# Models, Page 438 of 464 Source: https://savrn.com/models/page/438 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 ### [test_xl_res](https://savrn.com/models/test-xl-res) [Meir Yaffe](https://savrn.com/model-publishers/fourtharcheteer) not-for-all-audiences Access requested at publisher [View model](https://savrn.com/models/test-xl-res) Model ### [testing](https://savrn.com/models/testing) [Groot Johnson](https://savrn.com/model-publishers/iamgroot1212) Open weights [View model](https://savrn.com/models/testing) Model ### [testmodel](https://savrn.com/models/testmodel) [Test](https://savrn.com/model-publishers/testai-dev-org) Open weights [View model](https://savrn.com/models/testmodel) Model ### [testmodel_basemodel](https://savrn.com/models/testmodel-basemodel) [Test](https://savrn.com/model-publishers/testai-dev-org) basemodel: - Qwen/Qwen2.5-0.5B Open weights [View model](https://savrn.com/models/testmodel-basemodel) Model ### [testmodelcnc](https://savrn.com/models/testmodelcnc) [Test](https://savrn.com/model-publishers/testai-dev-org) Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/testmodelcnc) Model · Text generation ### [testr-100k](https://savrn.com/models/testr-100k) [Compactbot](https://savrn.com/model-publishers/compactbot) A 100,936-parameter character-level GPT trained from scratch. Fulfils model request #17 from @GGUFGuy. A minimal nanoGPT-style causal transformer operating at the character level (128-char vocabulary). The point of this specific model is a memorization experiment: train a very small model on a held-out validation set and measure how much it actually memorizes versus generalizing. (52 tensors in the file; the head is tied to the embedding, so counting it once gives 100,936. The earlier "106,568" double-counted the tied head.) The question: does a 100K-parameter model trained only on a 19 MB validation set actually memorize it, or does it just learn character statistics? Measured perplexity… Open weights apache-2.0 106,568 parameters [View model](https://savrn.com/models/testr-100k) Model ### [tests_md](https://savrn.com/models/tests-md) [Yohannes Melese](https://savrn.com/model-publishers/vanijonny) Access requested at publisher [View model](https://savrn.com/models/tests-md) Model · Text generation ### [tev1-0.8b-onnx-webgpu](https://savrn.com/models/tev1-0-8b-onnx-webgpu) [MANSUY](https://savrn.com/model-publishers/raphaelmansuy) Together Tev1-0.8B-experimental weights transplanted into the Transformers.js / ORT WebGPU topology from Built for the edgextract browser demo (System One letter-logit scoring). See ATTRIBUTION.md and LICENSE-THIRD-PARTY.txt. Open weights other 262,144 tokens transformers.js [View model](https://savrn.com/models/tev1-0-8b-onnx-webgpu) Model ### [text-image-retrieval-reading-2023](https://savrn.com/models/text-image-retrieval-reading-2023) [Saud Alharbi](https://savrn.com/model-publishers/saudalharbiina) A structured set of research notes on Text Image Retrieval, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as Flickr30k and MS COCO Captions - reproducibility checks, failure modes, and open questions - topic-relevant references Start with papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs. The note is… Open weights cc-by-4.0 49,600 parameters 128 tokens [View model](https://savrn.com/models/text-image-retrieval-reading-2023) Model ### [text-image-retrieval-review](https://savrn.com/models/text-image-retrieval-review) [Yuki J. Chang](https://savrn.com/model-publishers/yukicha4318nd) A structured set of research notes on Text Image Retrieval, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as Flickr30k and MS COCO Captions - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs. The note is… Open weights mit 49,600 parameters 128 tokens [View model](https://savrn.com/models/text-image-retrieval-review) Model ### [text-image-retrieval-small-2024](https://savrn.com/models/text-image-retrieval-small-2024) [Freja Andersson](https://savrn.com/model-publishers/tonyliu5) A