# Models, Page 431 of 464 Source: https://savrn.com/models/page/431 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 ### [sts2-rl-archive](https://savrn.com/models/sts2-rl-archive) [William Liu](https://savrn.com/model-publishers/liuyuan103) Open weights [View model](https://savrn.com/models/sts2-rl-archive) Model ### [studioneverai-01-10](https://savrn.com/models/studioneverai-01-10) [Menochoppa](https://savrn.com/model-publishers/menochoppa) Open weights [View model](https://savrn.com/models/studioneverai-01-10) Model ### [studioneverai-03-10](https://savrn.com/models/studioneverai-03-10) [Menochoppa](https://savrn.com/model-publishers/menochoppa) Open weights [View model](https://savrn.com/models/studioneverai-03-10) Model ### [study-classification](https://savrn.com/models/study-classification) [Tony Y.](https://savrn.com/model-publishers/tonygyoshida) A small Mocov3 implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The small variant is a reproducible starting point, not a trained model 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 adam with a constant warmup schedule. These are starting values in the script… Open weights apache-2.0 33,088 parameters 128 tokens [View model](https://savrn.com/models/study-classification) Model ### [study-lightweight-multimodal](https://savrn.com/models/study-lightweight-multimodal) [Macolombo](https://savrn.com/model-publishers/macolombo) This is an exploratory note for Lightweight Multimodal. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with notes.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… Open weights cc-by-4.0 24,832 parameters 512 tokens [View model](https://savrn.com/models/study-lightweight-multimodal) Model ### [study-lightweight-multimodal](https://savrn.com/models/study-lightweight-multimodal-2) [M. Garcia](https://savrn.com/model-publishers/melissa-garcia) A structured set of research notes on Lightweight Multimodal, 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 task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with review.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… Open weights cc-by-4.0 24,832 parameters 512 tokens [View model](https://savrn.com/models/study-lightweight-multimodal-2) Model ### [stvg-r1-model-7b](https://savrn.com/models/stvg-r1-model-7b) [Javion](https://savrn.com/model-publishers/javion2025) This repository contains the checkpoint for STVG-R1, the first reinforcement learning framework for spatial–temporal video grounding. We finetuned Qwen2.5-VL-7B-Instruct with constructed Visual Prompt Data. If you use STVG-R1 or this checkpoint in your research, please cite Open weights cc-by-nc-sa-4.0 8.3B parameters 128,000 tokens [View model](https://savrn.com/models/stvg-r1-model-7b) Model ### [style_lora](https://savrn.com/models/style-lora) [Zac Bates](https://savrn.com/model-publishers/crimsonmonkey) Open weights [View model](https://savrn.com/models/style-lora) Model ### [stylegan2-skin-lesion-ham10000](https://savrn.com/models/stylegan2-skin-lesion-ham10000) [DACNTT&KLTN](https://savrn.com/model-publishers/dacntt-kltn) Open weights [View model](https://savrn.com/models/stylegan2-skin-lesion-ham10000) Model ### [stylegan2-unconditional-skin-lesion-ham10000](https://savrn.com/models/stylegan2-unconditional-skin-lesion-ham10000) [DACNTT&KLTN](https://savrn.com/model-publishers/dacntt-kltn) Open weights [View model](https://savrn.com/models/stylegan2-unconditional-skin-lesion-ham10000) Model ### [suaGCRpE9Iu45azz](https://savrn.com/models/suagcrpe9iu45azz) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/suagcrpe9iu45azz) Model ### [super_model](https://savrn.com/models/super-model) [Vuong](https://savrn.com/model-publishers/thien2006) Open weights [View model](https://savrn.com/models/super-model) Model · Text classification ### [superfast-tiny-home-robotic](https://savrn.com/models/superfast-tiny-home-robotic) [Sr Aivante](https://savrn.com/model-publishers/sraivante) An offline English and Hinglish command interpreter for home devices and registered industrial/robotics devices. It converts one instruction into a catalog-validated JSON intent. The package includes a trained 87,181-byte action model, a 1,002,414-device SQLite catalog, a Python API, an HTTP service, a browser playground, Docker files, and reproducible training and evaluation data. The runtime was previously packaged locally as edge-command-docker-v3-ui. The system resolves device names through explicit identifiers, aliases and SQLite FTS5 search. It prioritizes action patterns and capability checks, with a stored count-based unigram/bigram classifier for the fallback scoring path. The… Open weights apache-2.0 [View model](https://savrn.com/models/superfast-tiny-home-robotic) Model · Any to any ### [support](https://savrn.com/models/support) [David Belton](https://savrn.com/model-publishers/davidau) https://huggingface.co/collections/DavidAU/turbo-brilliance-ai-constructs-50x-reasoning-instruct To support our team, sponsor, "Buy Me A Coffee", commission project(s), access premium item(s)/model(s)/module(s), model modifications, GET HELP, see a sorted list of models (380+) and more please see below. We build precision models at scale for the community. We are user-funded, and use local hardware to create, tune and deploy models worldwide. We also create systems for fine tuning, and specialized model systems too as well as "augment" current model/AI technology. - Model fine tuning across a diverse arch type of models and sizes, including MOEs/sparse moes. (over 1000) - Model… Open weights apache-2.0 transformers [View model](https://savrn.com/models/support) Model ### [surabaya-opinion-tweet-clusters](https://savrn.com/models/surabaya-opinion-tweet-clusters) [Ketut Shridhara](https://savrn.com/model-publishers/kiuyha) topic-modeling - kmeans - fasttext - surabaya This repository contains a set of models for performing topic modeling on tweets from Surabaya. This repository contains two key components: 1. fasttext.model: A gensim FastText model trained on processed tweet text. It is used to generate semantic vector embeddings for documents. 