# Models, Page 461 of 464 Source: https://savrn.com/models/page/461 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub # Open-Weight Models An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data. Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page. 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 Models Task - [Text generation1,362](https://savrn.com/models?task=Text+generation) - [Image and text to text494](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction332](https://savrn.com/models?task=Feature+extraction) - [Robotics329](https://savrn.com/models?task=Robotics) - [Text classification293](https://savrn.com/models?task=Text+classification) - [Text to image202](https://savrn.com/models?task=Text+to+image) - [Speech recognition170](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification135](https://savrn.com/models?task=Image+classification) - [Text to speech130](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity102](https://savrn.com/models?task=Sentence+similarity) - [Token classification96](https://savrn.com/models?task=Token+classification) - [Zero-shot classification92](https://savrn.com/models?task=Zero-shot+classification) - [Reinforcement learning91](https://savrn.com/models?task=Reinforcement+learning) - [Image segmentation87](https://savrn.com/models?task=Image+segmentation) - [Object detection86](https://savrn.com/models?task=Object+detection) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [Translation77](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask67](https://savrn.com/models?task=Fill+mask) - [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Image to text57](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,718](https://savrn.com/models?library=transformers) - [peft329](https://savrn.com/models?library=peft) - [diffusers183](https://savrn.com/models?library=diffusers) - [pytorch169](https://savrn.com/models?library=pytorch) - [gguf165](https://savrn.com/models?library=gguf) - [sentence-transformers138](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [lerobot135](https://savrn.com/models?library=lerobot) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp40](https://savrn.com/models?library=llama.cpp) - [vllm38](https://savrn.com/models?library=vllm) - [onnx35](https://savrn.com/models?library=onnx) - [diffusion-single-file27](https://savrn.com/models?library=diffusion-single-file) - [minimax-h324](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [openpi16](https://savrn.com/models?library=openpi) - [exllamav315](https://savrn.com/models?library=exllamav3) - [stable-baselines315](https://savrn.com/models?library=stable-baselines3) - [keras14](https://savrn.com/models?library=keras) Modality - [Other6,276](https://savrn.com/models?modality=Other) - [Text2,536](https://savrn.com/models?modality=Text) - [Image554](https://savrn.com/models?modality=Image) - [Image and text551](https://savrn.com/models?modality=Image+and+text) - [Audio473](https://savrn.com/models?modality=Audio) - [Control420](https://savrn.com/models?modality=Control) - [Video114](https://savrn.com/models?modality=Video) - [Multimodal79](https://savrn.com/models?modality=Multimodal) - [Time series64](https://savrn.com/models?modality=Time+series) - [Tabular55](https://savrn.com/models?modality=Tabular) License - [apache-2.03,031](https://savrn.com/models?license=apache-2.0) - [mit1,221](https://savrn.com/models?license=mit) - [other681](https://savrn.com/models?license=other) - [cc-by-nc-4.0184](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0147](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma59](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.234](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.127](https://savrn.com/models?license=llama3.1) - [unknown26](https://savrn.com/models?license=unknown) - [cc-by-sa-4.018](https://savrn.com/models?license=cc-by-sa-4.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [gpl-3.016](https://savrn.com/models?license=gpl-3.0) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail13](https://savrn.com/models?license=openrail) - [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m) - [cc-by-nc-nd-4.010](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama26](https://savrn.com/models?license=llama2) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights10,787](https://savrn.com/models?access=open_access) - [Access requested at publisher335](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher286](https://savrn.com/models?publisher=mradermacher) - [Qwen91](https://savrn.com/models?publisher=qwen) - [RunningHub.ai91](https://savrn.com/models?publisher=runninghubai) - [Yurun Yuan86](https://savrn.com/models?publisher=ryanyr) - [Jenny Wilson81](https://savrn.com/models?publisher=jenny08311) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Jessen