# Models, Page 361 of 477 Source: https://savrn.com/models/page/361 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. 11,442Models 3,247Datasets 285Papers 6,976Publishers 12,091Sourced relationships Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,413](https://savrn.com/models?task=Text+generation) - [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction341](https://savrn.com/models?task=Feature+extraction) - [Robotics338](https://savrn.com/models?task=Robotics) - [Text classification303](https://savrn.com/models?task=Text+classification) - [Text to image209](https://savrn.com/models?task=Text+to+image) - [Speech recognition171](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification137](https://savrn.com/models?task=Image+classification) - [Text to speech132](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification97](https://savrn.com/models?task=Token+classification) - [Reinforcement learning93](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation88](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) - [Translation78](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 classification59](https://savrn.com/models?task=Video+classification) - [Image to text58](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,777](https://savrn.com/models?library=transformers) - [peft345](https://savrn.com/models?library=peft) - [diffusers187](https://savrn.com/models?library=diffusers) - [pytorch178](https://savrn.com/models?library=pytorch) - [gguf167](https://savrn.com/models?library=gguf) - [lerobot139](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [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) - [vllm38](https://savrn.com/models?library=vllm) - [onnx36](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) - [exllamav316](https://savrn.com/models?library=exllamav3) - [openpi16](https://savrn.com/models?library=openpi) - [litert15](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,473](https://savrn.com/models?modality=Other) - [Text2,612](https://savrn.com/models?modality=Text) - [Image and text570](https://savrn.com/models?modality=Image+and+text) - [Image565](https://savrn.com/models?modality=Image) - [Audio478](https://savrn.com/models?modality=Audio) - [Control431](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) - [Tabular56](https://savrn.com/models?modality=Tabular) License - [apache-2.03,105](https://savrn.com/models?license=apache-2.0) - [mit1,246](https://savrn.com/models?license=mit) - [other703](https://savrn.com/models?license=other) - [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma62](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](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) - [llama3.128](https://savrn.com/models?license=llama3.1) - [unknown27](https://savrn.com/models?license=unknown) - [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-m11](https://savrn.com/models?license=creativeml-openrail-m) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama27](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 weights11,087](https://savrn.com/models?access=open_access) - [Access requested at publisher355](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai94](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [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 Yuan63](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](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) - [Yurun Yuan48](https://savrn.com/models?publisher=yuruny) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [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) 11,442 models, sorted by most downloaded. Model ### [lvr-export-lvr_pet_textcot_10k_nonema](https://savrn.com/models/lvr-export-lvr-pet-textcot-10k-nonema) [Rlyang](https://savrn.com/model-publishers/yrlyrl) Open weights apache-2.0 14.6B parameters 32,768 tokens [View model](https://savrn.com/models/lvr-export-lvr-pet-textcot-10k-nonema) Model ### [lvr-xfer](https://savrn.com/models/lvr-xfer) [Rlyang](https://savrn.com/model-publishers/yrlyrl) Open weights [View model](https://savrn.com/models/lvr-xfer) Model ### [lxsone](https://savrn.com/models/lxsone) [Nigel White](https://savrn.com/model-publishers/muzimi) Open weights mit [View model](https://savrn.com/models/lxsone) Model ### [lylyreader-tts-models](https://savrn.com/models/lylyreader-tts-models) [Jonatas](https://savrn.com/model-publishers/jonjonesbr) Modelos de TTS neural local para o LylyReader (app Android de audiobooks, repo JonJonesBR/LylyReader-Android). Infraestrutura de modelo do projeto num lugar só — hospedar aqui evita depender de repositórios de terceiros que podem cair ou ser removidos. Por que FP32 (2026-09-03): o bundle INT8 (kokoro-int8-multi-lang-v10.zip, ainda hospedado como fallback/arquivo) tem artefato auditivo de sibilo/chiado agudo (ressalto ~9,6kHz + ruído de quantização — validado por ouvido contra o FP32, que é limpo). O app baixa o FP32. filesDir/kokoro/ (mesmo mecanismo para os bundles da Parte A). Fonte original: release tts-models do k2-fsa/sherpa-onnx. Licença Apache-2.0. mms-tts-por-v1.zip — voz VITS pt-BR… Open weights [View model](https://savrn.com/models/lylyreader-tts-models) Model · Text to image ### [m](https://savrn.com/models/m) [C](https://savrn.com/model-publishers/craftlyrobotmushfiqur) We are excited to open-source Qwen-Image-2.1, a unified text-to-image generation and image editing model in the Qwen family. With just 7B parameters in its visual generation component (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility. Four key improvements define this release: - Compact and Efficient — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost. - Native Transparency, Unified Creation and Editing — Generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs—all in one… Open weights other 7.1B parameters diffusers [View model](https://savrn.com/models/m) Model ### [m-056f62fd](https://savrn.com/models/m-056f62fd) [Dsa](https://savrn.com/model-publishers/daslol) Open weights 25.8B parameters 262,144 tokens [View model](https://savrn.com/models/m-056f62fd) Model ### [m-16ae98f9ac20](https://savrn.com/models/m-16ae98f9ac20) [i love SSD](https://savrn.com/model-publishers/aoiandroid) Open weights [View model](https://savrn.com/models/m-16ae98f9ac20) Model ### [m-1ad9399f](https://savrn.com/models/m-1ad9399f) [Hundred](https://savrn.com/model-publishers/subnethundred) Open weights 25.8B parameters 262,144 tokens [View model](https://savrn.com/models/m-1ad9399f) Model · Text generation ### [m-a-p__YuE-s2-1B-general](https://savrn.com/models/m-a-p-yue-s2-1b-general) [Allinsama](https://savrn.com/model-publishers/allinsama) Our model's name is YuE (乐). In Chinese, the word means "music" and "happiness." Some of you may find words that start with Yu hard to pronounce. If so, you can just call it "yeah." We wrote a song with our model's name. YuE is a groundbreaking series of open-source foundation models designed for music generation, specifically for transforming lyrics into full songs (lyrics2song). It can generate a complete song, lasting several minutes, that includes both a catchy vocal track and accompaniment track. YuE is capable of modeling diverse genres/languages/vocal techniques. Please visit the Demo Page for amazing vocal performance. Join Us on Discord! 