C100M)
- [100M<n<1B23](https://savrn.com/datasets?size=100M%3Cn%3C1B)
- [n>1T16](https://savrn.com/datasets?size=n%3E1T)
- [1B<n<10B9](https://savrn.com/datasets?size=1B%3Cn%3C10B)
- [100B<n<1T5](https://savrn.com/datasets?size=100B%3Cn%3C1T)
- [10B<n<100B3](https://savrn.com/datasets?size=10B%3Cn%3C100B)
- [10GB<n<100GB1](https://savrn.com/datasets?size=10GB%3Cn%3C100GB)
- [120,8981](https://savrn.com/datasets?size=120%2C898)
- [1B-tokens1](https://savrn.com/datasets?size=1B-tokens)
- [1T<n<10T1](https://savrn.com/datasets?size=1T%3Cn%3C10T)
- [n1K<n10K1](https://savrn.com/datasets?size=n1K%3Cn10K)
- [n<100K1](https://savrn.com/datasets?size=n%3C100K)
- [n<10K1](https://savrn.com/datasets?size=n%3C10K)
- [n<1GB1](https://savrn.com/datasets?size=n%3C1GB)
- [n>1M1](https://savrn.com/datasets?size=n%3E1M)

Domain

- [LeRobot32](https://savrn.com/datasets?domain=LeRobot)
- [legal, law, graphrag12](https://savrn.com/datasets?domain=legal%2C+law%2C+graphrag)
- [tsfile, timeseries, time-series12](https://savrn.com/datasets?domain=tsfile%2C+timeseries%2C+time-series)
- [open-data, data-science, web-scraping9](https://savrn.com/datasets?domain=open-data%2C+data-science%2C+web-scraping)
- [seo-cluster, web-nodes9](https://savrn.com/datasets?domain=seo-cluster%2C+web-nodes)
- [dpo, rlhf, ultrafeedback8](https://savrn.com/datasets?domain=dpo%2C+rlhf%2C+ultrafeedback)
- [mteb, text, image7](https://savrn.com/datasets?domain=mteb%2C+text%2C+image)
- [nepali, instruction-tuning, agriculture7](https://savrn.com/datasets?domain=nepali%2C+instruction-tuning%2C+agriculture)
- [science, stackexchange, provenance5](https://savrn.com/datasets?domain=science%2C+stackexchange%2C+provenance)
- [LeRobot, robotics, lerobot4](https://savrn.com/datasets?domain=LeRobot%2C+robotics%2C+lerobot)
- [ml-intern4](https://savrn.com/datasets?domain=ml-intern)
- [robotics, bimanual-manipulation, imitation-learning4](https://savrn.com/datasets?domain=robotics%2C+bimanual-manipulation%2C+imitation-learning)
- [AMASS, Retarget, Robotics3](https://savrn.com/datasets?domain=AMASS%2C+Retarget%2C+Robotics)
- [benchmark, agents, terminal3](https://savrn.com/datasets?domain=benchmark%2C+agents%2C+terminal)
- [climate3](https://savrn.com/datasets?domain=climate)
- [code3](https://savrn.com/datasets?domain=code)
- [crypto, cryptocurrency, finance3](https://savrn.com/datasets?domain=crypto%2C+cryptocurrency%2C+finance)
- [data-attribution, influence-functions, interpretability3](https://savrn.com/datasets?domain=data-attribution%2C+influence-functions%2C+interpretability)
- [fireviewer, fire, smoke3](https://savrn.com/datasets?domain=fireviewer%2C+fire%2C+smoke)
- [land-records, deeds, ocr3](https://savrn.com/datasets?domain=land-records%2C+deeds%2C+ocr)
- [video3](https://savrn.com/datasets?domain=video)
- [LeRobot, lerobot, grabette2](https://savrn.com/datasets?domain=LeRobot%2C+lerobot%2C+grabette)
- [LeRobot, ur7e, robotiq2](https://savrn.com/datasets?domain=LeRobot%2C+ur7e%2C+robotiq)
- [agentenv, rl-environment, agents2](https://savrn.com/datasets?domain=agentenv%2C+rl-environment%2C+agents)

[Most downloaded](https://savrn.com/datasets?sort=downloads)[Most liked](https://savrn.com/datasets?sort=likes)[Recently updated](https://savrn.com/datasets?sort=updated)[Name](https://savrn.com/datasets?sort=name)

3,247 datasets, sorted by most downloaded.

