# Datasets, Page 109 of 136
Source: https://savrn.com/datasets/page/109
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

### [aodraw-raw](https://savrn.com/datasets/aodraw-raw)

[Xinzwang](https://savrn.com/model-publishers/xinzwang)

Publicly accessible

[View dataset](https://savrn.com/datasets/aodraw-raw)

Dataset

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

[Darkred Developer](https://savrn.com/model-publishers/darkred06)

Publicly accessible

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

Dataset · Text classification

### [arabic-decision-benchmark](https://savrn.com/datasets/arabic-decision-benchmark)

[NOMEDA](https://savrn.com/model-publishers/nomeda-lab)

A unified Arabic typed-decision evaluation benchmark, built to measure a model as a decision engine (choose / score / yes-no over supplied options) rather than a free-text generator. It merges two complementary evaluation layers: - Core — held-out, genuinely Arabic states from licensed Arabic sources, with benchmark-only (pool-B) question wording. - OpenJev — the repaired new-in-V1 domains (mailroom, amountextraction, phoneextraction, irdecision, entityalignment, silentfailure, sponsorsegment, contextretention, emailselection, citationcontrol, browserdrone) recovered from the existing Arabic OpenJev variants. choice = pick one candidate; score = ordered level; noul = yes/no. Every candidate…

Publicly accessible other 1K<n<10K

[View dataset](https://savrn.com/datasets/arabic-decision-benchmark)

Dataset · Image to text

### [arabic-document-atlas](https://savrn.com/datasets/arabic-document-atlas)

[Bayanat Labs](https://savrn.com/model-publishers/bayanat-labs)

25,289 unique document images · 8,131,215 machine word records · 26,629 attributed community-text records. A visual Arabic document resource with JPEG page bytes, machine OCR, approximate word localization and line order, exact source PDF/manifest hashes, edition metadata, duplicate lineage and mechanical quality flags. The main scan tier comes from Arabic Collections Online and its supplying institutions. A separate, smaller PDF image/reference tier preserves Wikisource transcriptions and file-specific permission evidence. Bayanat builds the normalized visual representation and auditable processing pipeline. The main image shards occupy 20.47 GiB across 92 source books. Reference-pair…

Publicly accessible other 10K<n<100K

[View dataset](https://savrn.com/datasets/arabic-document-atlas)

Dataset

### [architecture-data-2024](https://savrn.com/datasets/architecture-data-2024)

[Sofia G.](https://savrn.com/model-publishers/sofiasof)

A documented Architecture data-preparation workflow for Sensor Fusion records. The bundled rows demonstrate the schema and validation path rather than pretending to be a full training corpus. - dataloader.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/architecture-data-2024)

Dataset

### [art-video-text](https://savrn.com/datasets/art-video-text)

[T. Nguyen](https://savrn.com/model-publishers/nguye-n3417)

Art Video Text Data Notes This data card accompanies a lightweight Art loader for Video Text metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation. Included material loader.py — loading, cleaning, and split preparation code. datasetinfos.json — schema and split metadata. metadatasample.jsonl — small, human-readable records for checking the schema. README.md — data card and usage

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/art-video-text)

Dataset

### [ashley-live](https://savrn.com/datasets/ashley-live)

[G Dave Roby](https://savrn.com/model-publishers/kirikir13)

Publicly accessible

[View dataset](https://savrn.com/datasets/ashley-live)

Dataset · Audio classification

### [audio-eval-suite](https://savrn.com/datasets/audio-eval-suite)

[Mandip Goswami](https://savrn.com/model-publishers/mandipgoswami)

A small, standardized audio-robustness evaluation set packaged to drop into eval loops. It pairs fully synthetic, license-clean speech-like probe signals with 300 real-synthetic room impulse responses (RIRs) and a labeled degradation chain (reverberation + additive noise), so you can measure how a model's acoustic predictions hold up as rooms get more reverberant and noisier. Think of it as a quick sanity/robustness check: one line to load, a concrete metric, and ground-truth room acoustics for every clip. Measured ground-truth ranges (from the underlying RIRs): - reverbbin distribution: mild 875, strong 600, clean 25 Sources are synthetic speech-LIKE probe signals, not real speech. Each is…

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

[View dataset](https://savrn.com/datasets/audio-eval-suite)

Dataset

### [audio-text-corpus](https://savrn.com/datasets/audio-text-corpus)

[Aara Vsas](https://savrn.com/model-publishers/aara-vsas)

This repository contains a preparation pipeline and a small metadata sample for Comics work with Audio Text inputs. It does not claim to be a complete benchmark release; the loader documents how source data is normalized and validated. - clean.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/audio-text-corpus)

Dataset

### [audio-text-data-2024](https://savrn.com/datasets/audio-text-data-2024)

[Sachin Bhat](https://savrn.com/model-publishers/sachinbhat)

This data card accompanies a lightweight Traffic loader for Audio Text metadata. It is meant for pipeline inspection, source adaptation, and reproducible split preparation. - loaddata.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for testing the data…

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/audio-text-data-2024)

Dataset

### [audio-text-samples](https://savrn.com/datasets/audio-text-samples)

[J. Martinez](https://savrn.com/model-publishers/jamesmartinez)

This repository contains a preparation pipeline and a small metadata sample for Travel work with Audio Text inputs. It does not claim to be a complete benchmark release; the loader documents how source data is normalized and validated. - prepare.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the…

Publicly accessible mit

[View dataset](https://savrn.com/datasets/audio-text-samples)

Dataset

### [audio-video-data](https://savrn.com/datasets/audio-video-data)

[H. Khan](https://savrn.com/model-publishers/hannahkhan)

