# Models, Page 273 of 501 Source: https://savrn.com/models/page/273 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 · Sentence similarity ### [bge-small-vscode-dup](https://savrn.com/models/bge-small-vscode-dup) [Musab Suhail](https://savrn.com/model-publishers/musab6969) This is a sentence-transformers model finetuned from BAAI/bge-small-en-v1.5. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more. First install the Sentence Transformers library: Then you can load this model and run inference. Evaluated with EmbeddingSimilarityEvaluator Approximate statistics based on the first 100 samples: "scale": 20.0, "similarityfct": "cossim", "gatheracrossdevices": false, "directions": [ "querytodoc" "partitionmode": "joint", "hardnessmode": null, "hardnessstrength": 0.0 bibtex title = "Sentence-BERT: Sentence Embeddings using… Open weights 33M parameters 512 tokens sentence-transformers [View model](https://savrn.com/models/bge-small-vscode-dup) Model ### [bge_m3_onnx](https://savrn.com/models/bge-m3-onnx) [Run.](https://savrn.com/model-publishers/harinsk8) Open weights 8,194 tokens [View model](https://savrn.com/models/bge-m3-onnx) Model · Text to image ### [bhea2](https://savrn.com/models/bhea2) [Bienjamin Gabutan](https://savrn.com/model-publishers/cloudstrife5551) Open weights apache-2.0 diffusers [View model](https://savrn.com/models/bhea2) Model ### [bhkgbuuuiokjivgiy](https://savrn.com/models/bhkgbuuuiokjivgiy) [Browniohnoiuhiuoihoi](https://savrn.com/model-publishers/browniohoihoih) Open weights mit [View model](https://savrn.com/models/bhkgbuuuiokjivgiy) Model ### [bifocalgemma-sft](https://savrn.com/models/bifocalgemma-sft) [Daru Okta Buana](https://savrn.com/model-publishers/daruokta) Open weights [View model](https://savrn.com/models/bifocalgemma-sft) Model · Audio to audio ### [bigvgan_v2_22khz_80band_256x](https://savrn.com/models/bigvgan-v2-22khz-80band-256x-2) [losCrossos](https://savrn.com/model-publishers/loscrossos) [[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN) - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling… Open weights mit PyTorch [View model](https://savrn.com/models/bigvgan-v2-22khz-80band-256x-2) Model · Audio to audio ### [bigvgan_v2_44khz_128band_512x](https://savrn.com/models/bigvgan-v2-44khz-128band-512x-2) [losCrossos](https://savrn.com/model-publishers/loscrossos) [[Paper]](https://arxiv.org/abs/2206.04658) - [[Code]](https://github.com/NVIDIA/BigVGAN) - [[Showcase]](https://bigvgan-demo.github.io/) - [[Project Page]](https://research.nvidia.com/labs/adlr/projects/bigvgan/) - [[Weights]](https://huggingface.co/collections/nvidia/bigvgan-66959df3d97fd7d98d97dc9a) - [[Demo]](https://huggingface.co/spaces/nvidia/BigVGAN) - General refactor and code improvements for improved readability. - Fully fused CUDA kernel of anti-alised activation (upsampling + activation + downsampling) with inference speed benchmark. - We provide pretrained checkpoints of BigVGAN-v2 using diverse audio configurations, supporting up to 44 kHz sampling rate and 512x upsampling… Open weights mit PyTorch [View model](https://savrn.com/models/bigvgan-v2-44khz-128band-512x-2) Model · Text classification ### [bilstm-sentimen-kebijakan-publik](https://savrn.com/models/bilstm-sentimen-kebijakan-publik) [Dante](https://savrn.com/model-publishers/whotao1766) A Bidirectional LSTM that classifies Indonesian public comments about a government policy (the Kopdes program) into three sentiment classes: Negatif, Netral, and Positif. The model is trained from scratch (no pretrained embeddings) on a small dataset, so treat it as a course/research-level baseline rather than a production classifier. - About 850 unique comments remained after preprocessing and deduplication. - Labels are not human-annotated. They were generated automatically with the InSet lexicon: score = (number of positive words) − (number of negative words) in the stemmed text. Score > 0 is Positif, < 0 is Negatif, and 0 is Netral. Because of this, the reported scores measure how well… Open weights keras [View model](https://savrn.com/models/bilstm-sentimen-kebijakan-publik) Model · Text classification ### [binary-product-review-sentiment-with-distilbert](https://savrn.com/models/binary-product-review-sentiment-with-distilbert) [Profautorino](https://savrn.com/model-publishers/profautorino) Open weights apache-2.0 67M parameters [View model](https://savrn.com/models/binary-product-review-sentiment-with-distilbert) Model ### [biodata-registry](https://savrn.com/models/biodata-registry) [Anne Voigt](https://savrn.com/model-publishers/anne-voigt) Open weights [View model](https://savrn.com/models/biodata-registry) Model ### [biomistral-cardiac-acsm](https://savrn.com/models/biomistral-cardiac-acsm) [BILAL](https://savrn.com/model-publishers/bilaljaouad) Model Card: BioMistral-Cardiac-ACSMOverviewDeveloper: Bilal JaouadBase Model: BioMistral/BioMistral-7B-BnB.4Domain: Cardiovascular rehabilitation and ACSM clinical exercise prescription.Methodology: Domain adaptation (continued pre-training) via QLoRA (4-bit NF4 quantization, LoRA rank $r=16$) on an NVIDIA T4 GPU.Training DatasetThe