# Models, Page 370 of 477 Source: https://savrn.com/models/page/370 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 ### [mms-tts-hat-train](https://savrn.com/models/mms-tts-hat-train) [John Styves Vallon](https://savrn.com/model-publishers/re100) 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 83M parameters transformers [View model](https://savrn.com/models/mms-tts-hat-train) Model ### [mobilenetv4-ham10000](https://savrn.com/models/mobilenetv4-ham10000) [DACNTT&KLTN](https://savrn.com/model-publishers/dacntt-kltn) Open weights [View model](https://savrn.com/models/mobilenetv4-ham10000) Model ### [mobilevit-demo](https://savrn.com/models/mobilevit-demo) [Trevor Walker](https://savrn.com/model-publishers/trevorwalker) This repository is a compact, custom PyTorch implementation of Mobilevit for Retrieval. The giant configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 sgd with a cosine schedule. These are… Open weights bsd-3-clause 33,088 parameters 256 tokens [View model](https://savrn.com/models/mobilevit-demo) Model ### [mobilevit-demo](https://savrn.com/models/mobilevit-demo-2) [Mart Inez](https://savrn.com/model-publishers/mart-inez) Working implementation of Mobilevit for Classification using a tiny 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 novograd with a step schedule. These are starting values in the script, not evidence of… Open weights apache-2.0 16,576 parameters 256 tokens [View model](https://savrn.com/models/mobilevit-demo-2) Model ### [mobilevit-experiment](https://savrn.com/models/mobilevit-experiment) [A. Smith](https://savrn.com/model-publishers/anthonysmithcih) This is an experimental Mobilevit codebase for Retrieval. It keeps the huge 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 novograd with a onecycle schedule. These are starting values in the script, not evidence of a completed run.… Open weights mit 16,576 parameters 512 tokens [View model](https://savrn.com/models/mobilevit-experiment) Model ### [mobilevit-experiment](https://savrn.com/models/mobilevit-experiment-2) [Bintang V. Rahman](https://savrn.com/model-publishers/bintangrahman) This repository is a compact, custom PyTorch implementation of Mobilevit for Multitask. The xlarge configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 adamw with a onecycle schedule. These are… Open weights apache-2.0 16,576 parameters 128 tokens [View model](https://savrn.com/models/mobilevit-experiment-2) Model ### [mobilevit-experiment-2024](https://savrn.com/models/mobilevit-experiment-2024) [Hazel White](https://savrn.com/model-publishers/josephbro) This is an experimental Mobilevit codebase for Retrieval. 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 lamb with a cosine schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 33,088 parameters 512 tokens [View model](https://savrn.com/models/mobilevit-experiment-2024) Model ### [mobilevit-retrieval](https://savrn.com/models/mobilevit-retrieval) [Amitiyerpaw](https://savrn.com/model-publishers/amitiyerpaw) A small Mobilevit implementation for Retrieval, packaged with an explicit configuration and an initialization checkpoint. The small 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 linear warmup schedule. These are starting values in the script, not… Open weights apache-2.0 49,600 parameters 512 tokens [View model](https://savrn.com/models/mobilevit-retrieval) Model ### [mobilevit-retrieval-dev](https://savrn.com/models/mobilevit-retrieval-dev) [A. Lopez](https://savrn.com/model-publishers/alejandrolru) A small Mobilevit implementation for Retrieval, packaged with an explicit configuration and an initialization checkpoint. The xlarge 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 onecycle schedule. These are starting values in the script, not… Open weights bsd-3-clause 24,832 parameters 256 tokens [View model](https://savrn.com/models/mobilevit-retrieval-dev) Model ### [mobilevit-retrieval-fast](https://savrn.com/models/mobilevit-retrieval-fast) [Maksym Oliynyk](https://savrn.com/model-publishers/maksymohk) This repository is a compact, custom PyTorch implementation of Mobilevit for Retrieval. The huge configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 novograd with a cosine schedule. These are… Open weights apache-2.0 49,600 parameters 128 tokens [View model](https://savrn.com/models/mobilevit-retrieval-fast) Model ### [mocov3-baseline](https://savrn.com/models/mocov3-baseline) [Lyongarcia](https://savrn.com/model-publishers/lyongarcia) This is an experimental Mocov3 codebase for Classification. It keeps the tiny 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 adam with a constant warmup schedule. These are starting values in the script, not evidence of a completed… Open weights bsd-3-clause 33,088 parameters 128 tokens [View model](https://savrn.com/models/mocov3-baseline) Model ### [mocov3-baseline](https://savrn.com/models/mocov3-baseline-2) [Rymalhotra07](https://savrn.com/model-publishers/rymalhotra07) A small Mocov3 implementation for Retrieval, packaged with an explicit configuration and an initialization checkpoint. The tiny 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 lion with a exponential schedule. These are starting values in the script, not… Open weights apache-2.0 16,576 parameters 128 tokens [View model](https://savrn.com/models/mocov3-baseline-2) Model ### [mocov3-classification](https://savrn.com/models/mocov3-classification) [Kayla