# Models, Page 498 of 501 Source: https://savrn.com/models/page/498 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. 12,010Models 3,413Datasets 288Papers 7,298Publishers 12,421Sourced relationships Updated 2026-10-21 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,467](https://savrn.com/models?task=Text+generation) - [Image and text to text542](https://savrn.com/models?task=Image+and+text+to+text) - [Robotics370](https://savrn.com/models?task=Robotics) - [Feature extraction352](https://savrn.com/models?task=Feature+extraction) - [Text classification326](https://savrn.com/models?task=Text+classification) - [Text to image224](https://savrn.com/models?task=Text+to+image) - [Speech recognition176](https://savrn.com/models?task=Speech+recognition) - [Audio classification160](https://savrn.com/models?task=Audio+classification) - [Text to speech141](https://savrn.com/models?task=Text+to+speech) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification97](https://savrn.com/models?task=Token+classification) - [Reinforcement learning96](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation90](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) - [Translation79](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask69](https://savrn.com/models?task=Fill+mask) - [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting) - [Video classification60](https://savrn.com/models?task=Video+classification) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,877](https://savrn.com/models?library=transformers) - [peft357](https://savrn.com/models?library=peft) - [diffusers199](https://savrn.com/models?library=diffusers) - [pytorch189](https://savrn.com/models?library=pytorch) - [gguf175](https://savrn.com/models?library=gguf) - [lerobot148](https://savrn.com/models?library=lerobot) - [mlx148](https://savrn.com/models?library=mlx) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp44](https://savrn.com/models?library=llama.cpp) - [onnx39](https://savrn.com/models?library=onnx) - [vllm38](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [diffusion-single-file28](https://savrn.com/models?library=diffusion-single-file) - [minimax-h326](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino20](https://savrn.com/models?library=openvino) - [exllamav319](https://savrn.com/models?library=exllamav3) - [coreml18](https://savrn.com/models?library=coreml) - [litert17](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,847](https://savrn.com/models?modality=Other) - [Text2,701](https://savrn.com/models?modality=Text) - [Image and text601](https://savrn.com/models?modality=Image+and+text) - [Image586](https://savrn.com/models?modality=Image) - [Audio495](https://savrn.com/models?modality=Audio) - [Control466](https://savrn.com/models?modality=Control) - [Video115](https://savrn.com/models?modality=Video) - [Multimodal79](https://savrn.com/models?modality=Multimodal) - [Time series64](https://savrn.com/models?modality=Time+series) - [Tabular56](https://savrn.com/models?modality=Tabular) License - [apache-2.03,220](https://savrn.com/models?license=apache-2.0) - [mit1,282](https://savrn.com/models?license=mit) - [other743](https://savrn.com/models?license=other) - [cc-by-nc-4.0201](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0155](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.051](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.240](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [unknown29](https://savrn.com/models?license=unknown) - [llama3.128](https://savrn.com/models?license=llama3.1) - [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0) - [gpl-3.017](https://savrn.com/models?license=gpl-3.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [cc-by-nc-nd-4.013](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [creativeml-openrail-m13](https://savrn.com/models?license=creativeml-openrail-m) - [llama28](https://savrn.com/models?license=llama2) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights11,627](https://savrn.com/models?access=open_access) - [Access requested at publisher383](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher313](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan106](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai101](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311) - [Jessen Pu77](https://savrn.com/models?publisher=putty77) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan63](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yurun Yuan59](https://savrn.com/models?publisher=yuruny) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Szekely52](https://savrn.com/models?publisher=attila115) - [Yurun Yuan51](https://savrn.com/models?publisher=yyuan244) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Christopher Subia-Waud48](https://savrn.com/models?publisher=cwaud) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [NVIDIA48](https://savrn.com/models?publisher=nvidia) - [BAAR46](https://savrn.com/models?publisher=aixk) [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) 12,010 models, sorted by most downloaded. 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 ### [xiaomi-firmware-cache](https://savrn.com/models/xiaomi-firmware-cache) [Yash10101](https://savrn.com/model-publishers/yash10101) Open weights [View model](https://savrn.com/models/xiaomi-firmware-cache) Model ### [xktuRWWPzLxRgvl2](https://savrn.com/models/xkturwwpzlxrgvl2) [15B30](https://savrn.com/model-publishers/15b30) Open weights [View model](https://savrn.com/models/xkturwwpzlxrgvl2) 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 ### [xxxxxxx](https://savrn.com/models/xxxxxxx) [Dnotme](https://savrn.com/model-publishers/dnotme) Open weights [View model](https://savrn.com/models/xxxxxxx) Model ### [yAJReqXWzbQZ5Y99](https://savrn.com/models/yajreqxwzbqz5y99) [15B30](https://savrn.com/model-publishers/15b30) Open weights [View model](https://savrn.com/models/yajreqxwzbqz5y99) 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 ### [yaw-asr-twi-en-v13b](https://savrn.com/models/yaw-asr-twi-en-v13b) [Prince Nasamu Alhassan](https://savrn.com/model-publishers/princealhassannasamu) Open weights 316M parameters [View model](https://savrn.com/models/yaw-asr-twi-en-v13b) Model · Text generation ### [yejin-korean-8b-v3-cpt6000-distill-qwen36-lora-lr2e4-nothink-v2](https://savrn.com/models/yejin-korean-8b-v3-cpt6000-distill-qwen36-lora-lr2e4-nothink-v2) [Hajunho](https://savrn.com/model-publishers/cooler8) Korean instruction-following language model. Built from Yejin 8B v3 (pretrained from scratch by Yejin: cooler8/yejin-korean-8b-v3-base): continued pretraining for 6,000 steps on public Korean data, Yejin chat vector from our own SFT model, then LoRA (rank 64, lr 2e-4). The LoRA data mixes our phase-1 format data (45,013 samples) with 39,290 short Korean solutions written by Qwen/Qwen3.6-27B (Apache-2.0) for questions from our own puzzle generator, KMMLU train, AI Hub school exams and AI Hub reading comprehension; only solutions that reached the known answer were kept (sequence-level distillation; no teacher weights or logits). No benchmark test items were used, and the teacher solutions… Open weights apache-2.0 7.2B parameters 4,096 tokens [View model](https://savrn.com/models/yejin-korean-8b-v3-cpt6000-distill-qwen36-lora-lr2e4-nothink-v2) Model · Text classification ### [yelpModel2026Fall](https://savrn.com/models/yelpmodel2026fall) [Cetus Wong](https://savrn.com/model-publishers/cthtr) This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Open weights 67M parameters 512 tokens transformers [View model](https://savrn.com/models/yelpmodel2026fall) ## 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.