Pu69](https://savrn.com/models?publisher=putty77) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan61](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami60](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [Yurun Yuan44](https://savrn.com/models?publisher=yuruny) - [BAAR43](https://savrn.com/models?publisher=aixk) - [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud) [Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params) 11,122 models, sorted by most downloaded. Model ### [wt-nids-zeroday](https://savrn.com/models/wt-nids-zeroday) [SeokHyun](https://savrn.com/model-publishers/sh98) Open weights [View model](https://savrn.com/models/wt-nids-zeroday) Model ### [wukong-runtime](https://savrn.com/models/wukong-runtime) [Khói Studio](https://savrn.com/model-publishers/khoistudio7789) Open weights [View model](https://savrn.com/models/wukong-runtime) Model ### [wuwa_playable_character_identifier](https://savrn.com/models/wuwa-playable-character-identifier) [Luke](https://savrn.com/model-publishers/abcwewe) Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/wuwa-playable-character-identifier) Model ### [wuwanalyzt-model](https://savrn.com/models/wuwanalyzt-model) [Bryan Hartanto Salim](https://savrn.com/model-publishers/brihrs) Open weights 111M parameters 512 tokens [View model](https://savrn.com/models/wuwanalyzt-model) Model ### [wwyhahaha](https://savrn.com/models/wwyhahaha) [Zoarag](https://savrn.com/model-publishers/zoarag) Access requested at publisher [View model](https://savrn.com/models/wwyhahaha) Model ### [wy3NcQtnNJo0XOgb](https://savrn.com/models/wy3ncqtnnjo0xogb) [15A7](https://savrn.com/model-publishers/15a7) Open weights [View model](https://savrn.com/models/wy3ncqtnnjo0xogb) Model ### [wyglore-leaf](https://savrn.com/models/wyglore-leaf) [Moddim](https://savrn.com/model-publishers/m6dd8m) WygLore Leaf is a long-term memory plugin for RisuAI. This repository holds the update manifest the plugin reads and the changelogs it links to. RisuAI 용 장기기억 플러그인 잎 과, 곁들여 켜는 로컬 서버 뿌리 입니다. 서버 없이도 전부 동작합니다. 2. RisuAI 의 플러그인 화면에서 나온 파일을 기존 것 위에 덮어씁니다. WygLoreLeaf--latest.js와 WygLoreLeaf-latest.js는 RisuAI의 공식 업데이트 경로에서 받는 설치 파일입니다. 수동 설치 ZIP은 프로톤 공유 폴더에서 받으세요. 공개 소스: 잎 3.1.4 · 뿌리 서버 1.2.1. 소스 압축본은 개발용이며 RisuAI에 설치하는 플러그인 파일과 다릅니다. 윈도우는 wyglore-root.bat, 맥과 리눅스와 안드로이드(Termux)는 wyglore-root.sh 를 실행합니다. 설치와 연결 절차는 받는 곳의 뿌리 서버 폴더에 든 사용 안내 · 연동 안내에 있습니다. 이 저장소의 Community 탭에서 받습니다. 잎과 뿌리 서버 모두 Apache License 2.0 입니다. 공개 소스 압축본에 전문이 들어 있습니다. Open weights apache-2.0 [View model](https://savrn.com/models/wyglore-leaf) Model ### [x](https://savrn.com/models/x) [Salia Coel](https://savrn.com/model-publishers/saliacoel) Open weights [View model](https://savrn.com/models/x) Model ### [xDecision](https://savrn.com/models/xdecision) [Mccoysc](https://savrn.com/model-publishers/mccoysc) Open weights apache-2.0 [View model](https://savrn.com/models/xdecision) Model · Tabular classification ### [xcross-attempt-v1](https://savrn.com/models/xcross-attempt-v1) [(Ministry of) Silly Kicks](https://savrn.com/model-publishers/silly-kicks) XCrossAttemptModel is a deterministic-XGBoost classifier estimating P(the in-possession team attempts a cross within ~1 s of a tracking frame) — a STATE-anchored framing, reframed from the sender-level event treatment in Cao et al. (arXiv:2505.11841). It carries 7 of the paper's 8 confounders (crosser-position, #7, is omitted — no faithful tracking-only proxy) plus a novel, isolatable GK-position confounder block, which is the paper's headline gap and the reason this model exists inside the GKDV research arc (TF-17 → TF-19). - 16 features, faithful (velocity-bearing), goal-relative coordinates via the shared geometry helper - The scextended model is fit on the owner-tier corpus (IDSSE +… Open weights mit silly-kicks [View model](https://savrn.com/models/xcross-attempt-v1) Model ### [xeus-10th_2k_units](https://savrn.com/models/xeus-10th-2k-units) [Arigalaadarsh](https://savrn.com/model-publishers/arigalaadarsh) Open weights [View model](https://savrn.com/models/xeus-10th-2k-units) Model ### [xlerobot-dice-4cam-v2-act](https://savrn.com/models/xlerobot-dice-4cam-v2-act) [Biil](https://savrn.com/model-publishers/bilimili) Open weights 52M parameters [View model](https://savrn.com/models/xlerobot-dice-4cam-v2-act) Model ### [xlerobot-dice-4cam-v2-smolvla](https://savrn.com/models/xlerobot-dice-4cam-v2-smolvla) [Biil](https://savrn.com/model-publishers/bilimili) Open weights 450M parameters [View model](https://savrn.com/models/xlerobot-dice-4cam-v2-smolvla) Model ### [xlerobot-dice-4cam-v2-smolvla-nolang](https://savrn.com/models/xlerobot-dice-4cam-v2-smolvla-nolang) [Biil](https://savrn.com/model-publishers/bilimili) Open weights 450M parameters [View model](https://savrn.com/models/xlerobot-dice-4cam-v2-smolvla-nolang) Model ### [xlm-roberta-ev-absa](https://savrn.com/models/xlm-roberta-ev-absa) [Wicaksono Hanif