# Models, Page 462 of 464 Source: https://savrn.com/models/page/462 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 ### [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 ### [yhpMswCT3aQjufWj](https://savrn.com/models/yhpmswct3aqjufwj) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/yhpmswct3aqjufwj) Model ### [yizhi-lora-zhengshiban](https://savrn.com/models/yizhi-lora-zhengshiban) [Ln](https://savrn.com/model-publishers/xczrj-001) Open weights [View model](https://savrn.com/models/yizhi-lora-zhengshiban) Model ### [ykITejeaUr7Xab9J](https://savrn.com/models/ykitejeaur7xab9j) [14A3](https://savrn.com/model-publishers/14a3) Open weights [View model](https://savrn.com/models/ykitejeaur7xab9j) Model ### [ym](https://savrn.com/models/ym) [Alex](https://savrn.com/model-publishers/alexheihei) Open weights [View model](https://savrn.com/models/ym) Model ### [ymcykdyojpevurxaylcqdyjkp263_ad28ac02-2a32-4733-aed3-4ccb3d04d1ca](https://savrn.com/models/ymcykdyojpevurxaylcqdyjkp263-ad28ac02-2a32-4733-aed3-4ccb3d04d1ca) [Tscstudios](https://savrn.com/model-publishers/tscstudios) Open weights [View model](https://savrn.com/models/ymcykdyojpevurxaylcqdyjkp263-ad28ac02-2a32-4733-aed3-4ccb3d04d1ca) Model ### [ymluck](https://savrn.com/models/ymluck) [Alex](https://savrn.com/model-publishers/alexheihei) Open weights [View model](https://savrn.com/models/ymluck) Model ### [ympass](https://savrn.com/models/ympass) [Alex](https://savrn.com/model-publishers/alexheihei) Open weights [View model](https://savrn.com/models/ympass) Model ### [ymym](https://savrn.com/models/ymym) [Alex](https://savrn.com/model-publishers/alexheihei) Open weights [View model](https://savrn.com/models/ymym) Model ### [yn2500-preview](https://savrn.com/models/yn2500-preview) [Nikita](https://savrn.com/model-publishers/nikipshg) Open weights [View model](https://savrn.com/models/yn2500-preview) Model · Text generation ### [yoda-run3-sft](https://savrn.com/models/yoda-run3-sft) [Niranjan Deshpande](https://savrn.com/model-publishers/n-deshpande) This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct. It has been trained using TRL. This model was trained with SFT. - PEFT 0.21.0 Open weights peft [View model](https://savrn.com/models/yoda-run3-sft) Model · Object detection ### [yolo-v7-tiny-4s](https://savrn.com/models/yolo-v7-tiny-4s) [Nakamura Shuta](https://savrn.com/model-publishers/mccbena) MCCbena's YOLOv7-tiny, fine-tuned for 35 classes using the local open-images-4s-yolo dataset. This is a modified/fine-tuned release, not an official upstream release. Training continues until all 300 epochs complete; the final checkpoint is published automatically after completion. This page is also updated with interim checkpoints during training. Recommended for inference: best.pt, validation epoch 128, mAP@0.5 54.47%, mAP@0.5:0.95 40.55%. It is selected by the upstream fitness score; last.pt remains the latest resumable checkpoint. When available, DATASETEXPANSION.json records a supplemental Hammer/Drill subset from the original Zenodo publisher, CC BY 4.0. SUPPLEMENTALDATASETLICENSE.md… Open weights gpl-3.0 [View model](https://savrn.com/models/yolo-v7-tiny-4s) Model ### [yolo26m-object](https://savrn.com/models/yolo26m-object) [HydraVMS](https://savrn.com/model-publishers/hydravision) Open weights [View model](https://savrn.com/models/yolo26m-object) Model · Image segmentation ### [yolo26n-seg](https://savrn.com/models/yolo26n-seg) [Junho P](https://savrn.com/model-publishers/developerdobby) The official Ultralytics YOLO26n-seg COCO weights (yolo26n-seg.pt), exported unchanged to ONNX so they can run in the browser with onnxruntime-web. Not fine-tuned. Used by the model-card site AI Models Organize (YOLO26n-seg card → «돌려보기»). AGPL-3.0, inherited from Ultralytics YOLO. Open weights agpl-3.0 ultralytics [View model](https://savrn.com/models/yolo26n-seg) Model · Object detection ### [yolo_box](https://savrn.com/models/yolo-box) [Kittisak Kaewpichai](https://savrn.com/model-publishers/kkshaolin) Central model repository for ILoveSeafood AI Engineering Ecosystem. 