Dataset · Question answering

### [vmlu-cp](https://savrn.com/datasets/vmlu-cp)

[LuVox](https://savrn.com/model-publishers/luvox-ai)

Single val split: 1,013 retained records from 303 source train + 744 source validation records of tridm/VMLU. Source train: 292 retained / 11 excluded; source validation: 721 retained / 23 excluded. All 58 subjects remain represented. No test data was downloaded or included. Only these nine columns are exported in the Parquet dataset: Every promptaudio begins with: The remaining answer-letter instructions and all original choices are unchanged. For text evaluation use prompt. For audio evaluation supply promptaudio together with audio; do not send question, transcription or ttstext as additional model context. The audio contains the question only, without options, answer keys or model…

Access requested at publisher 1K<n<10K

[View dataset](https://savrn.com/datasets/vmlu-cp)

Dataset

### [void-byte-a3-data](https://savrn.com/datasets/void-byte-a3-data)

[Appvoid](https://savrn.com/model-publishers/appvoid)

Publicly accessible

[View dataset](https://savrn.com/datasets/void-byte-a3-data)

Dataset · Image to text

### [voobus-syriac-manuscripts](https://savrn.com/datasets/voobus-syriac-manuscripts)

[Drew Tada](https://savrn.com/model-publishers/tadad)

Original manuscript photographs from the HMML Vööbus collection, packaged with provenance metadata for OCR/HTR research and transcription. Release status: inprogress. 7,379 image records uploaded out of 75,581 catalogue entries. The catalogue configuration contains the full inventory; the images configuration contains only successfully downloaded, decoded, and uploaded images. See ingestion-status.json for the latest counts and ingestion-errors.jsonl for unresolved records. Each image is embedded in a Parquet shard. Loading images does not depend on HMML's server. JPEG bytes are preserved without resizing or recompression. Dimensions and SHA-256 hashes refer to these original files. The…

Publicly accessible cc-by-4.0 1K<n<10K

[View dataset](https://savrn.com/datasets/voobus-syriac-manuscripts)

Dataset

### [voxblink-metadata-full](https://savrn.com/datasets/voxblink-metadata-full)

[Amini](https://savrn.com/model-publishers/adollahamini1998)

Publicly accessible

[View dataset](https://savrn.com/datasets/voxblink-metadata-full)

Dataset · Visual question answering

### [vqacovers](https://savrn.com/datasets/vqacovers)

[B.VINAY KUMAR](https://savrn.com/model-publishers/bvinay06)

Textbook Cover VQA is a Visual Question Answering (VQA) dataset developed for research on structured understanding of text-rich academic textbook covers. The dataset is designed to evaluate vision–language models (VLMs) on textual understanding, academic-domain recognition, spatial grounding, and visual content understanding. Textbook Cover VQA consists of 1,510 textbook-cover images covering 9 academic domains. Each textbook-cover image is associated with 8 predefined VQA questions, resulting in 12,080 question-answer annotations. The dataset captures four major types of information: - B. Vinay Kumar — L24AEC592 - A. Akash — Y23AEC404 - A. Pujitha — Y23AEC410 - B. Avinash — Y23AEC413…

Publicly accessible cc-by-4.0 1K<n<10K

[View dataset](https://savrn.com/datasets/vqacovers)

Dataset

### [vsr-multi-speaker](https://savrn.com/datasets/vsr-multi-speaker)

[PEIROU](https://savrn.com/model-publishers/peirou)