Cruz](https://savrn.com/model-publishers/kayla-cruz99) This repository is a compact, custom PyTorch implementation of Mocov3 for Classification. The huge configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 linear warmup schedule. These… Open weights apache-2.0 49,600 parameters 256 tokens [View model](https://savrn.com/models/mocov3-classification) Model ### [mocov3-classification](https://savrn.com/models/mocov3-classification-2) [Yoon Allen](https://savrn.com/model-publishers/allenyoon1988) Working implementation of Mocov3 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 rmsprop with a polynomial schedule. These are starting values in the script, not evidence… Open weights apache-2.0 33,088 parameters 128 tokens [View model](https://savrn.com/models/mocov3-classification-2) Model ### [mocov3-contrastive81](https://savrn.com/models/mocov3-contrastive81) [Kevin Nugroho](https://savrn.com/model-publishers/kevinjnugroho) This repository is a compact, custom PyTorch implementation of Mocov3 for Contrastive. The giant configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 lamb with a linear warmup schedule. These are… Open weights bsd-3-clause 24,832 parameters 512 tokens [View model](https://savrn.com/models/mocov3-contrastive81) Model ### [mocov3-experiment](https://savrn.com/models/mocov3-experiment) [Joko Purnomo](https://savrn.com/model-publishers/ppurnomojoko) A research-oriented Mocov3 prototype targeting Generation. The included small 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 lamb with a step schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 24,832 parameters 128 tokens [View model](https://savrn.com/models/mocov3-experiment) Model ### [mocov3-generation-distilled](https://savrn.com/models/mocov3-generation-distilled) [Atharvsinghana](https://savrn.com/model-publishers/atharvsinghana) A small Mocov3 implementation for Generation, packaged with an explicit configuration and an initialization checkpoint. The giant 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 adafactor with a constant warmup schedule. These are starting values in the script… Open weights mit 24,832 parameters 128 tokens [View model](https://savrn.com/models/mocov3-generation-distilled) Model ### [mocov3-multitask](https://savrn.com/models/mocov3-multitask) [Lucia Garcia](https://savrn.com/model-publishers/luciagarcia) This repository is a compact, custom PyTorch implementation of Mocov3 for Multitask. The nano configuration is intended for code review, smoke tests, and small controlled experiments rather than as a production-ready pretrained 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 rmsprop with a cosine schedule. These are… Open weights bsd-3-clause 16,576 parameters 256 tokens [View model](https://savrn.com/models/mocov3-multitask) Model ### [mocov3-multitask-efficient](https://savrn.com/models/mocov3-multitask-efficient) [Vitor PEREIRA](https://savrn.com/model-publishers/poapereira) A research-oriented Mocov3 prototype targeting Multitask. 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 rmsprop with a constant warmup 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/mocov3-multitask-efficient) Model ### [mocov3-retrieval](https://savrn.com/models/mocov3-retrieval) [Rizky Gunawan](https://savrn.com/model-publishers/rizkyghb) This is an experimental Mocov3 codebase for Retrieval. It keeps the xlarge 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 adamw with a polynomial schedule. These are starting values in the script, not evidence of a completed run.… Open weights mit 33,088 parameters 256 tokens [View model](https://savrn.com/models/mocov3-retrieval) Model · Image classification ### [modalityscan](https://savrn.com/models/modalityscan) [Arnab Pal](https://savrn.com/model-publishers/vectorsense) Single-label imaging-modality classification for medical images. One shared ConvNet backbone, two softmax heads (family, modality), shipped as a static-INT8 ONNX graph. Predicts what kind of image this is, not what is in it. Anatomy is a different model. Softmax, temperature and thresholds live outside the graph so calibration and policy stay configuration. modality-taxonomy.json travels with the weights - a 20-element logits vector is 20 meaningless numbers without it. Tier is a statement about evidence, not difficulty. modeltrained means the corpus had at least two independent sources and enough patients to make the number falsifiable. experimental means it did not. nottrained classes are… Open weights apache-2.0 onnx [View model](https://savrn.com/models/modalityscan) Model ### [model](https://savrn.com/models/model) [KazukiEgashira](https://savrn.com/model-publishers/kazuki1450) Open weights [View model](https://savrn.com/models/model) Model · Text to image ### [model-kurumi-krea2](https://savrn.com/models/model-kurumi-krea2) [KATSUNORI MUROOKA](https://savrn.com/model-publishers/misapapa71) A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps). Use the token model-Kurumi-krea2 to invoke the concept. Open weights apache-2.0 diffusers [View model](https://savrn.com/models/model-kurumi-krea2) Model ### [model-tempfiles](https://savrn.com/models/model-tempfiles) [Samir R.](https://savrn.com/model-publishers/sr5434) Open weights [View model](https://savrn.com/models/model-tempfiles) ## 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.