# Datasets, Page 101 of 132
Source: https://savrn.com/datasets/page/101
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.

11,122Models

3,164Datasets

285Papers

6,792Publishers

11,852Sourced relationships

Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology)

## Filter Datasets

Task

- [Text generation127](https://savrn.com/datasets?task=Text+generation)
- [Robotics106](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 answering36](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 text17](https://savrn.com/datasets?task=Image+to+text)
- [Image classification16](https://savrn.com/datasets?task=Image+classification)
- [Object detection14](https://savrn.com/datasets?task=Object+detection)
- [Text to speech14](https://savrn.com/datasets?task=Text+to+speech)
- [Visual question answering14](https://savrn.com/datasets?task=Visual+question+answering)
- [Image to image11](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)
- [Audio classification7](https://savrn.com/datasets?task=Audio+classification)
- [Feature extraction7](https://savrn.com/datasets?task=Feature+extraction)
- [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

- [other250](https://savrn.com/datasets?license=other)
- [apache-2.0236](https://savrn.com/datasets?license=apache-2.0)
- [mit232](https://savrn.com/datasets?license=mit)
- [cc-by-4.0224](https://savrn.com/datasets?license=cc-by-4.0)
- [cc-by-nc-4.077](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-4.020](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-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)
- [cc-by-nc-3.01](https://savrn.com/datasets?license=cc-by-nc-3.0)

Access

- [Publicly accessible2,933](https://savrn.com/datasets?access=open_access)
- [Access requested at publisher231](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 Xie23](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)
- [Ai210](https://savrn.com/datasets?publisher=allenai)
- [Ps Yo10](https://savrn.com/datasets?publisher=atokforps)
- [Dougall DeepMind10](https://savrn.com/datasets?publisher=dougalldeepmind)
- [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)
- [Sota9](https://savrn.com/datasets?publisher=sotaagi2030)
- [Sunsetcanyon9](https://savrn.com/datasets?publisher=sunsetcanyon)

Size

- [10K<n<100K125](https://savrn.com/datasets?size=10K%3Cn%3C100K)
- [1K<n<10K106](https://savrn.com/datasets?size=1K%3Cn%3C10K)
- [n<1K100](https://savrn.com/datasets?size=n%3C1K)
- [100K<n<1M70](https://savrn.com/datasets?size=100K%3Cn%3C1M)
- [1M<n<10M51](https://savrn.com/datasets?size=1M%3Cn%3C10M)
- [10M<n<100M33](https://savrn.com/datasets?size=10M%3Cn%3C100M)
- [100M<n<1B21](https://savrn.com/datasets?size=100M%3Cn%3C1B)
- [n>1T16](https://savrn.com/datasets?size=n%3E1T)
- [1B<n<10B8](https://savrn.com/datasets?size=1B%3Cn%3C10B)
- [100B<n<1T5](https://savrn.com/datasets?size=100B%3Cn%3C1T)
- [10B<n<100B2](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

- [LeRobot31](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)
- [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)
- [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)
- [ai-governance, measurement2](https://savrn.com/datasets?domain=ai-governance%2C+measurement)
- [autonomous, agents, evolution2](https://savrn.com/datasets?domain=autonomous%2C+agents%2C+evolution)

[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)

3,164 datasets, sorted by most downloaded.

Dataset · Image and text to text

### [MemGUI-3K-Verl](https://savrn.com/datasets/memgui-3k-verl)

[Anonymous](https://savrn.com/model-publishers/memgui-rl)

RL prompt corpus of the ICLR 2027 submission MemGUI-RL (project page: https://memgui-rl-anonymous.github.io/). Every record is one annotated state of a MemGUI-3K trajectory in the ConAct conversation format consumed by the FARPO trainer (https://github.com/memgui-rl-anonymous/MemGUI-RL): Each record has taskid, stepnumber, ispositive, badstep, rawresponse (the annotated ConAct response:,,,, ), conversations (system prompt, user prompt with the folded history / UI memory / recent step record and the screenshot, reference assistant turn) and metadata (impact / reasonableness labels of the step, trajectory ids). Screenshots are not duplicated here: image references have the form…

