# Models, Page 428 of 464 Source: https://savrn.com/models/page/428 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 ### [spawnscene-models](https://savrn.com/models/spawnscene-models) [Todd Tanner](https://savrn.com/model-publishers/lostbeard) Models SpawnScene (https://spawnscene.com) runs on SpawnDev.ILGPU.ML in the browser, in a form that engine takes. MI-GAN (Picsart AI Research, "MI-GAN: A Simple Baseline for Image Inpainting on Mobile Devices", ICCV 2023), the plain 512 network migan.onnx from https://huggingface.co/andraniksargsyan/migan, MIT licence (LICENSE-MI-GAN, unchanged). Changed: float32 I/O instead of uint8 - inputs image [B,3,H,W] and mask [B,1,H,W] are FLOAT (values 0..255; mask 255 = known pixel, 0 = fill), the output result [B,3,H,W] is FLOAT 0..255, and the graph's four Cast(to=uint8) became Cast(to=float). Weights untouched. Against the original on onnxruntime: max |difference| 1/255 (rounding). Open weights mit [View model](https://savrn.com/models/spawnscene-models) Model ### [spd01](https://savrn.com/models/spd01) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd01) Model ### [spd01b](https://savrn.com/models/spd01b) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd01b) Model ### [spd02](https://savrn.com/models/spd02) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd02) Model ### [spd02b](https://savrn.com/models/spd02b) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd02b) Model ### [spd03](https://savrn.com/models/spd03) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd03) Model ### [spd03b](https://savrn.com/models/spd03b) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd03b) Model ### [spd04](https://savrn.com/models/spd04) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd04) Model ### [spd04b](https://savrn.com/models/spd04b) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd04b) Model ### [spd05](https://savrn.com/models/spd05) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd05) Model ### [spd05b](https://savrn.com/models/spd05b) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd05b) Model ### [spd06](https://savrn.com/models/spd06) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd06) Model ### [spd06b](https://savrn.com/models/spd06b) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd06b) Model ### [spd07](https://savrn.com/models/spd07) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd07) Model ### [spd08](https://savrn.com/models/spd08) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd08) Model ### [spd09](https://savrn.com/models/spd09) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd09) Model ### [spd10](https://savrn.com/models/spd10) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd10) Model ### [spd10b](https://savrn.com/models/spd10b) [Player](https://savrn.com/model-publishers/pes24) Open weights [View model](https://savrn.com/models/spd10b) Model · Image classification ### [species-classifier-arachnids](https://savrn.com/models/species-classifier-arachnids) [Michael G](https://savrn.com/model-publishers/mlgraham) A 153-species arachnid classifier (spiders, scorpions, ticks, harvestmen) small enough for microcontrollers and the Coral Edge TPU. MobileNetV2 at full width and 160 px, full-integer int8, trained on the iNaturalist 2021 competition data. It fills the gap people hit first with Google's AIY insect model, which has no spiders. Part of species-classifiers. Measured on the int8 file over the 1,530 iNaturalist 2021 validation images for these species (10 per species), with an 87.5% centre crop: The PyTorch checkpoint scores 64.4% top-1. For comparison, Google's stock AIY insect model scores 59.1% top-1 on the iNat2021 species it shares, choosing among 1,022 classes. - Input [1, 160, 160, 3]… Open weights apache-2.0 tflite [View model](https://savrn.com/models/species-classifier-arachnids) Model · Image classification ### [species-classifier-mammals](https://savrn.com/models/species-classifier-mammals) [Michael G](https://savrn.com/model-publishers/mlgraham) A 246-species mammal classifier small enough for microcontrollers and the Coral Edge TPU. MobileNetV2 at full width and 160 px, full-integer int8, trained on the iNaturalist 2021 competition data. Part of species-classifiers, which also rescues Google's AIY Vision Kit insect, plant and bird models into the same shape. Measured on the int8 file over the 2,460 iNaturalist 2021 validation images for these species (10 per species), with an 87.5% centre crop: The PyTorch checkpoint scores 56.1% top-1; int8 costs about a point. Grayscale is reported because the model was trained with 20% random grayscale so it holds up on monochrome cameras such as the Coral Dev Board Micro's. For comparison… Open weights apache-2.0 tflite [View model](https://savrn.com/models/species-classifier-mammals) Model ### [speech_to_asl](https://savrn.com/models/speech-to-asl) [Amey Chopde](https://savrn.com/model-publishers/bighammer21) Open weights mit keras [View model](https://savrn.com/models/speech-to-asl) Model ### [speecht5_tts_finetuned_fulfude](https://savrn.com/models/speecht5-tts-finetuned-fulfude) [Ndasi Joseph Noahye](https://savrn.com/model-publishers/ndasi-joseph-237) Open weights [View model](https://savrn.com/models/speecht5-tts-finetuned-fulfude) Model ### [spender](https://savrn.com/models/spender) [Peter Melchior](https://savrn.com/model-publishers/pmelchior) Open weights mit [View model](https://savrn.com/models/spender) Model ### [spi](https://savrn.com/models/spi) [Holder](https://savrn.com/model-publishers/desiree) Evidence-backed orchestration for generative media production. Not a text-to-video wrapper: SPI plans a production as structured state, renders it through swappable model backends, verifies results with evidence-carrying metrics, and (planned) repairs only what failed. See docs/architecture.md for the component map and docs/researchreport.md for the Phase-2 backend decision (Wan 2.2, Apache-2.0; LTX-2 as fast secondary). Mock mode (SPIMODE=mock equivalent: SPIApi(mode="mock")) needs no GPU. - SPIMODE=mock — zero GPU, deterministic artifacts (current default) - SPIMODE=small — reserved for Wan 2.1-1.3B / LTX-Video-2B smoke tests (Phase 2) - SPIMODE=production — reserved for Wan 2.2 A14B… Open weights [View model](https://savrn.com/models/spi) ## 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.