2025.03.12 Paper Released: We now release… Open weights apache-2.0 2B parameters 8,192 tokens [View model](https://savrn.com/models/m-a-p-yue-s2-1b-general) Model ### [m1001a](https://savrn.com/models/m1001a) [S. M.](https://savrn.com/model-publishers/mitawa) Open weights [View model](https://savrn.com/models/m1001a) Model ### [m9b1UG1dcgh7IUBR](https://savrn.com/models/m9b1ug1dcgh7iubr) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/m9b1ug1dcgh7iubr) Model ### [mae-baseline-2024](https://savrn.com/models/mae-baseline-2024) [Tobias Schwarz](https://savrn.com/model-publishers/tobi-asskv) A small Mae implementation for Multitask, packaged with an explicit configuration and an initialization checkpoint. The huge variant is a reproducible starting point, not a trained model release. - 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 adam with a constant warmup schedule. These are starting values in the script, not… Open weights apache-2.0 33,088 parameters 256 tokens [View model](https://savrn.com/models/mae-baseline-2024) Model ### [mae-classification-2024](https://savrn.com/models/mae-classification-2024) [Minho B. Hwang](https://savrn.com/model-publishers/minhohwang) This is an experimental Mae codebase for Classification. It keeps the small setup intentionally manageable so architecture changes can be inspected before a full training run. - 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 rmsprop with a onecycle schedule. These are starting values in the script, not evidence of a completed run.… Open weights bsd-3-clause 16,576 parameters 256 tokens [View model](https://savrn.com/models/mae-classification-2024) Model ### [mae-classification-distilled76](https://savrn.com/models/mae-classification-distilled76) [Leticia Moreira](https://savrn.com/model-publishers/justleticiamoreira) A research-oriented Mae prototype targeting Classification. The included large 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 adamw with a step schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights bsd-3-clause 24,832 parameters 256 tokens [View model](https://savrn.com/models/mae-classification-distilled76) Model ### [mae-contrastive](https://savrn.com/models/mae-contrastive) [Rahu Lsin](https://savrn.com/model-publishers/rahu-lsin) Working implementation of Mae for Contrastive using a huge configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - 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 sgd with a onecycle schedule. These are starting values in the script, not evidence of a… Open weights mit 16,576 parameters 512 tokens [View model](https://savrn.com/models/mae-contrastive) Model ### [mae-demo](https://savrn.com/models/mae-demo) [Matthew W. Carter](https://savrn.com/model-publishers/matthewcardin) Working implementation of Mae for Classification using a base configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - 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 adam with a constant warmup schedule. These are starting values in the script, not evidence of… Open weights mit 16,576 parameters 512 tokens [View model](https://savrn.com/models/mae-demo) Model ### [mae-finetuned](https://savrn.com/models/mae-finetuned) [Lauren Wood](https://savrn.com/model-publishers/laurenwood) This is an experimental Mae codebase for Retrieval. It keeps the base setup intentionally manageable so architecture changes can be inspected before a full training run. - 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 rmsprop with a constant warmup schedule. These are starting values in the script, not evidence of a completed run.… Open weights apache-2.0 49,600 parameters 512 tokens [View model](https://savrn.com/models/mae-finetuned) Model ### [mae-generation-large](https://savrn.com/models/mae-generation-large) [Chunhung Lee](https://savrn.com/model-publishers/chunh-unglkm) A research-oriented Mae prototype targeting Generation. The included large 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 sgd with a exponential schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 24,832 parameters 256 tokens [View model](https://savrn.com/models/mae-generation-large) Model ### [magic1](https://savrn.com/models/magic1) [Infinite3214](https://savrn.com/model-publishers/infinite3214) Open weights [View model](https://savrn.com/models/magic1) Model ### [magic2](https://savrn.com/models/magic2) [Infinite3214](https://savrn.com/model-publishers/infinite3214) Open weights [View model](https://savrn.com/models/magic2) Model ### [magic3](https://savrn.com/models/magic3) [Infinite3214](https://savrn.com/model-publishers/infinite3214) Open weights [View model](https://savrn.com/models/magic3) Model ### [mainsweep4ep-base-s0-realign120](https://savrn.com/models/mainsweep4ep-base-s0-realign120) [Julia Rezvani](https://savrn.com/model-publishers/jlsrls) This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/mainsweep4ep-base-s0-realign120) Model ### [mainsweep4ep-base-s1-realign120](https://savrn.com/models/mainsweep4ep-base-s1-realign120) [Julia Rezvani](https://savrn.com/model-publishers/jlsrls) This model is a fine-tuned version of unsloth/Llama-3.2-1B-Instruct. It has been trained using TRL. This model was trained with SFT. Open weights transformers [View model](https://savrn.com/models/mainsweep4ep-base-s1-realign120) Model ### [mainsweep4ep-ctrl-s0-logitrl120](https://savrn.com/models/mainsweep4ep-ctrl-s0-logitrl120) [Julia Rezvani](https://savrn.com/model-publishers/jlsrls) 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 transformers [View model](https://savrn.com/models/mainsweep4ep-ctrl-s0-logitrl120) ## 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.