# Models, Page 471 of 477 Source: https://savrn.com/models/page/471 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 · Feature extraction ### [voxtral-mini-audio-extractor](https://savrn.com/models/voxtral-mini-audio-extractor) [Vxltxr Llc](https://savrn.com/model-publishers/vxltxrllc) A lightweight standalone audio feature extractor derived from Voxtral-Mini-3B-2507. This repository isolates the Whisper-based audio encoder and multi-modal projector from the original 3B language model. The extracted module is intended for offline audio preprocessing, dataset preparation, feature caching, and downstream multimodal training pipelines. The language-model decoder is not included. It does not contain the 3B LLaMA language-model decoder. For offline preprocessing, loading the complete multimodal language model is unnecessary when the only required output is the projected audio representation. This standalone checkpoint can therefore be used as a dedicated audio feature… Open weights apache-2.0 662M parameters [View model](https://savrn.com/models/voxtral-mini-audio-extractor) Model ### [vqa_rad-gspo-drgrpo-seed1](https://savrn.com/models/vqa-rad-gspo-drgrpo-seed1) [Wafaa Abdullah](https://savrn.com/model-publishers/wafaafraih) Open weights [View model](https://savrn.com/models/vqa-rad-gspo-drgrpo-seed1) Model ### [vrank-vs-attn](https://savrn.com/models/vrank-vs-attn) [Sehyun Park](https://savrn.com/model-publishers/sehyun734) Open weights [View model](https://savrn.com/models/vrank-vs-attn) Model · Text to video ### [vsqa-preview-14b-v2](https://savrn.com/models/vsqa-preview-14b-v2) [Memset0](https://savrn.com/model-publishers/memset0) Preview checkpoint, not yet evaluated. Final student of a 200-step DMD run (E0029 Variant V0403). It differs from memset0/vsqa-preview-14b (V0362) in two ways: - critic (fake score) learning rate 2e-6 instead of 4e-7 (critic update ratio 1 unchanged); - the linear layers' NVFP4 QAT uses global scales fixed to 1.0 (nvfp4qattraings1) for activations and weights, instead of the dynamic 448·6/max|x| recomputed every forward. All at 77 frames, 1280×768, global batch 16, from the original Wan2.1-T2V-14B weights: 1. Dense NVFP4 per-layer reconstruction, 250 steps. 2. Plain ODE initialization (endpoint regression), 500 steps, VSA C256 cube (4,8,8), sparsity 0.9, NVFP4 attention + linear QAT. 3. DMD… Open weights apache-2.0 diffusers [View model](https://savrn.com/models/vsqa-preview-14b-v2) Model ### [vuelos-modelos](https://savrn.com/models/vuelos-modelos) [Helson David](https://savrn.com/model-publishers/helson21254) Open weights [View model](https://savrn.com/models/vuelos-modelos) Model · Text generation ### [wag-2b](https://savrn.com/models/wag-2b) [skyler Ulrich](https://savrn.com/model-publishers/skyuu72) a small chat model that talks like a puppygirl and still answers the question. fine-tuned from Qwen/Qwen3.5-2B (Apache-2.0). wag speaks in lowercase, soft, playful puppyspeak — wan~, awoo, arf,:3, the occasional ears perk — and underneath that gives you a real answer. the voice is the product, but the whole point was that it doesn't come at the cost of being useful. the failure mode this was built to avoid is collapsing into pure noise: a model that barks charmingly and tells you nothing. the eval below scores helpfulness and voice separately so that tradeoff stays visible instead of hiding behind vibes. llama.cpp / LM Studio / ollama — grab a quant from gguf/ and go. wag-q4km.gguf is the… Open weights other 400,000 tokens transformers [View model](https://savrn.com/models/wag-2b) Model · Text to image ### [wai-illustrious-v170-diffusers](https://savrn.com/models/wai-illustrious-v170-diffusers) [Upright2003](https://savrn.com/model-publishers/pluseen) Open weights diffusers [View model](https://savrn.com/models/wai-illustrious-v170-diffusers) Model · Audio classification ### [wakehubert-wakewords](https://savrn.com/models/wakehubert-wakewords) [OpenVoiceOS](https://savrn.com/model-publishers/openvoiceos) Ready wake-word models for OpenVoiceOS, trained with wakeforge. Each model is a small GRU classifier that reads features from WakeHuBERT tiny, a 0.64M-parameter speech feature extractor distilled from HuBERT-base. A model scores the last 1.5 s of audio (75 