1. YOLO Box Detection (yolo/yolo11n.pt): Real-time seafood box detection from surveillance camera feeds. 2. ARIMA Demand Forecasting (arima/arimaFrozenSeafood.pkl): Optimal stock inventory prediction. Open weights apache-2.0 [View model](https://savrn.com/models/yolo-box) Model ### [yolo_buttons](https://savrn.com/models/yolo-buttons) [Jack Zheng](https://savrn.com/model-publishers/lubucat) Open weights [View model](https://savrn.com/models/yolo-buttons) Model ### [yolo_pallet_rfdetr_v4_small](https://savrn.com/models/yolo-pallet-rfdetr-v4-small) [Piero](https://savrn.com/model-publishers/piero123) Open weights [View model](https://savrn.com/models/yolo-pallet-rfdetr-v4-small) Model ### [yolobattlesif](https://savrn.com/models/yolobattlesif) [Vulcano](https://savrn.com/model-publishers/cranjismcbasketball) Open weights [View model](https://savrn.com/models/yolobattlesif) Model ### [yolov8n-agrovision-plagas](https://savrn.com/models/yolov8n-agrovision-plagas) [Ander](https://savrn.com/model-publishers/drex09) Open weights apache-2.0 [View model](https://savrn.com/models/yolov8n-agrovision-plagas) Model ### [yue2-industrial-rock-lora](https://savrn.com/models/yue2-industrial-rock-lora) [Monsterovich](https://savrn.com/model-publishers/monsterovich) A set of LoRA adapters for m-a-p/YuE2-3B, fine-tuned to push the base model toward industrial rock: heavy distorted guitars, mechanical/industrial sound textures, a dense mix, and aggressive rhythms. YuE2 generates music in two stages, and there is one adapter per stage: - AR adapter — shapes the plan: structure, melody, and genre direction (the autoregressive planning stage). - NAR adapter — shapes the timbre: the actual instrumental/audio texture (the non-autoregressive decoding stage). Each stage has an adapter trained on a large pool (179 tracks) and a small pool (86 tracks). Use the large-pool ones for the best quality; the small-pool ones are kept for A/B comparisons and as the… Open weights [View model](https://savrn.com/models/yue2-industrial-rock-lora) Model · Text to audio ### [yue2-mltnt-militant-reggae](https://savrn.com/models/yue2-mltnt-militant-reggae) [Because Reasons](https://savrn.com/model-publishers/becausereasons) Four artist-style LoRAs that push YuE2-3B into modern militant roots reggae: dark raspy male patois vocals, steppers and one-drop grooves, deep sub bass, bubbling Hammond, nyabinghi drums, horn stabs, dub sirens and spring reverb. Conscious, apocalyptic, anthemic. Each file patches both halves of YuE2 in one go: the autoregressive planner (writes the score, decides the arrangement and the vocal lines) and the flow-matching decoder (the sound). Trigger word for all three: mltnt. All demos use the same original lyric, seed 7, 32 steps dpm2 / sgmuniform, no post-processing. MLTNT Frontline — baseline recipe, prompt prompts/steppersbaseline.txt, dense lyric (verses written at ~17 words per… Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/yue2-mltnt-militant-reggae) Model ### [z51Sa8manu4AOeWP](https://savrn.com/models/z51sa8manu4aoewp) [14A3](https://savrn.com/model-publishers/14a3) Open weights [View model](https://savrn.com/models/z51sa8manu4aoewp) ## 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.