Publicly accessible apache-2.0

[View dataset](https://savrn.com/datasets/memgui-3k-verl)

Dataset · Robotics

### [MesaTask-CTRC-100-shuffled](https://savrn.com/datasets/mesatask-ctrc-100-shuffled)

[Yifanwin](https://savrn.com/model-publishers/mifanfan)

This dataset holds the starting state of a scene restoration task: compared with the target state, 1-3 objects in each scene have been moved to random positions and need to be put back. source.scene on each object identifies its scene. - position is the model origin (not the bounding-box center), in centimeters - size is the object's target extent; the model is already scaled to match it. (x, y) are the horizontal extents and z is the height. A model's own local up-axis may not align with z, so orient it using size as the reference - rotation is the complete absolute orientation as a quaternion in [x, y, z, w] order. Apply it directly, per object, and do not compose any additional rotation…

Publicly accessible cc-by-nc-4.0

[View dataset](https://savrn.com/datasets/mesatask-ctrc-100-shuffled)

Dataset · Robotics

### [MesaTask-CTRC-100-target](https://savrn.com/datasets/mesatask-ctrc-100-target)

[Yifanwin](https://savrn.com/model-publishers/mifanfan)

This dataset holds the target state of a scene restoration task: each scene should be restored to the arrangement given here. source.scene on each object identifies its scene and can be used to pair with other data from the same batch. The dataset is self-contained: every referenced model lives under assets/, there are no absolute paths, and no external asset library is required. - position is the model origin (not the bounding-box center), in centimeters - size is the object's target extent; the model is already scaled to match it. (x, y) are the horizontal extents and z is the height. A model's own local up-axis may not align with z, so orient it using size as the reference - rotation is…

Publicly accessible cc-by-nc-4.0

[View dataset](https://savrn.com/datasets/mesatask-ctrc-100-target)

Dataset

### [MindBigData2022_MNIST_EP](https://savrn.com/datasets/mindbigdata2022-mnist-ep)

[Joshua Murray](https://savrn.com/model-publishers/joshuamurray67)

Publicly accessible odbl

[View dataset](https://savrn.com/datasets/mindbigdata2022-mnist-ep)

Dataset

### [MisdirectVQA-200](https://savrn.com/datasets/misdirectvqa-200)

[Tahsin Mayeesha](https://savrn.com/model-publishers/tahsin-mayeesha)

Publicly accessible

[View dataset](https://savrn.com/datasets/misdirectvqa-200)

Dataset · Text classification

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

[Dian Ouo](https://savrn.com/model-publishers/dian-ouo)

Public posts, comments and agent profiles collected from Moltbook, a Reddit-style social platform exclusively for AI agents ("the front page of the agent internet"). The platform launched in late January 2026; this dataset covers its first eight-plus months (posts created 2026-01-27 through 2026-10-07). Collection runs daily; the published files are refreshed as dated snapshots, normally monthly. Every piece of content was posted by an AI-agent account, each meant to be an autonomous AI agent; the corpus cannot show how much of it a human operator wrote or prompted. This makes the dataset suited for studying AI agent communication patterns, community formation, self-presentation, and social…

Publicly accessible cc-by-4.0 10M<n<100M

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

Dataset

### [Morgan_Stanley_Bank_of_America_Merrill_Lynch_Trust_2025_5C1_2056713](https://savrn.com/datasets/morgan-stanley-bank-of-america-merrill-lynch-trust-2025-5c1-2056713)

[Deny Tran](https://savrn.com/model-publishers/denytrandfw)

SEC ABS-EE asset-level filings for CIK 2056713 (Morgan Stanley Bank of America Merrill Lynch Trust 2025-5C1). Parquet files are loan-level / asset-level data extracted from XML exhibits, organised as {accessionnodash}/{exhibitname}.parquet. Reporting-period dates are derived from the asset-level XML (reportingPeriodEndingDate).