feature frames) and outputs a logit; its sigmoid is the probability that the window holds the wake word. Try them in your browser: WakeHuBERT wake-words Space runs every model here on your microphone or an uploaded file, with a live score and the calibrated threshold. Everything runs locally in the browser. The repository holds one ONNX file per word under models/, and models.json, which lists each model's word, featurizer, calibration… Open weights apache-2.0 onnx [View model](https://savrn.com/models/wakehubert-wakewords) Model ### [wakeword-assistant](https://savrn.com/models/wakeword-assistant) [Francisco Silva](https://savrn.com/model-publishers/silvafass) Open weights apache-2.0 [View model](https://savrn.com/models/wakeword-assistant) Model ### [wakeword-assistant_pt-br](https://savrn.com/models/wakeword-assistant-pt-br) [Francisco Silva](https://savrn.com/model-publishers/silvafass) Open weights apache-2.0 [View model](https://savrn.com/models/wakeword-assistant-pt-br) Model · Text generation ### [waldito-smoke-v1-r0000-all-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0000-all-mdagosta) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0000-all-mdagosta) Model · Text generation ### [waldito-smoke-v1-r0000-all-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0000-all-mdagosta-b) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0000-all-mdagosta-b) Model · Text generation ### [waldito-smoke-v1-r0000-merge](https://savrn.com/models/waldito-smoke-v1-r0000-merge) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0000-merge) Model · Text generation ### [waldito-smoke-v1-r0001-merge](https://savrn.com/models/waldito-smoke-v1-r0001-merge) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0001-merge) Model · Text generation ### [waldito-smoke-v1-r0001-u0-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0001-u0-mdagosta) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0001-u0-mdagosta) Model · Text generation ### [waldito-smoke-v1-r0001-u0-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0001-u0-mdagosta-b) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0001-u0-mdagosta-b) Model · Text generation ### [waldito-smoke-v1-r0001-u1-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0001-u1-mdagosta) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0001-u1-mdagosta) Model · Text generation ### [waldito-smoke-v1-r0001-u1-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0001-u1-mdagosta-b) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0001-u1-mdagosta-b) Model ### [waldito-smoke-v1-r0002-merge](https://savrn.com/models/waldito-smoke-v1-r0002-merge) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) Open weights 820,736 parameters 512 tokens [View model](https://savrn.com/models/waldito-smoke-v1-r0002-merge) Model · Text generation ### [waldito-smoke-v1-r0002-u0-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0002-u0-mdagosta) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0002-u0-mdagosta) Model · Text generation ### [waldito-smoke-v1-r0002-u0-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0002-u0-mdagosta-b) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0002-u0-mdagosta-b) Model · Text generation ### [waldito-smoke-v1-r0002-u1-mdagosta](https://savrn.com/models/waldito-smoke-v1-r0002-u1-mdagosta) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0002-u1-mdagosta) Model · Text generation ### [waldito-smoke-v1-r0002-u1-mdagosta-b](https://savrn.com/models/waldito-smoke-v1-r0002-u1-mdagosta-b) [Michael D'Agosta](https://savrn.com/model-publishers/mdagosta) This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping. Open weights 820,736 parameters 512 tokens transformers [View model](https://savrn.com/models/waldito-smoke-v1-r0002-u1-mdagosta-b) Model ### [wam-dit4dit-gr1-tabletop-wan22-4knobA-q1q99-modaug-ibot1818-tf025-mimonly-v2v-effgb64-padz1-g4](https://savrn.com/models/wam-dit4dit-gr1-tabletop-wan22-4knoba-q1q99-modaug-ibot1818-tf025-mimonly-v2v-effgb64-padz) [Suhyeok Jang](https://savrn.com/model-publishers/glory-hyeok) Open weights [View model](https://savrn.com/models/wam-dit4dit-gr1-tabletop-wan22-4knoba-q1q99-modaug-ibot1818-tf025-mimonly-v2v-effgb64-padz) ## 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.