Publicly accessible

[View dataset](https://savrn.com/datasets/vsr-multi-speaker)

Dataset

### [warp-3090-quant-lab-frozen-data](https://savrn.com/datasets/warp-3090-quant-lab-frozen-data)

[Nick Chapman](https://savrn.com/model-publishers/nchapman)

The frozen calibration, probe, and ladder sets underpinning every published number in the warp-3090-quant-lab repo (W4A16 GPTQ quants + DFlash2 speculative drafters for Qwen 3.5-family models). These files are deliberately frozen: the headline metrics are defined on these exact rows — - calib-cascade-v1.jsonl (999 rows) — the champion GPTQ calibration set (weightedKL 0.1087 @ 4B, 0.0889 @ 9B, 0.1264 @ 27B trace to it) - probe-v4.jsonl (378 rows) — the fidelity ruler; the weightedKL metric - probe-guard.jsonl (76 rows) — OOD guard (norobots + Belebele, 9 languages) - ladder-20.jsonl (20 prompts) — the acceptance ladder; the τ metric (shipped drafter τ 3.79 at the serving point / native MTP…

Publicly accessible cc-by-nc-4.0 n<1K

[View dataset](https://savrn.com/datasets/warp-3090-quant-lab-frozen-data)

Dataset

### [wazobia-asr-data](https://savrn.com/datasets/wazobia-asr-data)

[Bello Quamdeen](https://savrn.com/model-publishers/nolimitsxl)

Publicly accessible

[View dataset](https://savrn.com/datasets/wazobia-asr-data)

Dataset · Image segmentation

### [weed_segmentation_france](https://savrn.com/datasets/weed-segmentation-france)

[AgML](https://savrn.com/model-publishers/project-agml)

This dataset comprises real RGB images of weeds in agricultural fields at Montoldre, France, specifically within maize and bean crop areas. Captured using a robot platform during 2019 and 2021, the images were collected under natural field conditions to support semantic segmentation research for weed detection in crop environments. The dataset contains 985 images with pixel-level mask annotations. This dataset is indexed on https://project-agml.github.io/ as part of the AgML python library. This dataset was reformatted from its original format to match HuggingFace standards.

Publicly accessible cc-by-4.0 n<1K

[View dataset](https://savrn.com/datasets/weed-segmentation-france)

Dataset · Text generation

### [wildchecklists](https://savrn.com/datasets/wildchecklists)

[Yike Wang](https://savrn.com/model-publishers/yikeee)

WildChat-derived checklist/requirements data, excluding examples already present in yikeee/wildchecklistsopenrubricsrubrics. Rows from combinedwildchatrequirements whose instruction appears in yikeee/wildchecklistsopenrubricsrubrics (matched via messages[0].content) were removed. - Removed (instruction overlap, including duplicate instructions): 62,617 - instruction: user prompt - rawresponse: model/raw checklist generation output - requirements: parsed requirements list - origrow: original WildChat row metadata - origrowidx: index in source file - sourcefile: source file path

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/wildchecklists)

Dataset

### [wilds-archive](https://savrn.com/datasets/wilds-archive)

[Fumiya UCHIYAMA](https://savrn.com/model-publishers/fumiyau)

This repository mirrors archives of two WILDS datasets for download and staging on compute nodes. It is not an official WILDS repository. - camelyon17v1.0zip/: six independent ZIP files. Each contains paths under camelyon17v1.0/. - povertyv1.1zip/: seven independent ZIP files. Each contains paths under povertyv1.1/. Each ZIP can be extracted separately. Extract all archives into the same root directory to reconstruct both datasets: The resulting directories are wilds/camelyon17v1.0/ and wilds/povertyv1.1/. Pass wilds as the rootdir when loading these datasets with WILDS. See the WILDS dataset descriptions and cite the original datasets in research using them. WILDS describes Camelyon17 as…

Publicly accessible

[View dataset](https://savrn.com/datasets/wilds-archive)