model was adapted on a rigorously cleaned and curated corpus comprising five core reference documents:Patient Workbook: A 120-page cardiac rehabilitation patient education manual (with blank progress logs filtered out).Clinical Guidelines: The 146-page CPG STEMI 2019 guidelines, utilizing Camelot for structured table extraction.Clinical Protocols: The Malaysian… Access requested at publisher apache-2.0 7.2B parameters [View model](https://savrn.com/models/biomistral-cardiac-acsm) Model ### [bkv2tadmm-test](https://savrn.com/models/bkv2tadmm-test) [Kmpartner](https://savrn.com/model-publishers/kmpartner) Open weights diffusers [View model](https://savrn.com/models/bkv2tadmm-test) Model ### [blender-ai-bridge](https://savrn.com/models/blender-ai-bridge) [Help Livadies: Stream Music, Fund AI GPUs](https://savrn.com/model-publishers/livadies) что нужно, чтобы за несколько минут начать управлять живым Blender кодом. Никаких предварительных знаний о Blender, Python или MCP не требуется — только этот документ. Кому адресовано: агенту (Claude, GPT, Gemini, локальной модели — любой), которого подключили к Blender и который не знает, с чего начать. Читай по порядку. Статус: проверено на живом Blender 5.2.1 LTS, аддон blend-ai v1.3.0, Windows 11. 1. Blender: N в 3D-вьюпорте → вкладка blend-ai → Start Server (порт 9876). 2. Скачать: git clone https://github.com/HoldMyBeer-gg/blend-ai + uv pip install -e. 3. Проверить связь: отправить {"command": "getsceneinfo"} на 127.0.0.1:9876. Ключевое, что нужно понять: Blender — это сервер. Он… Open weights [View model](https://savrn.com/models/blender-ai-bridge) Model ### [blip-checkpoint-2024](https://savrn.com/models/blip-checkpoint-2024) [Zachary Gomez](https://savrn.com/model-publishers/zacharygl) This repository is a compact, custom PyTorch implementation of Blip for Generation. The nano 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 adamw with a exponential schedule. These are… Open weights bsd-3-clause 24,832 parameters 128 tokens [View model](https://savrn.com/models/blip-checkpoint-2024) Model ### [blip-classification](https://savrn.com/models/blip-classification) [Arif Anggraini](https://savrn.com/model-publishers/jktanggraini) This repository is a compact, custom PyTorch implementation of Blip for Classification. 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 lion with a polynomial schedule. These are… Open weights mit 24,832 parameters 512 tokens [View model](https://savrn.com/models/blip-classification) Model ### [blip-matching](https://savrn.com/models/blip-matching) [Jennifer Thomas](https://savrn.com/model-publishers/pdxt-homas) Working implementation of Blip for Matching using a large configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - 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 adafactor with a onecycle schedule. These are starting values in the script, not evidence of a… Open weights mit 16,576 parameters 512 tokens [View model](https://savrn.com/models/blip-matching) Model ### [blip-matching](https://savrn.com/models/blip-matching-2) [Vikas Banerjee](https://savrn.com/model-publishers/vikasbanerjee) Working implementation of Blip for Matching using a huge configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - 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 adamw with a onecycle schedule. These are starting values in the script, not evidence of a… Open weights apache-2.0 49,600 parameters 512 tokens [View model](https://savrn.com/models/blip-matching-2) Model ### [blip-retrieval-v2](https://savrn.com/models/blip-retrieval-v2) [Nicholas Brown](https://savrn.com/model-publishers/nicholasbrownped) This is an experimental Blip codebase for Retrieval. It keeps the xlarge 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 lion with a constant warmup schedule. These are starting values in the script, not evidence of a completed run.… Open weights apache-2.0 24,832 parameters 256 tokens [View model](https://savrn.com/models/blip-retrieval-v2) Model ### [blt-1b-sft-tr-en](https://savrn.com/models/blt-1b-sft-tr-en) [Network for Intelligent Research Vietnam](https://savrn.com/model-publishers/nirvlab) Open weights 4.6B parameters 4,096 tokens [View model](https://savrn.com/models/blt-1b-sft-tr-en) Model ### [bmb01](https://savrn.com/models/bmb01) [Luckyman](https://savrn.com/model-publishers/alteinstein) Open weights [View model](https://savrn.com/models/bmb01) Model ### [bmb01b](https://savrn.com/models/bmb01b) [Luckyman](https://savrn.com/model-publishers/alteinstein) Open weights [View model](https://savrn.com/models/bmb01b) Model ### [bmb02](https://savrn.com/models/bmb02) [Luckyman](https://savrn.com/model-publishers/alteinstein) Open weights [View model](https://savrn.com/models/bmb02) Model ### [bmb02b](https://savrn.com/models/bmb02b) [Luckyman](https://savrn.com/model-publishers/alteinstein) Open weights [View model](https://savrn.com/models/bmb02b) Model ### [bmb03](https://savrn.com/models/bmb03) [Luckyman](https://savrn.com/model-publishers/alteinstein) Open weights [View model](https://savrn.com/models/bmb03) ## 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.