# Models, Page 376 of 477 Source: https://savrn.com/models/page/376 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 ### [mum04](https://savrn.com/models/mum04) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum04) Model ### [mum04b](https://savrn.com/models/mum04b) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum04b) Model ### [mum05](https://savrn.com/models/mum05) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum05) Model ### [mum05b](https://savrn.com/models/mum05b) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum05b) Model ### [mum06](https://savrn.com/models/mum06) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum06) Model ### [mum06b](https://savrn.com/models/mum06b) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum06b) Model ### [mum07](https://savrn.com/models/mum07) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum07) Model ### [mum07b](https://savrn.com/models/mum07b) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum07b) Model ### [mum08](https://savrn.com/models/mum08) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum08) Model ### [mum08b](https://savrn.com/models/mum08b) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum08b) Model ### [mum09](https://savrn.com/models/mum09) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum09) Model ### [mum09b](https://savrn.com/models/mum09b) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum09b) Model ### [mum10](https://savrn.com/models/mum10) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum10) Model ### [mum10b](https://savrn.com/models/mum10b) [Oa](https://savrn.com/model-publishers/mama-chen) Open weights [View model](https://savrn.com/models/mum10b) Model ### [muraqib-judge-GGUF](https://savrn.com/models/muraqib-judge-gguf) [Mohamed Al Balkhi](https://savrn.com/model-publishers/mohamedbalkhi169) The on-device judge of Muraqib, a desktop tool that checks posts from watched social-media accounts against editorial rules an auditor writes, and shows the auditor the cases to decide on. - Use it with the base model's vision file (mmproj-F16.gguf from unsloth/gemma-4-E4B-it-GGUF) for photos, video frames and sound. - It expects Muraqib's own system prompt and JSON answer shape (notes, claims, then one verdict per rule). Used any other way, it behaves like a slightly changed Gemma 4 E4B. Open weights apache-2.0 [View model](https://savrn.com/models/muraqib-judge-gguf) Model ### [muse-glimmer-30b-topo-governed-cbp-new-version](https://savrn.com/models/muse-glimmer-30b-topo-governed-cbp-new-version) [Frank Morales](https://savrn.com/model-publishers/frankmorales2020) This model checkpoint represents the full integration of Topological Continual Backpropagation (Topo-CBP) with the Hugging Face Trainer on the 30B multimodal architecture Muse-Glimmer-30B. The training workflow combines standard Hugging Face orchestration with bare-silicon parameter governance via TopoAdamW across sequential downstream classification tasks. code: https://github.com/frank-morales2020/AST/blob/main/TOPOMUSEHFTRAINER.ipynb article: https://medium.com/ai-simplified-in-plain-english/the-architectural-amnesia-of-metas-muse-solving-catastrophic-forgetting-without-the-cloud-crutch-9b839345138e The model and training ran successfully to the end across all three tasks, demonstrating… Open weights apache-2.0 [View model](https://savrn.com/models/muse-glimmer-30b-topo-governed-cbp-new-version) Model ### [musebystableyogi_v35fp8extended](https://savrn.com/models/musebystableyogi-v35fp8extended) [Max Nazarov](https://savrn.com/model-publishers/kentus) Open weights [View model](https://savrn.com/models/musebystableyogi-v35fp8extended) Model ### [music7b_ckpt400](https://savrn.com/models/music7b-ckpt400) [Le](https://savrn.com/model-publishers/ldlfighting) Open weights 7.6B parameters 32,768 tokens [View model](https://savrn.com/models/music7b-ckpt400) Model ### [musicnote-ondevice-models](https://savrn.com/models/musicnote-ondevice-models) [Baron Ed](https://savrn.com/model-publishers/baroned) ONNX exports of published music-transcription models, used by the audio → sheet music tool on musicnote.app. The browser downloads them once and runs them on the user's device with onnxruntime-web; recordings never leave the device. These are conversions, not new models. Each file keeps the licence of the weights it was converted from. Every file's size and SHA-256 are in manifest.json. Attribution (CC BY 4.0, piano model): "High-resolution Piano Transcription with Pedals by Regressing Onset and Offset Times", Qiuqiang Kong, Bochen Li, Xuchen Song, Yuan Wan, Yuxuan Wang, 2020 — weights from Zenodo record 4034264, licensed under CC BY 4.0. Changes: converted to ONNX, pedal sub-network… Open weights other [View model](https://savrn.com/models/musicnote-ondevice-models) Model ### [mxk54szjmasff2zfknrh5enl](https://savrn.com/models/mxk54szjmasff2zfknrh5enl) [Mike Well](https://savrn.com/model-publishers/mikeone) Open weights [View model](https://savrn.com/models/mxk54szjmasff2zfknrh5enl) Model ### [my-awesome-model](https://savrn.com/models/my-awesome-model) [A21DSAD](https://savrn.com/model-publishers/asdczx12ds) This is a state-of-the-art model trained on multiple benchmarks. All scores are reported to 3 decimal places. Open weights [View model](https://savrn.com/models/my-awesome-model) Model ### [my-classification](https://savrn.com/models/my-classification) [Leo Moore](https://savrn.com/model-publishers/imvictornugroho85) A research-oriented CLIP prototype targeting Classification. The included base 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 polynomial schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights bsd-3-clause 24,832 parameters 128 tokens [View model](https://savrn.com/models/my-classification) Model ### [my-classification](https://savrn.com/models/my-classification-2) [Victor JOHNSON](https://savrn.com/model-publishers/timdegroot80) This is an experimental Poolformer codebase for Classification. It keeps the large 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 polynomial schedule. These are starting values in the script, not evidence of a completed… Open weights bsd-3-clause 24,832 parameters 256 tokens [View model](https://savrn.com/models/my-classification-2) Model ### [my-cool-model](https://savrn.com/models/my-cool-model) [Brym](https://savrn.com/model-publishers/brymstone) Open weights [View model](https://savrn.com/models/my-cool-model) ## 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.