Dataset

### [wmw-internal](https://savrn.com/datasets/wmw-internal)

[Nicklas Hansen](https://savrn.com/model-publishers/nicklashansen)

Publicly accessible

[View dataset](https://savrn.com/datasets/wmw-internal)

Dataset

### [wmw-internal-v2](https://savrn.com/datasets/wmw-internal-v2)

[Nicklas Hansen](https://savrn.com/model-publishers/nicklashansen)

Publicly accessible

[View dataset](https://savrn.com/datasets/wmw-internal-v2)

Dataset

### [world-signals](https://savrn.com/datasets/world-signals)

[Kosuke OKADA](https://savrn.com/model-publishers/lilambd)

One folder per day under data/YYYY-MM-DD/, and the same files copied to latest/. Built every morning (JST) by the EmpireOS world model. Nothing is generated by a model; every row is a measurement from a public source. Countries: JP, US, GB, DE, FR, ES, IT, BR, MX, IN, ID, KR, TW, VN, TH, PH, TR, SA, EG, NG, ZA, KE, PL, NL, SE, AU, CA, AR, CO, CL. Agents and analysts keep re-scraping the same public endpoints every day. This dataset does it once, keeps the history, and gives it in one flat shape. GDELT data is free for any use with attribution to the GDELT Project. Google Trends, Apple, GitHub, Hugging Face, Steam and npm data are reproduced as published by their public endpoints; check each…

Publicly accessible cc-by-4.0 1K<n<10K

[View dataset](https://savrn.com/datasets/world-signals)

Dataset

### [world_of_warcraft_bc_01](https://savrn.com/datasets/world-of-warcraft-bc-01)

[Opensima37](https://savrn.com/model-publishers/opensima37)

Publicly accessible

[View dataset](https://savrn.com/datasets/world-of-warcraft-bc-01)

Dataset · Other

### [worldengine-counterfactual-lead-pair-ladder-v9](https://savrn.com/datasets/worldengine-counterfactual-lead-pair-ladder-v9)

[CHEN ZHAN](https://savrn.com/model-publishers/weixicz)

This public dataset contains 65,131 rendered counterfactual cases organized into 8,491 same-history action/future groups. Each case has 4 historical and 8 future CAMF0 frames plus WorldEngine metadata. Archives preserve complete pair groups. The audit bundle contains group JSON manifests, receipts, validation reports and the completion contract. Large generator-intermediate subset PKLs are intentionally excluded because the CAST paired-cache builder consumes the group JSON and rendered frames only. Download all files and run bash extractall.sh DESTINATION. The extraction script verifies every archive against SHA256SUMS first.

Publicly accessible other

[View dataset](https://savrn.com/datasets/worldengine-counterfactual-lead-pair-ladder-v9)

Dataset · Tabular regression

### [worldpop-jp-2026-01](https://savrn.com/datasets/worldpop-jp-2026-01)

[Yui Matsumura](https://savrn.com/model-publishers/yuiseki)

WorldPop's gridded population of Japan, 2015 to 2030, release R2025A: how many people live in every 100 m and every 1 km cell, by sex and five-year age band at 1 km, and the degree of urbanisation of every cell. 1,024 rasters as WorldPop published them, and the same rasters as Cloud Optimized GeoTIFF. CC BY 4.0. Cite each product by its DOI (see Licence). WorldPop's server sends whole files and ignores HTTP Range requests, at about 400 KB/s a connection, and its GeoTIFFs are striped. To look at one city you fetch the country. The COGs here are tiled at 512 with overviews, and the Hub answers Range requests, so GDAL's /vsicurl/ or rasterio can read a window. The files of a release are not…

Publicly accessible cc-by-4.0 1K<n<10K

[View dataset](https://savrn.com/datasets/worldpop-jp-2026-01)

Dataset · Robotics

### [xarm7_pick_orange_ball_large](https://savrn.com/datasets/xarm7-pick-orange-ball-large)

[National Engineering Research Center for Big Data Software](https://savrn.com/model-publishers/thulab)