Preparation notes and schema examples for Satellite tasks using Audio Video data. Full source material is intentionally not bundled, so provenance and licensing remain explicit. - dataloader.py — loading, cleaning, and split preparation code. - datasetinfos.json — schema and split metadata. - metadatasample.jsonl — small, human-readable records for checking the schema. - README.md — data card and usage notes. Before using the prepared data, verify source licenses, duplicates across splits, missing values, label balance, and modality-specific corruption. Record the source version and every filtering rule so a later run can reproduce the same rows. The repository is suitable for testing the…

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/audio-video-data)

Dataset

### [augmentation-full-seed42-runs](https://savrn.com/datasets/augmentation-full-seed42-runs)

[LeDuy](https://savrn.com/model-publishers/duyle2408)

Publicly accessible

[View dataset](https://savrn.com/datasets/augmentation-full-seed42-runs)

Dataset

### [augmentation-oacp-runs](https://savrn.com/datasets/augmentation-oacp-runs)

[LeDuy](https://savrn.com/model-publishers/duyle2408)

Publicly accessible

[View dataset](https://savrn.com/datasets/augmentation-oacp-runs)

Dataset · Robotics

### [augs_hiliter_01](https://savrn.com/datasets/augs-hiliter-01)

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

Apache TsFile version of nitsuga-hug/augs-hiliter01. A SO follower (SO-101/SO-100) dataset for the "pnphiliter" pick-and-place task, collected with augmented human-in-the-loop data. Each frame records the 6-D joint state and the 6-D commanded action. Task (taskindex 0): "pnphiliter". - 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. - action0..5, observationstate0..5 (FIELD) - FLOAT 6-D SO-follower…

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

[View dataset](https://savrn.com/datasets/augs-hiliter-01)

Dataset

### [autonomous-ai-agent-security-incidents-2026](https://savrn.com/datasets/autonomous-ai-agent-security-incidents-2026)

[Serhii Doletskyi](https://savrn.com/model-publishers/doletskyisergey)

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/autonomous-ai-agent-security-incidents-2026)

Dataset

### [avspeech-100k-source-archive](https://savrn.com/datasets/avspeech-100k-source-archive)

[Tianyu Shen](https://savrn.com/model-publishers/terryshenty)

Publicly accessible

[View dataset](https://savrn.com/datasets/avspeech-100k-source-archive)

Dataset · Robotics

### [axiboai_piper_umi_ee_pick_place](https://savrn.com/datasets/axiboai-piper-umi-ee-pick-place)

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

Apache TsFile version of axiboai/piper-umi-ee-pick-place. This is a pick-and-place demonstration dataset recorded on an AgileX Piper arm with a single 6-DoF end-effector (pipersingleeef6d) and packaged in the LeRobot v2.1 layout. Each row is one control step: a 10-dimensional end-effector state (position x, y, z, rotation as 6-D rot6d, and gripper) together with the 10-dimensional commanded action. The metadata records a single task (taskindex = 0). - Time (INT64, milliseconds) — round(timestamp 1000); restarts at 0 for each episode. - episodeindex (TAG) — episode / device dimension. - taskindex (TAG) — task dimension (constant 0 here). - frameindex (FIELD, INT64) — source frame counter…

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

[View dataset](https://savrn.com/datasets/axiboai-piper-umi-ee-pick-place)

Dataset

### [b1k-task55-re-shelving-library-books-official-eval](https://savrn.com/datasets/b1k-task55-re-shelving-library-books-official-eval)

[Jeremy](https://savrn.com/model-publishers/jermo1120)

Rollouts of the Pi0.5 fine-tune jermo1120/b1k-task55-re-shelving-library-books (step24999) on BEHAVIOR-1K task reshelvinglibrarybooks, following the 2026 challenge leaderboard protocol. Missing instances count as 0 in Q. qscore gives partial credit: 1.0 for a full success, otherwise the fraction of goal predicates that were false at the start and true at the end of the episode. The task 55 goal is only "every book (3) is inside a bookcase", so per-instance scores are 0, 0.33, 0.67 or 1.0. Distances are normalized by the mean human demo (1.0 = same path length as a human). Per-rollout metrics are in results/ /.json and results/summary.csv, videos (head + both wrist cameras) in videos/ /…

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/b1k-task55-re-shelving-library-books-official-eval)

Dataset

### [baar-img-latents-cache](https://savrn.com/datasets/baar-img-latents-cache)

[BAAR](https://savrn.com/model-publishers/aixk)

Publicly accessible

[View dataset](https://savrn.com/datasets/baar-img-latents-cache)

Dataset

### [baar3-img-cache5](https://savrn.com/datasets/baar3-img-cache5)

[BAAR](https://savrn.com/model-publishers/aixk)

Publicly accessible

[View dataset](https://savrn.com/datasets/baar3-img-cache5)

Dataset

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

[Zhang Zhifang](https://savrn.com/model-publishers/zhangzhifang)

Publicly accessible

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

Dataset

### [backup](https://savrn.com/datasets/backup-2)

[Tom](https://savrn.com/model-publishers/dizh23)

Publicly accessible

[View dataset](https://savrn.com/datasets/backup-2)

Dataset · Question answering

### [bank-deployment-blind-spot-eval](https://savrn.com/datasets/bank-deployment-blind-spot-eval)

[Appiffy](https://savrn.com/model-publishers/appiffy)

Bank Deployment Blind Spot Evaluation A matched-pair evaluation of Qwen2.5-3B-Instruct on 80 practitioner-written questions about software deployment, split 40/40 between a bank setting and a startup setting. Submitted for the Fatima Institute technical challenge Blind Spots of Frontier Models. The hypothesis was that a small open-weight model would answer every deployment question with a startup-shaped recipe and under-score on the bank side. The data says

Publicly accessible mit n<1K

[View dataset](https://savrn.com/datasets/bank-deployment-blind-spot-eval)

## 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.