# Models, Page 272 of 501 Source: https://savrn.com/models/page/272 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 · Text generation ### [berna-r7-v3](https://savrn.com/models/berna-r7-v3) [MOHAMMED KAMIL](https://savrn.com/model-publishers/muhamedkamil) libraryname: transformers pipelinetag: text-generation - language-model - berna - dna-kernel-plexus - cells - research - non-commercial 1.2B parameter language model with self-growing cell architecture. 2. Saturation S = 0.8578 (just above threshold 0.85) Comparison: GPT-2 Small (124M) achieves HellaSwag 31.4%, ARC-Easy 44.0%, PIQA 64.1%. Berna R7 v3 (1.2B) matches this at ~15x less training data. code/ Training code config/ config.json cellsruntimecurrent/ Live cells + cognitive log cellsruntimefinal/ Final cell state checkpoints/step00010200/ Final model (with wrapper) checkpoints/step00001700/ Earlier checkpoint data/tokenized/ eng.npy, math.npy, code.npy data/tokenizer/ tokenizer.json… Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/berna-r7-v3) Model · Text generation ### [berna-r8-v3](https://savrn.com/models/berna-r8-v3) [MOHAMMED KAMIL](https://savrn.com/model-publishers/muhamedkamil) Each cell that saturates (S >= 0.68, K6 >= 0.03): 3. Gradient hooks - zero gradients for frozen neurons Result: Bounded Forgetting - old knowledge protected while learning new. Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/berna-r8-v3) Model ### [bernice-fusion-6-dareties-p0.99-k0.5-l1-bestcross-hatebr-olidbr-toldbr-tupy-v61](https://savrn.com/models/bernice-fusion-6-dareties-p0-99-k0-5-l1-bestcross-hatebr-olidbr-toldbr-tupy-v61) [Annie Amorim](https://savrn.com/model-publishers/anorim) Open weights 278M parameters 130 tokens [View model](https://savrn.com/models/bernice-fusion-6-dareties-p0-99-k0-5-l1-bestcross-hatebr-olidbr-toldbr-tupy-v61) Model ### [bernini-checkpoints](https://savrn.com/models/bernini-checkpoints) [Lawrence](https://savrn.com/model-publishers/lawrshen) Open weights [View model](https://savrn.com/models/bernini-checkpoints) Model · Text classification ### [bert-base-agnews-topic-classification](https://savrn.com/models/bert-base-agnews-topic-classification) [Dalila Ku](https://savrn.com/model-publishers/dalila-ku) Full fine-tuning of bert-base-uncased for 4-class news topic classification, delivered as part of assignment U2T01 (Adapting BERT for NLP tasks — Trends in Data Science, Unit 2, Universidad Politécnica de Yucatán). bert-base-uncased with a linear classification head on top of the [CLS] token's last hidden state, mapping to 4 topic classes: World, Sports, Business, Sci/Tech. - 10% of the official train split was held out as validation; the official test split was left untouched. - 4 balanced classes. Two adaptation methods were trained and compared; full fine-tuning is the delivered model, since it beat the feature-based baseline by a margin well above the run-to-run noise floor (±1–3 points… Open weights apache-2.0 109M parameters 512 tokens [View model](https://savrn.com/models/bert-base-agnews-topic-classification) Model · Token classification ### [bert-base-cased-pos-tagging-ewt](https://savrn.com/models/bert-base-cased-pos-tagging-ewt) [Angel Rivaldo Canche Chuc](https://savrn.com/model-publishers/rivaldo2309030) Modelo bert-base-cased ajustado finamente (Fine-tuned) para la tarea de Part-of-Speech (POS) Tagging utilizando las 17 etiquetas universales (UPOS) del dataset universal-dependencies/universaldependencies (configuración enewt). Etiquetado Morfosintáctico Automático: Clasificación de categorías gramaticales (Part-of-Speech Tagging) en oraciones en idioma inglés. Análisis Lingüístico: Extracción de características sintácticas para sistemas downstream de NLP, desambiguación léxica y procesamiento del