2. kmeans.joblib: A scikit-learn K-Means model trained on the vectors produced by the FastText model. It contains the final topic cluster centroids. Load the models using gensim, joblib, and huggingfacehub. import joblib from gensim.models import FastText from huggingfacehub import hfhubdownload REPOID = "Kiuyha/surabaya-opinion-tweet-clusters" kmeanspath =… Open weights mit fasttext [View model](https://savrn.com/models/surabaya-opinion-tweet-clusters) Model ### [surfPRM-qwen3.5-4b](https://savrn.com/models/surfprm-qwen3-5-4b) [PoloWitty](https://savrn.com/model-publishers/polowitty) A process reward model (PRM) for web agents, built by LoRA SFT on Qwen3.5-4B and merged into full weights. It evaluates candidate next actions of web agents at intermediate steps, given the task intent, the current page's accessibility tree (AXTree), and the previous action trajectory. Trained on the surfPRM-data dataset: 9,821 pairwise step-preference samples for web agents. Each sample contains the task intent, the current page's accessibility tree (AXTree), the previous action trajectory, the start/current URLs, and two candidate next actions grounded on the current page; the model outputs structured / / / reasoning and selects the better action. Requires transformers >= 5.6. Load as a… Open weights apache-2.0 4.5B parameters 262,144 tokens [View model](https://savrn.com/models/surfprm-qwen3-5-4b) Model ### [surfPRM-qwen3.5-9b](https://savrn.com/models/surfprm-qwen3-5-9b) [PoloWitty](https://savrn.com/model-publishers/polowitty) Open weights [View model](https://savrn.com/models/surfprm-qwen3-5-9b) Model ### [survey-neural-architecture-search](https://savrn.com/models/survey-neural-architecture-search) [John Brown](https://savrn.com/model-publishers/johnbrownre) This repository contains a working research note about Neural Architecture Search. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - 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… Open weights mit 49,600 parameters 256 tokens [View model](https://savrn.com/models/survey-neural-architecture-search) Model ### [svd-safety-l2_base_k0_a1p0_free_remove40](https://savrn.com/models/svd-safety-l2-base-k0-a1p0-free-remove40) [Park](https://savrn.com/model-publishers/jeesup) Open weights [View model](https://savrn.com/models/svd-safety-l2-base-k0-a1p0-free-remove40) Model ### [svd-safety-l2_basis_jbbsftx1_remove40](https://savrn.com/models/svd-safety-l2-basis-jbbsftx1-remove40) [Park](https://savrn.com/model-publishers/jeesup) Open weights [View model](https://savrn.com/models/svd-safety-l2-basis-jbbsftx1-remove40) Model · Text generation ### [svd-safety-l2_jbbpure_ka1_a1p0_free_remove40](https://savrn.com/models/svd-safety-l2-jbbpure-ka1-a1p0-free-remove40) [Park](https://savrn.com/model-publishers/jeesup) A Llama-2-7b-chat checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then given a 0.0% parameter budget of restored SVD components selected by the unknown rule. This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-2-7b-chat: compression alone raises attack-success rate, and the point of the study is to quantify that and test recovery. Treat… Open weights llama2 6.7B parameters 4,096 tokens transformers [View model](https://savrn.com/models/svd-safety-l2-jbbpure-ka1-a1p0-free-remove40) Model · Text generation ### [svd-safety-l2_remove40_swapgapiter_rankunit_b010](https://savrn.com/models/svd-safety-l2-remove40-swapgapiter-rankunit-b010) [Park](https://savrn.com/model-publishers/jeesup) A Llama-2-7b-chat checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then edited by 10 of 10 rounds of iterative parameter-neutral swap selected by the gapiter rule (up to 0.1% of dense parameters per round; the full run's budget is 1.0%). This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-2-7b-chat: compression alone raises attack-success… Open weights llama2 6.7B parameters 4,096 tokens transformers [View model](https://savrn.com/models/svd-safety-l2-remove40-swapgapiter-rankunit-b010) Model ### [svd-safety-l3_basis_jbbsftx1_remove40](https://savrn.com/models/svd-safety-l3-basis-jbbsftx1-remove40) [Park](https://savrn.com/model-publishers/jeesup) Open weights [View model](https://savrn.com/models/svd-safety-l3-basis-jbbsftx1-remove40) Model · Text generation ### [svd-safety-l3_remove40_swapgapiter_b010](https://savrn.com/models/svd-safety-l3-remove40-swapgapiter-b010) [Park](https://savrn.com/model-publishers/jeesup) A Llama-3-8B-Instruct checkpoint compressed with SVD-LLM to 60.0% of dense parameters, then edited by 10 of 10 rounds of iterative parameter-neutral swap selected by the gapiter rule (up to 0.1% of dense parameters per round; the full run's budget is 1.0%). This is a research artifact from a study of how SVD compression damages safety behaviour and which component-selection rule best repairs it. It is one cell of a grid over selection rules and budgets; it is not a general-purpose chat model. This checkpoint exists to measure safety/utility trade-offs under compression. Several arms in the grid are deliberately safety-degraded relative to Llama-3-8B-Instruct: compression alone raises… Open weights llama3 8B parameters 8,192 tokens transformers [View model](https://savrn.com/models/svd-safety-l3-remove40-swapgapiter-b010) ## 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.