Supriyanto](https://savrn.com/model-publishers/wicaksonohanif) Open weights [View model](https://savrn.com/models/xlm-roberta-ev-absa) Model ### [xlsr-300m-dhonam](https://savrn.com/models/xlsr-300m-dhonam) [Cynthia Jayne Amol](https://savrn.com/model-publishers/jayneamol) Open weights 315M parameters [View model](https://savrn.com/models/xlsr-300m-dhonam) Model ### [xori](https://savrn.com/models/xori) [Nikita](https://savrn.com/model-publishers/abobus2222228) Access requested at publisher [View model](https://savrn.com/models/xori) Model · Tabular classification ### [xshot-occurrence-v1](https://savrn.com/models/xshot-occurrence-v1) [(Ministry of) Silly Kicks](https://savrn.com/model-publishers/silly-kicks) XShotOccurrenceModel is a deterministic-XGBoost classifier estimating P(a shot is attempted by the in-possession team within ~1 s of a tracking frame) — the xS surface of the GKDV research arc (TF-16). Paper-faithful 27-feature extractor in goal-relative coordinates via the shared geometry helper: ball r/θ/z/speed, an openGoal goal-mouth obstruction term, GK distance/bearing, and the 5 nearest defenders + 5 nearest attackers. - Trained on 964,263 rows / 182,517 positives (18.9% positive rate) scextended is HF-only for licensing, not quality: it is trained on restricted owner-tier SkillCorner data that cannot be redistributed inside the PyPI wheel (ADR-038). Only learned parameters are… Open weights mit silly-kicks [View model](https://savrn.com/models/xshot-occurrence-v1) Model · Robotics ### [xvla_sharpie_holder_v1](https://savrn.com/models/xvla-sharpie-holder-v1) [Parsh Gandhi](https://savrn.com/model-publishers/parshg) X-VLA is a soft-prompted, flow-matching Vision-Language-Action framework that treats each robot or hardware setup as a "task" encoded with a small set of learnable Soft Prompt embeddings, letting a single model reconcile diverse robot morphologies, sensors, and action spaces. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot xvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and… Open weights apache-2.0 880M parameters lerobot [View model](https://savrn.com/models/xvla-sharpie-holder-v1) Model · Robotics ### [xvla_so101_pickcube_bs32_VLMFrozen_AllAUG](https://savrn.com/models/xvla-so101-pickcube-bs32-vlmfrozen-allaug) [Genc](https://savrn.com/model-publishers/ulaszoi) X-VLA is a soft-prompted, flow-matching Vision-Language-Action framework that treats each robot or hardware setup as a "task" encoded with a small set of learnable Soft Prompt embeddings, letting a single model reconcile diverse robot morphologies, sensors, and action spaces. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot xvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and… Open weights apache-2.0 880M parameters lerobot [View model](https://savrn.com/models/xvla-so101-pickcube-bs32-vlmfrozen-allaug) Model ### [yam-backbone](https://savrn.com/models/yam-backbone) [Entong Zhang](https://savrn.com/model-publishers/zenith6666) Open weights [View model](https://savrn.com/models/yam-backbone) Model ### [yan](https://savrn.com/models/yan) [Brayan Moscairo](https://savrn.com/model-publishers/griel33) Open weights [View model](https://savrn.com/models/yan) Model ### [yang-pixelcut-models](https://savrn.com/models/yang-pixelcut-models) [Jkyy](https://savrn.com/model-publishers/jkyy) 「洋洋 · PixelCut 批量抠图工作台」所用开源模型的聚合上传包，保持与原项目一致的目录结构（models/models/ /...）。 在项目根目录执行，模型将下载到 models/models/ 下，与绿色版完全一致，开箱即用。 Open weights [View model](https://savrn.com/models/yang-pixelcut-models) Model ### [yaw-asr-twi-en-omni300m-v11b](https://savrn.com/models/yaw-asr-twi-en-omni300m-v11b) [Prince Nasamu Alhassan](https://savrn.com/model-publishers/princealhassannasamu) Open weights [View model](https://savrn.com/models/yaw-asr-twi-en-omni300m-v11b) ## 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) ## Open-Weight Models Explained ### What is an open-weight model? An AI model whose trained weights are published for anyone to download, so it can be run, tested and fine-tuned on hardware the user controls. ### Is an open-weight model the same as open source? Not always. Open weights means the trained model can be downloaded. Open source usually also means the training code and data are available and the license allows broad reuse. Many open-weight models release the weights only. ### Can I use an open-weight model commercially? It depends on the license. Apache 2.0 and MIT allow commercial use. Other licenses limit it, for example to non-commercial use or below a set number of users. Every model page here shows its license. ### How much memory does an open-weight model need? About two bytes per parameter at 16-bit precision, so a 7-billion-parameter model needs roughly 14 GB for its weights, plus memory for the context it processes. Each model page lists its parameter count and the size of its files. ## 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.