Publicly accessible gpl

[View dataset](https://savrn.com/datasets/morgan-stanley-bank-of-america-merrill-lynch-trust-2025-5c1-2056713)

Dataset · Visual question answering

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

[Logan Bolton](https://savrn.com/model-publishers/loganbolton)

Can a model recognize a famous video from just its colors? Each image is a barcode of one video. Every stripe is the average color of a few seconds, in order from start to end. The model picks which video it is out of 10 options, so guessing gets 10%. The wrong options are picked to be plausible. Same genre, close in year, similar colorfulness. The code is at https://github.com/LoganBolton/MovieColorBench. Any OpenRouter model runs with one command. Only barcodes are shared here, no frames from the videos.

Publicly accessible mit n<1K

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

Dataset

### [Net-VQA](https://savrn.com/datasets/net-vqa)

[Zhang](https://savrn.com/model-publishers/yan8818)

Net-VQA is a visual question answering benchmark for evaluating multimodal models on network topology understanding and configuration reasoning. The complete benchmark contains 100 enterprise network scenarios and 500 questions covering three tasks: network topology parsing, high-level configuration understanding, and configuration strategy generation. Each scenario includes a topology diagram and configuration intent, with questions and reference answers annotated and validated by networking experts. The current release provides the scenarios approved for public disclosure. A small number of topology scenarios remain unavailable because their diagrams contain commercially sensitive…

Access requested at publisher cc-by-4.0

[View dataset](https://savrn.com/datasets/net-vqa)

Dataset

### [Niji_1_Weapon-meta](https://savrn.com/datasets/niji-1-weapon-meta)

[D16](https://savrn.com/model-publishers/dsixteen)

Publicly accessible

[View dataset](https://savrn.com/datasets/niji-1-weapon-meta)

Dataset

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

[Anonimous Submission](https://savrn.com/model-publishers/anonimous-submission)

Each subset is one region. buildings and massings are splits.

Publicly accessible

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

Dataset

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

[Yang Chen](https://savrn.com/model-publishers/cheyan9)

Publicly accessible

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

Dataset

### [OpenJev-Curated-Dataset](https://savrn.com/datasets/openjev-curated-dataset)

[Mithilesh Nakade](https://savrn.com/model-publishers/man1103)

Publicly accessible

[View dataset](https://savrn.com/datasets/openjev-curated-dataset)

Dataset

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

[OptRecover](https://savrn.com/model-publishers/optrecover)

OptRecover is a dataset for conversational specification recovery in optimization modeling. Given an incomplete problem description, an assistant asks clarification questions and combines the answers into a recovered specification. Structured references support controlled answering and evaluation of how much missing information has been recovered. The dataset covers 12 scenario families, including task assignment, staff scheduling, logistics, healthcare, admissions, and team formation. All problem descriptions are in English. The dataset contains 48 complete problems and 288 incomplete variants, with a structured reference for every description. Each complete problem has six variants: two…

Publicly accessible cc-by-4.0 n<1K

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

Dataset

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

[Qinghangliu](https://savrn.com/model-publishers/qinghangliu)

This repository contains 94,372 multimodal robot subgoal training samples from 16 tasks and the 240,855 unique PNG files referenced by those samples. Images are distributed as 16 lossless tar.zst archives, one per task. Extract the archives before training; the image paths in the JSONL point to the extracted files. Install the Hugging Face CLI (pip install -U huggingfacehub) and the zstd command (brew install zstd on macOS or sudo apt-get install zstd on Debian/Ubuntu). Then, from an empty directory: The helper requires Python 3.9 or later. It verifies archive SHA-256 checksums, validates every member path, and verifies all restored PNG files against the image manifest. It resumes safely…

Publicly accessible 10K<n<100K

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

Dataset · Video to video

### [PCVE-ComparisonSources](https://savrn.com/datasets/pcve-comparisonsources)

[Yuanjie Chen](https://savrn.com/model-publishers/ccmoony)

PCVE comparison sources The nine source clips behind the "真实例子" and "经典例子" collections of the PCVE comparison-video review set. They live here for one reason: the Volcengine Ark video API (Seedance 2.5) only accepts a reference video as a public URL — it rejects base64 payloads — so the clips have to be fetchable by Ark's servers. file scene group note classicdomino.mp4 rendered classicdroplateralfootballrealroom.mp4 rendered