lenguaje. Parameter Groups (Método 1 - Partial): classifier: Learning Rate 1e-3 bert.encoder.layer.10 y layer.11: Learning Rate 2e-5 Parameter Groups (Método 2 - Full Ganador): classifier… Open weights apache-2.0 108M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-base-cased-pos-tagging-ewt) Model · Question answering ### [bert-base-cased-squad-extractive-qa](https://savrn.com/models/bert-base-cased-squad-extractive-qa) [Russel Ku](https://savrn.com/model-publishers/russelkuaguilar) This repository provides a fine-tuned bert-base-cased model adapted for the downstream task of Extractive Question Answering on the full SQuAD v1.1 dataset. Given a (Question, Context) pair, the model predicts the exact start and end character/token spans within the context passage that answer the question. The model was evaluated on the complete SQuAD v1.1 validation set (valdataset, 10,570 examples): Our final Epoch 3 checkpoint achieves 88.21% F1 and 80.75% EM, matching the original Google BERT benchmark (-0.29 F1 points) with complete reproducibility on modern GPU hardware and AMP FP16. - Sequence Length (maxlength): 384 tokens - Document Stride (docstride): 128 tokens To better… Open weights apache-2.0 108M parameters 512 tokens transformers [View model](https://savrn.com/models/bert-base-cased-squad-extractive-qa) Model · Token classification ### [bert-base-conll03-ner](https://savrn.com/models/bert-base-conll03-ner) [Dalila Ku](https://savrn.com/model-publishers/dalila-ku) token-classification - bert - ner - lhoestq/conll2003 - precision - recall pipelinetag: token-classification basemodel: bert-base-uncased Fine-tuning of bert-base-uncased for Named Entity Recognition (NER), delivered as part of assignment U2T01 (Adapting BERT for NLP tasks — Trends in Data Science, Unit 2, Universidad Politécnica de Yucatán). bert-base-uncased with a token-level linear classification head over the last hidden state of every token, predicting one of 9 BIO-scheme entity tags (person, organization, location, miscellaneous, or none). (lhoestq/conll2003), using its own official train/validation/test splits. split a word into multiple subwords, the label is placed on the first… Open weights apache-2.0 109M parameters 512 tokens [View model](https://savrn.com/models/bert-base-conll03-ner) Model · Question answering ### [bert-base-squad-qa](https://savrn.com/models/bert-base-squad-qa) [Dalila Ku](https://savrn.com/model-publishers/dalila-ku) Fine-tuning of bert-base-uncased for extractive Question Answering, delivered as part of assignment U2T01 (Adapting BERT for NLP tasks — Trends in Data Science, Unit 2, Universidad Politécnica de Yucatán). bert-base-uncased with a qaoutputs head that predicts, per token, the probability of being the start and the end of the answer span within the given context. (rajpurkar/squad), subsampled to 15,000 examples from the official train split (as required by the assignment), with a 90/10 split used for training/ validation. SQuAD's official validation set (10,570 questions, never seen during training) was reserved as the final test set. - Preprocessing uses a sliding window (stride) since… Open weights apache-2.0 109M parameters 512 tokens [View model](https://savrn.com/models/bert-base-squad-qa) Model · Token classification ### [bert-base-ud-ewt-pos](https://savrn.com/models/bert-base-ud-ewt-pos) [Dalila Ku](https://savrn.com/model-publishers/dalila-ku) Fine-tuning of bert-base-uncased for Part-of-Speech (POS) tagging, delivered as part of assignment U2T01 (Adapting BERT for NLP tasks — Trends in Data Science, Unit 2, Universidad Politécnica de Yucatán). bert-base-uncased with a token-level linear classification head over the last hidden state of every token, predicting one of 17 Universal POS (UPOS) tags (NOUN, VERB, ADJ, DET, PUNCT, etc.). (universal-dependencies/universaldependencies, config enewt). (universaldependencies/universaldependencies) has a typo; the correct organization name on the Hub uses a hyphen (universal-dependencies). - Same per-token label shape as NER, so the same subword-alignment convention is used: the label goes… Open weights apache-2.0 109M parameters 512 tokens [View model](https://savrn.com/models/bert-base-ud-ewt-pos) Model ### [bert-base-uncased-pnn-d50-int8-j21](https://savrn.com/models/bert-base-uncased-pnn-d50-int8-j21) [Primeneural](https://savrn.com/model-publishers/primeneural) Open weights other 79M parameters pnn [View model](https://savrn.com/models/bert-base-uncased-pnn-d50-int8-j21) Model ### [bert-disaster-tweets](https://savrn.com/models/bert-disaster-tweets) [111](https://savrn.com/model-publishers/liangzaiwudi12) Open weights 109M parameters 512 tokens [View model](https://savrn.com/models/bert-disaster-tweets) Model ### [bert-finetuned-ner](https://savrn.com/models/bert-finetuned-ner) [Aleksandr Aksenov](https://savrn.com/model-publishers/hades1126) Open weights [View model](https://savrn.com/models/bert-finetuned-ner) Model ### [bert-finetuned-squad](https://savrn.com/models/bert-finetuned-squad) [Yangjiaxuan](https://savrn.com/model-publishers/yjxyjx) Open weights [View model](https://savrn.com/models/bert-finetuned-squad) Model ### [bert-intent-classifier](https://savrn.com/models/bert-intent-classifier) [JOON](https://savrn.com/model-publishers/jh228932683) Open weights 102M parameters 512 tokens [View model](https://savrn.com/models/bert-intent-classifier) Model ### [bert-ms2agent](https://savrn.com/models/bert-ms2agent) [Monaaaaaa](https://savrn.com/model-publishers/monaaaaaa) Open weights 109M parameters 512 tokens [View model](https://savrn.com/models/bert-ms2agent) Model ### [bert_simpson](https://savrn.com/models/bert-simpson) [HI](https://savrn.com/model-publishers/ronaldo-goat) Open weights [View model](https://savrn.com/models/bert-simpson) Model ### [best-pt-louise](https://savrn.com/models/best-pt-louise) [Tdkdiketahui1945](https://savrn.com/model-publishers/tdkdiketahui1945) Open weights [View model](https://savrn.com/models/best-pt-louise) Model ### [bestbot_repick](https://savrn.com/models/bestbot-repick) [Jiajie Yu](https://savrn.com/model-publishers/jaye86) Open weights [View model](https://savrn.com/models/bestbot-repick) Model ### [bev1](https://savrn.com/models/bev1) [Evan Low](https://savrn.com/model-publishers/superbitdev) Open weights [View model](https://savrn.com/models/bev1) Model ### [bev1016-2](https://savrn.com/models/bev1016-2) [Matthewsliger](https://savrn.com/model-publishers/realfencer) Open weights [View model](https://savrn.com/models/bev1016-2) Model ### [bev_1_101](https://savrn.com/models/bev-1-101) [Alex Balin](https://savrn.com/model-publishers/whitecrow323) Open weights [View model](https://savrn.com/models/bev-1-101) Model ### [bev_1_101](https://savrn.com/models/bev-1-101-2) [Monkey.D.Luffy](https://savrn.com/model-publishers/mugiwaraboshi16) Open weights [View model](https://savrn.com/models/bev-1-101-2) Model · Token classification ### [bge-small-ner-early-stopping](https://savrn.com/models/bge-small-ner-early-stopping) [Arslan Yamaletdinov](https://savrn.com/model-publishers/arslanyam) This model is a fine-tuned version of BAAI/bge-small-en-v1.5 on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 16 - evalbatchsize: 16 - lrschedulertype: linear - numepochs: 10 - Transformers 4.50.0 - Pytorch 2.11.0+cu128 - Datasets 3.4.1 - Tokenizers 0.21.4 Open weights mit 33M parameters 512 tokens transformers [View model](https://savrn.com/models/bge-small-ner-early-stopping) ## 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.