# Models, Page 363 of 477 Source: https://savrn.com/models/page/363 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 · Zero-shot classification ### [malkuth-4b](https://savrn.com/models/malkuth-4b) [Sae Chan O](https://savrn.com/model-publishers/dhtocks) A small multilingual decision model for classification, with a focus on Korean. Given a text, a question, and a set of criteria, Malkuth returns probabilities over the possible choices instead of generating text. Malkuth-4B is a LoRA adapter trained on Kev and built on a frozen Qwen/Qwen3.5-4B-Base. It has 33.8M trainable parameters and a 124 MB adapter. - choice: select one of up to 255 options - noul: yes or no - score: select an ordered level Multiple questions can be evaluated against the same input. Questions are evaluated independently, so they cannot see each other's answers. Probabilities are temperature-calibrated on a held-out validation split. Malkuth is served through Kev and… Open weights cc-by-nc-4.0 peft [View model](https://savrn.com/models/malkuth-4b) Model ### [mangadict-models](https://savrn.com/models/mangadict-models) [Craig Kolb](https://savrn.com/model-publishers/craigkolb) ONNX models used by the mangadict browser extension to find and read Japanese text in manga pages. They are repackaged from the models below for in-browser inference with onnxruntime-web; see those repos for training details and their licenses (all Apache-2.0). Use tagged revisions (e.g. v1); main may change. Open weights apache-2.0 [View model](https://savrn.com/models/mangadict-models) Model ### [manifold-1.0-1b](https://savrn.com/models/manifold-1-0-1b) [Naveen Mysore](https://savrn.com/model-publishers/nmysore) Attention manifold checkpoint for LLaMA 3.2 1B Instruct. This model extends the standard transformer attention mechanism with learned manifold structures on the value path. The checkpoint contains the manifold parameters only — the base model weights are loaded from meta-llama/Llama-3.2-1B-Instruct. See the LLM Inspector for the full visualization and editing toolkit. Paper forthcoming. Open weights llama3.2 [View model](https://savrn.com/models/manifold-1-0-1b) Model ### [map_imgs_gpt](https://savrn.com/models/map-imgs-gpt) [Salia Coel](https://savrn.com/model-publishers/saliacoel) Open weights [View model](https://savrn.com/models/map-imgs-gpt) Model ### [margent-mcq-rsi](https://savrn.com/models/margent-mcq-rsi) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi) Model ### [margent-mcq-rsi-aqua_v2](https://savrn.com/models/margent-mcq-rsi-aqua-v2) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-aqua-v2) Model ### [margent-mcq-rsi-aqua_v3](https://savrn.com/models/margent-mcq-rsi-aqua-v3) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-aqua-v3) Model ### [margent-mcq-rsi-aqua_v3r5](https://savrn.com/models/margent-mcq-rsi-aqua-v3r5) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-aqua-v3r5) Model ### [margent-mcq-rsi-base_manager](https://savrn.com/models/margent-mcq-rsi-base-manager) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-base-manager) Model ### [margent-mcq-rsi-medqa_v2](https://savrn.com/models/margent-mcq-rsi-medqa-v2) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-medqa-v2) Model ### [margent-mcq-rsi-medqa_v3](https://savrn.com/models/margent-mcq-rsi-medqa-v3) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-medqa-v3) Model ### [margent-mcq-rsi-medqa_v3r5](https://savrn.com/models/margent-mcq-rsi-medqa-v3r5) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-medqa-v3r5) Model ### [margent-mcq-rsi-mmlu_pro_v3g_dyn](https://savrn.com/models/margent-mcq-rsi-mmlu-pro-v3g-dyn) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-mmlu-pro-v3g-dyn) Model ### [margent-mcq-rsi-mmlu_pro_v3g_sta](https://savrn.com/models/margent-mcq-rsi-mmlu-pro-v3g-sta) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-mmlu-pro-v3g-sta) Model ### [margent-mcq-rsi-mmlu_pro_v3g_suc](https://savrn.com/models/margent-mcq-rsi-mmlu-pro-v3g-suc) [Madison](https://savrn.com/model-publishers/maliddd) Open weights [View model](https://savrn.com/models/margent-mcq-rsi-mmlu-pro-v3g-suc) Model ### [maria_lora](https://savrn.com/models/maria-lora) [Zac Bates](https://savrn.com/model-publishers/crimsonmonkey) Open weights [View model](https://savrn.com/models/maria-lora) Model ### [marian-finetuned-kde4-en-to-fr](https://savrn.com/models/marian-finetuned-kde4-en-to-fr) [Thanapol Singjaroen](https://savrn.com/model-publishers/thanapolsjr) Open weights [View model](https://savrn.com/models/marian-finetuned-kde4-en-to-fr) Model · Text generation ### [mario-html-4B-GGUF](https://savrn.com/models/mario-html-4b-gguf) [Wesjos Max](https://savrn.com/model-publishers/wesjos) A visual-UI language model fine-tuned from Qwen3.5-4B, quantized to GGUF for llama.cpp. It answers in a small semantic HTML component system — bento grids, metric cards, steppers, callouts — wrapped in one root, instead of plain Markdown. Reasoning-native, and no system prompt required. One root element; everything else is an optional child: ai-view-header is not in the source repo's theme.css — it renders with default header styling. Fine, but define your own if you want tighter control. Apache License 2.0. Open weights apache-2.0 transformers [View model](https://savrn.com/models/mario-html-4b-gguf) Model ### [mario-html-v2-4B-GGUF](https://savrn.com/models/mario-html-v2-4b-gguf) [Wesjos Max](https://savrn.com/model-publishers/wesjos) Open weights [View model](https://savrn.com/models/mario-html-v2-4b-gguf) Model ### [mario-html-v2-9B-GGUF](https://savrn.com/models/mario-html-v2-9b-gguf) [Wesjos Max](https://savrn.com/model-publishers/wesjos) Open weights [View model](https://savrn.com/models/mario-html-v2-9b-gguf) Model · Robotics ### [marker_test_new_camera_setup_20260920_221944](https://savrn.com/models/marker-test-new-camera-setup-20260920-221944) [Krishaanth Ramaraj](https://savrn.com/model-publishers/krish5831) Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning… Open weights apache-2.0 52M parameters lerobot [View model](https://savrn.com/models/marker-test-new-camera-setup-20260920-221944) Model ### [marmo-ckpt](https://savrn.com/models/marmo-ckpt) [Lawrence](https://savrn.com/model-publishers/lawrshen) Open weights [View model](https://savrn.com/models/marmo-ckpt) Model · Image to 3d ### [matchanu-1.5](https://savrn.com/models/matchanu-1-5) [PYGRASSREAL](https://savrn.com/model-publishers/pygrassreal) Matchanu 1.5 is PyGrassReal's generative 3D asset engine capable of synthesizing production-ready watertight 3D meshes (STL, OBJ, PLY) and real-time 3D Gaussian Splats from text descriptions or single 2D concept images. Designed for Industrial Prototyping (3D Printing), Game & AR/VR Assets, and Architectural Elements, Matchanu 1.5 generates clean topology, UV unwrappings, and volume-accurate meshes without the point cloud artifacts common to open-source prototypes. 1. Prompt & Image-to-3D Mesh: - Converts single-view product photos or text prompts into clean, manifold 3D meshes ready for Blender, Rhino, Maya, or Unreal Engine. 2. 3D Printing Ready (Watertight Topology): - Automatically… Open weights other [View model](https://savrn.com/models/matchanu-1-5) Model ### [matching](https://savrn.com/models/matching) [Allen E. Li](https://savrn.com/model-publishers/alle-nlfg) This repository is a compact, custom PyTorch implementation of Dino for Matching. The base 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 linear warmup schedule. These are… Open weights mit 24,832 parameters 512 tokens [View model](https://savrn.com/models/matching) ## 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.