# Models, Page 143 of 537 Source: https://savrn.com/models/page/143 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. 12,870Models 3,655Datasets 291Papers 7,691Publishers 12,980Sourced relationships Updated 2026-10-24 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,582](https://savrn.com/models?task=Text+generation) - [Image and text to text588](https://savrn.com/models?task=Image+and+text+to+text) - [Robotics403](https://savrn.com/models?task=Robotics) - [Feature extraction400](https://savrn.com/models?task=Feature+extraction) - [Text classification335](https://savrn.com/models?task=Text+classification) - [Text to image236](https://savrn.com/models?task=Text+to+image) - [Speech recognition190](https://savrn.com/models?task=Speech+recognition) - [Text to speech148](https://savrn.com/models?task=Text+to+speech) - [Image classification140](https://savrn.com/models?task=Image+classification) - [Audio classification130](https://savrn.com/models?task=Audio+classification) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Reinforcement learning102](https://savrn.com/models?task=Reinforcement+learning) - [Token classification101](https://savrn.com/models?task=Token+classification) - [Zero-shot classification96](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation90](https://savrn.com/models?task=Image+segmentation) - [Object detection87](https://savrn.com/models?task=Object+detection) - [Translation82](https://savrn.com/models?task=Translation) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [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 classification61](https://savrn.com/models?task=Video+classification) - [Image to text60](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers3,042](https://savrn.com/models?library=transformers) - [peft369](https://savrn.com/models?library=peft) - [diffusers210](https://savrn.com/models?library=diffusers) - [pytorch206](https://savrn.com/models?library=pytorch) - [gguf188](https://savrn.com/models?library=gguf) - [mlx172](https://savrn.com/models?library=mlx) - [lerobot154](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp47](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [onnx42](https://savrn.com/models?library=onnx) - [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [minimax-h329](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [coreml23](https://savrn.com/models?library=coreml) - [exllamav323](https://savrn.com/models?library=exllamav3) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino20](https://savrn.com/models?library=openvino) - [litert17](https://savrn.com/models?library=litert) - [litert-lm16](https://savrn.com/models?library=litert-lm) Modality - [Other7,428](https://savrn.com/models?modality=Other) - [Text2,883](https://savrn.com/models?modality=Text) - [Image and text648](https://savrn.com/models?modality=Image+and+text) - [Image604](https://savrn.com/models?modality=Image) - [Control505](https://savrn.com/models?modality=Control) - [Audio487](https://savrn.com/models?modality=Audio) - [Video116](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-2.03,395](https://savrn.com/models?license=apache-2.0) - [mit1,335](https://savrn.com/models?license=mit) - [other812](https://savrn.com/models?license=other) - [cc-by-nc-4.0215](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0172](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-4.056](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.240](https://savrn.com/models?license=llama3.2) - [unknown33](https://savrn.com/models?license=unknown) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.129](https://savrn.com/models?license=llama3.1) - [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0) - [gpl-3.020](https://savrn.com/models?license=gpl-3.0) - [openrail++18](https://savrn.com/models?license=openrail%2B%2B) - [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [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 weights12,434](https://savrn.com/models?access=open_access) - [Access requested at publisher436](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan116](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai105](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) - [Yurun Yuan66](https://savrn.com/models?publisher=yurunyyr) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan62](https://savrn.com/models?publisher=yuruny) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Christopher Subia-Waud60](https://savrn.com/models?publisher=cwaud) - [Yurun