# Models, Page 448 of 464 Source: https://savrn.com/models/page/448 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 ### [transport-5850d48f2009](https://savrn.com/models/transport-5850d48f2009) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-5850d48f2009) Model ### [transport-58b6c904-6a18-475f-b4cb-ae8359e8c4cc](https://savrn.com/models/transport-58b6c904-6a18-475f-b4cb-ae8359e8c4cc) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-58b6c904-6a18-475f-b4cb-ae8359e8c4cc) Model ### [transport-5c2573fb6942](https://savrn.com/models/transport-5c2573fb6942) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-5c2573fb6942) Model ### [transport-829849d1643c](https://savrn.com/models/transport-829849d1643c) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-829849d1643c) Model ### [transport-9d52d5c009ab](https://savrn.com/models/transport-9d52d5c009ab) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-9d52d5c009ab) Model ### [transport-a45cfcc2e83d](https://savrn.com/models/transport-a45cfcc2e83d) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-a45cfcc2e83d) Model ### [transport-a958d2510d4a](https://savrn.com/models/transport-a958d2510d4a) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-a958d2510d4a) Model ### [transport-ccca629fe3dc](https://savrn.com/models/transport-ccca629fe3dc) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-ccca629fe3dc) Model ### [transport-ce483dc7a507](https://savrn.com/models/transport-ce483dc7a507) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-ce483dc7a507) Model ### [transport-cf837d6fe075](https://savrn.com/models/transport-cf837d6fe075) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-cf837d6fe075) Model ### [transport-d4805e4f8d83](https://savrn.com/models/transport-d4805e4f8d83) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-d4805e4f8d83) Model ### [transport-e9f82dc3-61e9-4d04-9576-aa058b013ea0](https://savrn.com/models/transport-e9f82dc3-61e9-4d04-9576-aa058b013ea0) [Paul Vines](https://savrn.com/model-publishers/vines26) Open weights [View model](https://savrn.com/models/transport-e9f82dc3-61e9-4d04-9576-aa058b013ea0) Model ### [triaje-dermatologico-pesos](https://savrn.com/models/triaje-dermatologico-pesos) [Alberto](https://savrn.com/model-publishers/erickkkkk1) Open weights mit [View model](https://savrn.com/models/triaje-dermatologico-pesos) Model ### [trial-generation21](https://savrn.com/models/trial-generation21) [Hyun-woo Park](https://savrn.com/model-publishers/emily-gonzalez) A research-oriented DeiT prototype targeting Generation. The included huge setup documents defaults and file formats without presenting unverified performance numbers. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses novograd with a onecycle schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 33,088 parameters 128 tokens [View model](https://savrn.com/models/trial-generation21) Model ### [triesofcaned](https://savrn.com/models/triesofcaned) [Nieadmsie](https://savrn.com/model-publishers/niedamsie9) Open weights apache-2.0 [View model](https://savrn.com/models/triesofcaned) Model · Other ### [trivago-recsys-moe](https://savrn.com/models/trivago-recsys-moe) [Taha Moula](https://savrn.com/model-publishers/tmoula) Trained weights and artifacts for tmoula/trivago-recsys-moe, the Polanyi Labs prototype. It does session-based hotel recommendation on the Trivago RecSys Challenge 2019 data. It also includes an experimental persona-conditioned mixture-of-experts bridge that transfers retail browsing behaviour (Yoochoose, RecSys Challenge 2015) into the travel domain. The code (model classes, preprocessing, training and inference scripts) lives on GitHub. This repo only hosts the files too large for git. The model takes two left-padded [batch, 20] tensors, refids and actionids, and returns logits over all 400,278 tokens (index 0 is padding). Decode the results with mappings["idx2ref"]. - Unrelated pairings… Open weights mit pytorch [View model](https://savrn.com/models/trivago-recsys-moe) Model ### [trocar-tele108-only](https://savrn.com/models/trocar-tele108-only) [Yunju Li](https://savrn.com/model-publishers/yunju1115) Open weights 3.1B parameters [View model](https://savrn.com/models/trocar-tele108-only) Model ### [trocar-tele30-ego](https://savrn.com/models/trocar-tele30-ego) [Yunju Li](https://savrn.com/model-publishers/yunju1115) Open weights [View model](https://savrn.com/models/trocar-tele30-ego) Model ### [trocar-tele30-only](https://savrn.com/models/trocar-tele30-only) [Yunju Li](https://savrn.com/model-publishers/yunju1115) Open weights 3.1B parameters [View model](https://savrn.com/models/trocar-tele30-only) Model ### [truecal-portion-encoder](https://savrn.com/models/truecal-portion-encoder) [Suren Sarafian](https://savrn.com/model-publishers/suren10pearl) The image encoder of Depth Anything V2 Small (Apache-2.0, unchanged weights), converted to Core ML (ML Program, Float16, iOS 17) for on-device use by TrueCal. - PortionFeatures.mlpackage: input image, RGB 476 x 630 (portrait), 0-255, with the ImageNet normalisation inside; output features, Float16 (1, 45, 34, 384), the patch features of the photo in rows. Licence: Apache-2.0, as the original model. The app downloads this file once and runs it on the phone; no photo or other user content is ever sent here. Open weights apache-2.0 [View model](https://savrn.com/models/truecal-portion-encoder) Model ### [truthprint-ours-coco-llava15-13b](https://savrn.com/models/truthprint-ours-coco-llava15-13b) [Đinh Quốc Bảo](https://savrn.com/model-publishers/toitenbao) Object-hallucination probe trained on a LLaVA-1.5-13B COCO corpus, with three independent training seeds (0, 42, 1337). It is not interchangeable with the 7B release: features, geometry and thresholds are model-specific. 10,000 COCO train2014 images split 80/20 by image: 8,000 train / 54,253 object spans and 2,000 holdout / 13,789 object spans. Captions are generated by 13B itself, so this corpus is not the 7B corpus. Thresholds are selected by best F1 on the COCO validation split and then frozen. Nothing is tuned on the test holdout, AMBER, or POPE. COCO test holdout — mention view, n=13,787, 16.19% positive AMBER, strict zero-shot — n=2,367 rows / 977 images, 5.37% positive, COCO… Open weights [View model](https://savrn.com/models/truthprint-ours-coco-llava15-13b) Model ### [truthprint-ours-coco-llava15-7b](https://savrn.com/models/truthprint-ours-coco-llava15-7b) [Đinh Quốc Bảo](https://savrn.com/model-publishers/toitenbao) Open weights [View model](https://savrn.com/models/truthprint-ours-coco-llava15-7b) Model ### [try2](https://savrn.com/models/try2) [PhysThermix](https://savrn.com/model-publishers/physthermix) Open weights [View model](https://savrn.com/models/try2) Model ### [tsuki-qwen3-tts-models](https://savrn.com/models/tsuki-qwen3-tts-models) [Tsukihime](https://savrn.com/model-publishers/tsukihime-exe) Open weights [View model](https://savrn.com/models/tsuki-qwen3-tts-models) ## 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.