Apache TsFile version of zwonzis/xarm7-pick-orange-ball-large. A UFactory xArm7 dataset for the task "pick up the orange ball and put it in the cyan bowl". Each frame records the 8-D arm state and the 8-D commanded action. Task (taskindex 0): "pick up the orange ball and put it in the cyan bowl". - Time (INT64, milliseconds) - per-episode sample time, round(timestamp 1000) in milliseconds (the source timestamp column equals Time / 1000 seconds and is therefore not stored separately). - episodeindex, taskindex (TAG) - device dimensions identifying one demonstration episode and one task; query a single episode with WHERE episodeindex=0. - observationstate0..7, action0..7 (FIELD) - FLOAT 8-D…

Publicly accessible apache-2.0 10K<n<100K

[View dataset](https://savrn.com/datasets/xarm7-pick-orange-ball-large)

Dataset

### [xe-dap-dien-co-phai-doi-mu-bao-hiem-khong](https://savrn.com/datasets/xe-dap-dien-co-phai-doi-mu-bao-hiem-khong)

[Xuong san xuat non bao hiem Thang Loi](https://savrn.com/model-publishers/nonbaohiemthangloi)

Publicly accessible

[View dataset](https://savrn.com/datasets/xe-dap-dien-co-phai-doi-mu-bao-hiem-khong)

Dataset

### [xvla-oct26-draft-dataset](https://savrn.com/datasets/xvla-oct26-draft-dataset)

[Sai Tarun Inaganti](https://savrn.com/model-publishers/kindredbluespirit)

Publicly accessible

[View dataset](https://savrn.com/datasets/xvla-oct26-draft-dataset)

Dataset

### [xxd-paper-task-blobs](https://savrn.com/datasets/xxd-paper-task-blobs)

[Steadyworks](https://savrn.com/model-publishers/steadyworks)

Publicly accessible

[View dataset](https://savrn.com/datasets/xxd-paper-task-blobs)

Dataset

### [ycxnk](https://savrn.com/datasets/ycxnk)

[Jingkun Yue](https://savrn.com/model-publishers/chaty123)

Access requested at publisher

[View dataset](https://savrn.com/datasets/ycxnk)

Dataset

### [yds_wip_102026](https://savrn.com/datasets/yds-wip-102026)

[922-Skinaypraeyna](https://savrn.com/model-publishers/922-sy)

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/yds-wip-102026)

Dataset

### [yodas3](https://savrn.com/datasets/yodas3)

[ESPnet](https://savrn.com/model-publishers/espnet)

Publicly accessible cc-by-3.0

[View dataset](https://savrn.com/datasets/yodas3)

## Model Collections

Hand-picked starting points, each with the reason it exists.

Collection · 4 entries

### [Embedding models for retrieval](https://savrn.com/collections/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.

[View collection](https://savrn.com/collections/embedding-models-for-retrieval)

Collection · 4 entries

### [Models that fit on one accelerator](https://savrn.com/collections/models-that-run-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.

[View collection](https://savrn.com/collections/models-that-run-on-one-accelerator)

Collection · 6 entries

### [Open-weight text models worth knowing](https://savrn.com/collections/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.

[View collection](https://savrn.com/collections/open-weight-text-models-worth-knowing)

Collection · 3 entries

### [Speech and audio models](https://savrn.com/collections/speech-and-audio)

Recognition and synthesis models, grouped so the two directions are easy to compare.

[View collection](https://savrn.com/collections/speech-and-audio)

## 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](https://savrn.com/ai-index/pricing/open-models)

The same open-weight model priced by every host that serves it, per million tokens.

Research Hub

### [Data center trackers and maps](https://savrn.com/data-center-research)

Moratoriums, permits, power, water and capital behind the facilities that run these models.

Method

### [How the Model Hub is built](https://savrn.com/model-hub/methodology)

Sources, evidence labels, refresh behaviour, and the limits of every comparison here.