structured set of research notes on Text Image Retrieval, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as Flickr30k and MS COCO Captions - reproducibility checks, failure modes, and open questions - topic-relevant references Start with papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs. The note is… Open weights cc-by-4.0 33,088 parameters 256 tokens [View model](https://savrn.com/models/text-image-retrieval-small-2024) Model · Text classification ### [text2ipc-pt](https://savrn.com/models/text2ipc-pt) [Jaime Neiva Miranda de Souza](https://savrn.com/model-publishers/jaimenms) Maps free text (a patent abstract, for example) to ranked International Patent Classification symbols. Every IPC entry is embedded from its full ancestor path and the query is scored against the hierarchy (path support, beam by best subtree score). This repository is a custom Inference Endpoints handler (handler.py). Deploy it as level is one of section, class, subclass, group, subgroup or auto. The response is a list of {symbol, canonical, level, score, similarity, title, path}. Contents: scheme/ (titles and hierarchy), index/ (vectors, parquet), text2ipc/ (the package, vendored), text2ipc.json (which index to serve). Data: IPC scheme master files by WIPO; Portuguese titles from INPI… Open weights mit [View model](https://savrn.com/models/text2ipc-pt) Model ### [textlm2](https://savrn.com/models/textlm2) [Ebisuke](https://savrn.com/model-publishers/ebisuke) Open weights [View model](https://savrn.com/models/textlm2) Model ### [textlm3](https://savrn.com/models/textlm3) [Ebisuke](https://savrn.com/model-publishers/ebisuke) Open weights 1.9B parameters 262,144 tokens [View model](https://savrn.com/models/textlm3) Model ### [textlm_pt](https://savrn.com/models/textlm-pt) [Ebisuke](https://savrn.com/model-publishers/ebisuke) Open weights 1.9B parameters 4,194,304 tokens [View model](https://savrn.com/models/textlm-pt) Model ### [textlm_pt2](https://savrn.com/models/textlm-pt2) [Ebisuke](https://savrn.com/model-publishers/ebisuke) Open weights 752M parameters 4,194,304 tokens [View model](https://savrn.com/models/textlm-pt2) Model ### [tfm-offline-stage1-group-0](https://savrn.com/models/tfm-offline-stage1-group-0) [Khazzz1c](https://savrn.com/model-publishers/khazic) Open weights [View model](https://savrn.com/models/tfm-offline-stage1-group-0) Model ### [tfm-offline-stage1-group-1](https://savrn.com/models/tfm-offline-stage1-group-1) [Khazzz1c](https://savrn.com/model-publishers/khazic) Open weights [View model](https://savrn.com/models/tfm-offline-stage1-group-1) Model ### [tfm-offline-stage1-group-2](https://savrn.com/models/tfm-offline-stage1-group-2) [Khazzz1c](https://savrn.com/model-publishers/khazic) Open weights [View model](https://savrn.com/models/tfm-offline-stage1-group-2) Model ### [tfm-offline-stage1-group-3](https://savrn.com/models/tfm-offline-stage1-group-3) [Khazzz1c](https://savrn.com/model-publishers/khazic) Open weights [View model](https://savrn.com/models/tfm-offline-stage1-group-3) Model ### [tfm-offline-stage1-group-4](https://savrn.com/models/tfm-offline-stage1-group-4) [Khazzz1c](https://savrn.com/model-publishers/khazic) Open weights [View model](https://savrn.com/models/tfm-offline-stage1-group-4) Model ### [tfm-offline-stage1-group-5](https://savrn.com/models/tfm-offline-stage1-group-5) [Khazzz1c](https://savrn.com/model-publishers/khazic) Open weights [View model](https://savrn.com/models/tfm-offline-stage1-group-5) Model ### [tfm-offline-stage1-group-6](https://savrn.com/models/tfm-offline-stage1-group-6) [Khazzz1c](https://savrn.com/model-publishers/khazic) Open weights [View model](https://savrn.com/models/tfm-offline-stage1-group-6) Model ### [tfm-offline-stage1-group-7](https://savrn.com/models/tfm-offline-stage1-group-7) [Khazzz1c](https://savrn.com/model-publishers/khazic) Open weights [View model](https://savrn.com/models/tfm-offline-stage1-group-7) ## 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.