# Models, Page 499 of 501 Source: https://savrn.com/models/page/499 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 ### [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 ### [zI11sk9VZXLnslSx](https://savrn.com/models/zi11sk9vzxlnslsx) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/zi11sk9vzxlnslsx) Model ### [zImageTurboNSFW_110](https://savrn.com/models/zimageturbonsfw-110) [Vin Fang](https://savrn.com/model-publishers/vbay03) Open weights [View model](https://savrn.com/models/zimageturbonsfw-110) Model ### [zXYu05VYqmxM0xwy](https://savrn.com/models/zxyu05vyqmxm0xwy) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/zxyu05vyqmxm0xwy) ## 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.