Yuan60](https://savrn.com/models?publisher=yyuan244) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Szekely53](https://savrn.com/models?publisher=attila115) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [NVIDIA50](https://savrn.com/models?publisher=nvidia) - [Wang49](https://savrn.com/models?publisher=davidwdw) [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) 12,870 models, sorted by most downloaded. Model ### [B4-150M](https://savrn.com/models/b4-150m) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/b4-150m) Model ### [BAAR250M](https://savrn.com/models/baar250m) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar250m) Model ### [BAAR3-IMG-299M-49M2](https://savrn.com/models/baar3-img-299m-49m2) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar3-img-299m-49m2) Model ### [BAAR3-IMG-299M-49M31](https://savrn.com/models/baar3-img-299m-49m31) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar3-img-299m-49m31) Model ### [BAAR3-IMG-299M-49M32](https://savrn.com/models/baar3-img-299m-49m32) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar3-img-299m-49m32) Model ### [BAAR3-embed-49M](https://savrn.com/models/baar3-embed-49m) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar3-embed-49m) Model ### [BAAR4-150M2](https://savrn.com/models/baar4-150m2) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar4-150m2) Model ### [BAAR4-49M](https://savrn.com/models/baar4-49m) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar4-49m) Model ### [BAAR4-IMG-335M-Realistic3](https://savrn.com/models/baar4-img-335m-realistic3) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar4-img-335m-realistic3) Model ### [BAAR4-VID-700M](https://savrn.com/models/baar4-vid-700m) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar4-vid-700m) Model · Tabular classification ### [BAAR4-guard](https://savrn.com/models/baar4-guard) [BAAR](https://savrn.com/model-publishers/aixk) 이 저장소는 2017~2026 전주기 트래픽(CIC-IDS2017, CICDDoS2019, CICIoT2023, 2024~2026 AI Vectors)으로 훈련된 고속 인라인 AI 보안 방화벽 엔진입니다. 1. model.safetensors: PyTorch SafeTensors 가중치 2. model.onnx: WebAssembly 및 에지 런타임용 고속 ONNX 그래프 3. modelguardultrafp16.baar3: Zero-Copy Direct-mmap FP16 초고속 커널 바이너리 4. config.json: Robust 스케일러 및 제로 트러스트 임계치 메타데이터 Open weights apache-2.0 644,381 parameters [View model](https://savrn.com/models/baar4-guard) Model ### [BAAR6-19M-3](https://savrn.com/models/baar6-19m-3) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar6-19m-3) Model ### [BAAR6-49M-1](https://savrn.com/models/baar6-49m-1) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar6-49m-1) Model · Text generation ### [BAAR8-44M-Vision](https://savrn.com/models/baar8-44m-vision) [BAAR](https://savrn.com/model-publishers/aixk) BAAR8-45M-Vision is an ultra-compact multilingual conversational model designed to enable real-time chat under minimal hardware conditions. With just 9 million (9M) parameters, it brings lightweight, interactive dialogue capabilities across 11 languages to extreme resource-constrained environments, embedded devices, and on-device platforms where standard LLMs cannot operate. BAAR8-45M-Vision은 최소한의 하드웨어 여건과 극단적인 리소스 제약 환경에서도 11개 언어로 실시간 대화(Chat)가 가능하도록 설계된 900만(9M) 파라미터 규모의 초경량 다국어 언어 모델입니다. 일반적인 대형 모델 구동이 불가능한 초저사양 엣지 및 임베디드 기기에서도 즉각적인 대화형 추론과 초경량 실시간 RAG 시스템 구축을 지원합니다. - Non-Commercial Use Only (비상업적 연구·개인 이용 한정): This model is strictly provided for non-commercial, research, and… Open weights other 44M parameters 512 tokens [View model](https://savrn.com/models/baar8-44m-vision) Model ### [BAAR8-45M-A](https://savrn.com/models/baar8-45m-a) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar8-45m-a) Model ### [BAAR8-45M-B](https://savrn.com/models/baar8-45m-b) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar8-45m-b) Model ### [BAAR8-98M-B](https://savrn.com/models/baar8-98m-b) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar8-98m-b) Model ### [BAAR8-9M-A](https://savrn.com/models/baar8-9m-a) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar8-9m-a) Model ### [BAAR8-9M-B](https://savrn.com/models/baar8-9m-b) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar8-9m-b) Model ### [BAAR9-2M](https://savrn.com/models/baar9-2m) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar9-2m) Model ### [BAAR9-45M](https://savrn.com/models/baar9-45m) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar9-45m) Model ### [BAAR9-45M-Embedding](https://savrn.com/models/baar9-45m-embedding) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar9-45m-embedding) Model ### [BAAR9-45M-Translate](https://savrn.com/models/baar9-45m-translate) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar9-45m-translate) Model ### [BAAR9-9M](https://savrn.com/models/baar9-9m) [BAAR](https://savrn.com/model-publishers/aixk) Open weights [View model](https://savrn.com/models/baar9-9m) ## 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.