Publicly accessible other

[View dataset](https://savrn.com/datasets/pcve-comparisonsources)

Dataset

### [PMC-Patients-JA-DDx](https://savrn.com/datasets/pmc-patients-ja-ddx)

[SIP Healthcare generative AI project: Med-LLM (nii)](https://savrn.com/model-publishers/sip-med-llm)

Publicly accessible cc-by-nc-sa-4.0

[View dataset](https://savrn.com/datasets/pmc-patients-ja-ddx)

Dataset

### [PT-data](https://savrn.com/datasets/pt-data)

[Nguyễn Minh Phúc](https://savrn.com/model-publishers/datpysci)

Publicly accessible

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

Dataset

### [PestVL-Net](https://savrn.com/datasets/pestvl-net)

[Zyj](https://savrn.com/model-publishers/zyjcode)

Publicly accessible

[View dataset](https://savrn.com/datasets/pestvl-net)

Dataset

### [Physical-AI-UMI-bottle-opening-with-left-gripper](https://savrn.com/datasets/physical-ai-umi-bottle-opening-with-left-gripper)

[DecisionFacts Inc](https://savrn.com/model-publishers/decisionfacts)

Publicly accessible cc-by-4.0

[View dataset](https://savrn.com/datasets/physical-ai-umi-bottle-opening-with-left-gripper)

Dataset

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

[WB-WAM](https://savrn.com/model-publishers/wb-wam)

English · 中文 Pico motion-transfer data for WB-WAM intermediate training. LeRobot v3.0, 20 Hz, RGB 360 × 270. The release contains 13,396 episodes, 1,579,028 frames, 73 tasks, and 302 independent recordings (21.931 hours). Each /recordXXXX/ is an independent LeRobot dataset. Recordings of the same task remain separate: The Dataset Viewer plays the MP4 files. The corresponding Parquet state/action rows and metadata are available under each recording in Files and versions. The action contains absolute next-frame joint targets (+50 ms), not joint increments. Root[3] denotes roll, pitch, and body-frame z angular velocity, not XYZ position. Pico hand data may include reconstructed or held values.…

Publicly accessible

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

Dataset

### [Poketwo-Spawn-Images](https://savrn.com/datasets/poketwo-spawn-images)

[Eeveebot](https://savrn.com/model-publishers/eeveebot)

Publicly accessible

[View dataset](https://savrn.com/datasets/poketwo-spawn-images)

Dataset

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

[Reiwa-AI K.K.](https://savrn.com/model-publishers/reiwa-ai)

This dataset is a collection of datasets intended for various language models, with response formats standardized using an LLM. The reformatting process preserves the writing style and persona of the original responses; the responses were not generated by the LLM from scratch. The aim is to have the model acquire basic knowledge and common sense during the initial phase of SFT (Supervised Fine-Tuning). This dataset contains LLM-transformed records derived from multiple source datasets with different licenses. - original-license: the license applicable to the original source - source: the URL from which the source data was obtained The original response text was stylistically rewritten by an…

Publicly accessible other

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

Dataset · Reinforcement learning

### [PortSimEnv-AgentEnv](https://savrn.com/datasets/portsimenv-agentenv)

[Edgar Arakelyan](https://savrn.com/model-publishers/earakely-scale)

call, an emergency, late ships, a crane outage, a gale and bunched arrivals come in as bulletins, and the tug company and the pilot station announce 2 tugs and 2 pilots out from hour 54. It ends at the hindsight optimum (reward 1.0), where a naive re-plan breaks the rules. on the replay Space. Twin © OpenStreetMap contributors (ODbL). An agent runs one container quay at the Port of Barcelona for a week of 2024 that has just gone wrong: late and bunched ships, closed quay sections, crane breakdowns, gales, emergencies. It decides when, where along the quay and with how many cranes every ship docks, and the week is graded deterministically against a plan CP-SAT proved optimal. This dataset…

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

[View dataset](https://savrn.com/datasets/portsimenv-agentenv)

## 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.
