# Models, Page 283 of 501 Source: https://savrn.com/models/page/283 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. 12,010Models 3,413Datasets 288Papers 7,298Publishers 12,421Sourced relationships Updated 2026-10-21 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,467](https://savrn.com/models?task=Text+generation) - [Image and text to text542](https://savrn.com/models?task=Image+and+text+to+text) - [Robotics370](https://savrn.com/models?task=Robotics) - [Feature extraction352](https://savrn.com/models?task=Feature+extraction) - [Text classification326](https://savrn.com/models?task=Text+classification) - [Text to image224](https://savrn.com/models?task=Text+to+image) - [Speech recognition176](https://savrn.com/models?task=Speech+recognition) - [Audio classification160](https://savrn.com/models?task=Audio+classification) - [Text to speech141](https://savrn.com/models?task=Text+to+speech) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification97](https://savrn.com/models?task=Token+classification) - [Reinforcement learning96](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation90](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) - [Translation79](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 classification60](https://savrn.com/models?task=Video+classification) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,877](https://savrn.com/models?library=transformers) - [peft357](https://savrn.com/models?library=peft) - [diffusers199](https://savrn.com/models?library=diffusers) - [pytorch189](https://savrn.com/models?library=pytorch) - [gguf175](https://savrn.com/models?library=gguf) - [lerobot148](https://savrn.com/models?library=lerobot) - [mlx148](https://savrn.com/models?library=mlx) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [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) - [onnx39](https://savrn.com/models?library=onnx) - [vllm38](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [diffusion-single-file28](https://savrn.com/models?library=diffusion-single-file) - [minimax-h326](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino20](https://savrn.com/models?library=openvino) - [exllamav319](https://savrn.com/models?library=exllamav3) - [coreml18](https://savrn.com/models?library=coreml) - [litert17](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,847](https://savrn.com/models?modality=Other) - [Text2,701](https://savrn.com/models?modality=Text) - [Image and text601](https://savrn.com/models?modality=Image+and+text) - [Image586](https://savrn.com/models?modality=Image) - [Audio495](https://savrn.com/models?modality=Audio) - [Control466](https://savrn.com/models?modality=Control) - [Video115](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-2.03,220](https://savrn.com/models?license=apache-2.0) - [mit1,282](https://savrn.com/models?license=mit) - [other743](https://savrn.com/models?license=other) - [cc-by-nc-4.0201](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0155](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.051](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) - [unknown29](https://savrn.com/models?license=unknown) - [llama3.128](https://savrn.com/models?license=llama3.1) - [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-m13](https://savrn.com/models?license=creativeml-openrail-m) - [llama28](https://savrn.com/models?license=llama2) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights11,627](https://savrn.com/models?access=open_access) - [Access requested at publisher383](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher313](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan106](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai101](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311) - [Jessen Pu77](https://savrn.com/models?publisher=putty77) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [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) - [Yurun Yuan59](https://savrn.com/models?publisher=yuruny) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Szekely52](https://savrn.com/models?publisher=attila115) - [Yurun Yuan51](https://savrn.com/models?publisher=yyuan244) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Christopher Subia-Waud48](https://savrn.com/models?publisher=cwaud) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [NVIDIA48](https://savrn.com/models?publisher=nvidia) - [BAAR46](https://savrn.com/models?publisher=aixk) [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) 12,010 models, sorted by most downloaded. Model ### [circuitvqa-checkpoints-v2](https://savrn.com/models/circuitvqa-checkpoints-v2) [Miranda Surya Prakash](https://savrn.com/model-publishers/mirandaabhilash) Open weights [View model](https://savrn.com/models/circuitvqa-checkpoints-v2) Model ### [circuitvqa-checkpoints-v4](https://savrn.com/models/circuitvqa-checkpoints-v4) [Miranda Surya Prakash](https://savrn.com/model-publishers/mirandaabhilash) Open weights [View model](https://savrn.com/models/circuitvqa-checkpoints-v4) Model · Text generation ### [cisimcik-4b-GGUF](https://savrn.com/models/cisimcik-4b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/cisimcik/cisimcik-4b For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And… Open weights apache-2.0 transformers [View model](https://savrn.com/models/cisimcik-4b-gguf) Model ### [citrus-disease-vlm](https://savrn.com/models/citrus-disease-vlm) [Yuvraj Sharma](https://savrn.com/model-publishers/ysharma) Open weights 2.2B parameters 262,144 tokens [View model](https://savrn.com/models/citrus-disease-vlm) Model ### [civi](https://savrn.com/models/civi) [Thepepe](https://savrn.com/model-publishers/thepepecon) Open weights [View model](https://savrn.com/models/civi) Model ### [civilian-sim-3b-v2-onnx](https://savrn.com/models/civilian-sim-3b-v2-onnx) [Adam Poum](https://savrn.com/model-publishers/ai-damp) Open weights 131,072 tokens [View model](https://savrn.com/models/civilian-sim-3b-v2-onnx) Model ### [civision](https://savrn.com/models/civision) [Wilson](https://savrn.com/model-publishers/wravelio9) Deteksi objek YOLO yang diekspos sebagai HTTP service, dipanggil oleh Backend Express.js. - AI Service (app.py): FastAPI, load model YOLO sekali saat startup, endpoint POST /predict yang menerima banyak gambar sekaligus (batch). - BE (examples/express-client.js): contoh Express yang menerima upload dari client dan meneruskannya ke AI service. Folder.venv tidak dibagikan lewat git (isinya binary khusus mesin & terlalu besar). Setelah clone repo, buat ulang virtual environment sendiri dari requirements.lock.txt (berisi versi PERSIS yang sudah teruji). - Pakai Python 3.11 (versi lama seperti 3.7 tidak didukung model YOLO baru). - requirements.lock.txt = versi terkunci (paling aman, sama… Open weights [View model](https://savrn.com/models/civision) Model ### [civit](https://savrn.com/models/civit) [Octavian123](https://savrn.com/model-publishers/octavian123) Open weights [View model](https://savrn.com/models/civit) Model ### [civitai-collections](https://savrn.com/models/civitai-collections) [Yami Uchi](https://savrn.com/model-publishers/yamiuchi) Open weights other [View model](https://savrn.com/models/civitai-collections) Model ### [civitai-lora-archive](https://savrn.com/models/civitai-lora-archive) [No Nope](https://savrn.com/model-publishers/isam) Open weights [View model](https://savrn.com/models/civitai-lora-archive) Model ### [ck0008](https://savrn.com/models/ck0008) [AI4Manufacturing](https://savrn.com/model-publishers/ai4manufacturing) Access requested at publisher [View model](https://savrn.com/models/ck0008) Model ### [ckpt](https://savrn.com/models/ckpt) [Jian Kim](https://savrn.com/model-publishers/jiankimr) Open weights [View model](https://savrn.com/models/ckpt) Model ### [ckpt-bigbench](https://savrn.com/models/ckpt-bigbench) [ES Heterogeneity](https://savrn.com/model-publishers/es-hetero) Open weights [View model](https://savrn.com/models/ckpt-bigbench) Model ### [ckpt-countdown-families](https://savrn.com/models/ckpt-countdown-families) [ES Heterogeneity](https://savrn.com/model-publishers/es-hetero) Evolution-Strategies fine-tuning checkpoints from IBM Blue Vela. iter.pth are the periodic training checkpoints and final/pytorchmodel.pth is the end-of-run save. The two are NOT copies of each other even at the same N -- the final save is written a few ES steps after the last periodic one, measured at a relative L2 difference of 0.002 to 0.004 where ten iterations move 0.008. Treat final/ as the canonical end-of-run weights. One copy per iteration is published. Where a run has both a final save and a periodic checkpoint at that same iteration, only the final save is here. Periodic checkpoints at earlier iterations are kept, and a run that never reached its end keeps every periodic… Open weights [View model](https://savrn.com/models/ckpt-countdown-families) Model ### [ckpts](https://savrn.com/models/ckpts) [Unsup Opsd2](https://savrn.com/model-publishers/unsup-opsd2) Open weights [View model](https://savrn.com/models/ckpts) Model ### [clara-releases](https://savrn.com/models/clara-releases) [Martin Vaillant](https://savrn.com/model-publishers/martinvaillant1) Entrepot des mises a jour de ClaraDesk Pro (assistante locale). - updates.json: manifest signe Ed25519 lu par le launcher de Clara. - ClaraSetup-x.y.z.exe: installeurs Windows (Inno Setup). Les manifests sont signes: un fichier modifie sans la cle privee de signature est ignore par tous les clients. Site: a venir. Open weights other [View model](https://savrn.com/models/clara-releases) Model ### [claridi-checkpoints](https://savrn.com/models/claridi-checkpoints) [Selim Emir Can](https://savrn.com/model-publishers/selimemircan) Trained weights accompanying a manuscript in preparation. Access is granted on request. Layout: /fold / — one directory per experiment and cross-validation fold. Diffusion experiments store the selected checkpoint plus its config.yaml; GAN baselines store the generator used at inference. Companion outputs and metadata are held separately and shared on request. Contact the repository owner for details. Access requested at publisher cc-by-nc-4.0 [View model](https://savrn.com/models/claridi-checkpoints) Model ### [clarity-semeval26](https://savrn.com/models/clarity-semeval26) [Siddarth G](https://savrn.com/model-publishers/siddarthg44) Open weights [View model](https://savrn.com/models/clarity-semeval26) Model ### [clasificador-noticias-peruanas](https://savrn.com/models/clasificador-noticias-peruanas) [MIRIAN DE LA CRUZ CONDORI](https://savrn.com/model-publishers/mirian13) Open weights 135M parameters [View model](https://savrn.com/models/clasificador-noticias-peruanas) Model · Text classification ### [clasificador-titulares-pe](https://savrn.com/models/clasificador-titulares-pe) [Amanda Gomez](https://savrn.com/model-publishers/av-gomez) Open weights mit 135M parameters [View model](https://savrn.com/models/clasificador-titulares-pe) Model ### [class-classification](https://savrn.com/models/class-classification) [Andregabrieleli](https://savrn.com/model-publishers/andregabrieleli) Open weights mit 16,576 parameters [View model](https://savrn.com/models/class-classification) Model ### [class-matching](https://savrn.com/models/class-matching) [Rreddy Aditya](https://savrn.com/model-publishers/rreddy-aditya) This is an experimental DeiT codebase for Matching. It keeps the giant 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 novograd with a step schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 33,088 parameters 256 tokens [View model](https://savrn.com/models/class-matching) Model ### [classification](https://savrn.com/models/classification) [David](https://savrn.com/model-publishers/davidhillket) This is an experimental Dino codebase for Classification. It keeps the huge 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 cosine schedule. These are starting values in the script, not evidence of a completed run. For a… Open weights apache-2.0 16,576 parameters 512 tokens [View model](https://savrn.com/models/classification) Model ### [classification](https://savrn.com/models/classification-2) [Gabriel Nascimento](https://savrn.com/model-publishers/gnascimentoeli) This is an experimental Tiny Transformer codebase for Classification. It keeps the tiny 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 lamb with a exponential schedule. These are starting values in the script, not evidence of a… Open weights bsd-3-clause 16,576 parameters 256 tokens [View model](https://savrn.com/models/classification-2) ## 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.