# Models, Page 316 of 489 Source: https://savrn.com/models/page/316 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. 11,734Models 3,323Datasets 288Papers 7,150Publishers 12,282Sourced relationships Updated 2026-10-20 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,441](https://savrn.com/models?task=Text+generation) - [Image and text to text530](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction351](https://savrn.com/models?task=Feature+extraction) - [Robotics345](https://savrn.com/models?task=Robotics) - [Text classification310](https://savrn.com/models?task=Text+classification) - [Text to image222](https://savrn.com/models?task=Text+to+image) - [Speech recognition175](https://savrn.com/models?task=Speech+recognition) - [Audio classification158](https://savrn.com/models?task=Audio+classification) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Text to speech137](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification97](https://savrn.com/models?task=Token+classification) - [Reinforcement learning95](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation89](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) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,829](https://savrn.com/models?library=transformers) - [peft355](https://savrn.com/models?library=peft) - [diffusers196](https://savrn.com/models?library=diffusers) - [pytorch184](https://savrn.com/models?library=pytorch) - [gguf172](https://savrn.com/models?library=gguf) - [mlx145](https://savrn.com/models?library=mlx) - [lerobot141](https://savrn.com/models?library=lerobot) - [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) - [diffusion-single-file28](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) - [openvino20](https://savrn.com/models?library=openvino) - [exllamav318](https://savrn.com/models?library=exllamav3) - [coreml17](https://savrn.com/models?library=coreml) - [litert16](https://savrn.com/models?library=litert) - [openpi16](https://savrn.com/models?library=openpi) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,664](https://savrn.com/models?modality=Other) - [Text2,658](https://savrn.com/models?modality=Text) - [Image and text589](https://savrn.com/models?modality=Image+and+text) - [Image582](https://savrn.com/models?modality=Image) - [Audio488](https://savrn.com/models?modality=Audio) - [Control440](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) - [Tabular56](https://savrn.com/models?modality=Tabular) License - [apache-2.03,175](https://savrn.com/models?license=apache-2.0) - [mit1,275](https://savrn.com/models?license=mit) - [other723](https://savrn.com/models?license=other) - [cc-by-nc-4.0200](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0153](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause107](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.050](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 weights11,363](https://savrn.com/models?access=open_access) - [Access requested at publisher371](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher304](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan101](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai100](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [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 Pu71](https://savrn.com/models?publisher=putty77) - [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) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Yurun Yuan55](https://savrn.com/models?publisher=yuruny) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [BAAR46](https://savrn.com/models?publisher=aixk) - [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=yyuan244) [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) 11,734 models, sorted by most downloaded. Model ### [distilbert-base-uncased-finetuned-cola](https://savrn.com/models/distilbert-base-uncased-finetuned-cola) [User Name](https://savrn.com/model-publishers/username-6370) Open weights [View model](https://savrn.com/models/distilbert-base-uncased-finetuned-cola) Model · Text classification ### [distilbert-base-uncased-finetuned-emotion](https://savrn.com/models/distilbert-base-uncased-finetuned-emotion) [Vinay](https://savrn.com/model-publishers/vinaypoli) This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 64 - evalbatchsize: 64 - lrschedulertype: linear - numepochs: 2 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-base-uncased-finetuned-emotion) Model · Text classification ### [distilbert-food-not-food](https://savrn.com/models/distilbert-food-not-food) [Saber Bounehas](https://savrn.com/model-publishers/saberbounehas) This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 5.0.1 - Tokenizers 0.23.2 Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-food-not-food) Model · Text classification ### [distilbert-intel-arc-sentiment](https://savrn.com/models/distilbert-intel-arc-sentiment) [Karun2026](https://savrn.com/model-publishers/karun2026) Open weights 67M parameters transformers [View model](https://savrn.com/models/distilbert-intel-arc-sentiment) Model · Text classification ### [distilbert-pt-cased-redacao-nota](https://savrn.com/models/distilbert-pt-cased-redacao-nota) [Marcelo Lampe Zacharias](https://savrn.com/model-publishers/zaka0001) This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Open weights 63M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-pt-cased-redacao-nota) Model · Text classification ### [distilbert-rotten-tomatoes](https://savrn.com/models/distilbert-rotten-tomatoes) [Zhoutian](https://savrn.com/model-publishers/zhoutian) Open weights apache-2.0 67M parameters transformers [View model](https://savrn.com/models/distilbert-rotten-tomatoes) Model ### [distilbert-titulares-elcomercio](https://savrn.com/models/distilbert-titulares-elcomercio) [Nicolas Silva Andujar](https://savrn.com/model-publishers/nicosil02) Open weights 135M parameters 512 tokens [View model](https://savrn.com/models/distilbert-titulares-elcomercio) Model ### [distilling-safety-adapters](https://savrn.com/models/distilling-safety-adapters) [Christopher Kinoshita](https://savrn.com/model-publishers/realmbird) LoRA adapters from a research sprint asking whether safety transfers subliminally through distillation. Report and code: https://github.com/Realmbird/distilling-safety (see reports/REPORT.md). Research artifacts, not for deployment. Several students are measurably less safe than the base model (that is the main finding); none of these adapters should be used as a safety intervention. All adapters sit on Qwen/Qwen2.5-7B-Instruct ("M0"). Teacher recipe: LoRA r=64, alpha=64, all attention and MLP projections; lr 1e-4, cosine schedule, 1 epoch, batch 16, max length 1024; completion-only loss. Data: LLM-LAT/harmful-dataset (2.5k-prompt training split) and LLM-LAT/benign-dataset (2.5k rows)… Open weights apache-2.0 peft [View model](https://savrn.com/models/distilling-safety-adapters) Model · Fill mask ### [distilroberta-base-finetuned-wikitext2](https://savrn.com/models/distilroberta-base-finetuned-wikitext2) [Pufhv98w](https://savrn.com/model-publishers/3809ksdfh) This model is a fine-tuned version of distilroberta-base on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 3.0 - Transformers 5.18.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Open weights apache-2.0 82M parameters 514 tokens transformers [View model](https://savrn.com/models/distilroberta-base-finetuned-wikitext2) Model ### [dl-classification](https://savrn.com/models/dl-classification) [Christian Krause](https://savrn.com/model-publishers/christiankrause) A small Mobilevit implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The nano 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 sgd with a linear warmup schedule. These are starting values in the script… Open weights apache-2.0 49,600 parameters 128 tokens [View model](https://savrn.com/models/dl-classification) Model ### [dl-engine](https://savrn.com/models/dl-engine) [Aiking Studio](https://savrn.com/model-publishers/aikingstudio) Download engine API (yt-dlp + ffmpeg) for VidMax and GramMax. Open weights [View model](https://savrn.com/models/dl-engine) Model ### [dl-generation61](https://savrn.com/models/dl-generation61) [James Morales](https://savrn.com/model-publishers/jamesmoralesee) This repository is a compact, custom PyTorch implementation of Beit for Generation. The huge 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 novograd with a constant warmup schedule. These… Open weights mit 16,576 parameters 512 tokens [View model](https://savrn.com/models/dl-generation61) Model ### [dl-retrieval](https://savrn.com/models/dl-retrieval) [Stefan Becker](https://savrn.com/model-publishers/stefanbeckerana) Working implementation of CLIP for Retrieval using a small 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 exponential schedule. These are starting values in the script, not evidence of a… Open weights bsd-3-clause 49,600 parameters 512 tokens [View model](https://savrn.com/models/dl-retrieval) Model ### [dllm](https://savrn.com/models/dllm) [Tongyao](https://savrn.com/model-publishers/tyzhu) Open weights [View model](https://savrn.com/models/dllm) Model ### [dlm](https://savrn.com/models/dlm) [Hoang P](https://savrn.com/model-publishers/hoangp111) Open weights [View model](https://savrn.com/models/dlm) Model ### [doc-cascade-fastpath](https://savrn.com/models/doc-cascade-fastpath) [Azhar](https://savrn.com/model-publishers/amohammed3339) CPU-only page classifier for text extracted from PDFs (no OCR), designed for, "label": one of invoice|lab|radiology|dischargesummary}, split train/val/test. Point steps 2-4 at it with --data-dir; re-calibrate thresholds on your val split before deploying. Note traintransformer.py targets transformers==4.57. — 5.x removed several TrainingArguments kwargs this script uses. - Live-metrics trackio dashboard could not be hosted on this account (Gradio Spaces require PRO), so run metrics live in the job logs. Open weights [View model](https://savrn.com/models/doc-cascade-fastpath) Model ### [document-ai-pretrained](https://savrn.com/models/document-ai-pretrained) [Subramaniankabir](https://savrn.com/model-publishers/subramaniankabir) This is an exploratory note for Document AI. 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 FUNSD, SROIE, and CORD - 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, they should include dataset versions, commands, seeds, hardware, and raw logs. The note is intentionally… Open weights cc-by-4.0 49,600 parameters 256 tokens [View model](https://savrn.com/models/document-ai-pretrained) Model ### [document-ai-survey](https://savrn.com/models/document-ai-survey) [Chunyu](https://savrn.com/model-publishers/tamkang-physics) This is an exploratory note for Document AI. 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 FUNSD, SROIE, and CORD - 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, they should include dataset versions, commands, seeds, hardware, and raw logs. The note is intentionally… Open weights cc-by-4.0 16,576 parameters 256 tokens [View model](https://savrn.com/models/document-ai-survey) Model · Image classification ### [document-moire-detector-ONNX](https://savrn.com/models/document-moire-detector-onnx) [ONNX Community](https://savrn.com/model-publishers/onnx-community) This is an ONNX version of Jwalit/document-moire-detector. It was automatically converted and uploaded using this Hugging Face Space. See the pipeline documentation for image-classification: https://huggingface.co/docs/transformers.js/api/pipelines#modulepipelines.ImageClassificationPipeline A fine-tuned DeiT-small Vision Transformer for detecting moiré patterns in document images. (common from screen photography, scanning, or screen captures). - clean (0): No moiré patterns - moire (1): Moiré patterns detected 1. Resize aliasing — downscale+upscale with NEAREST interpolation + pattern overlay 2. Pattern overlay — sinusoidal interference with per-channel color variation 3. Multi-frequency… Open weights apache-2.0 transformers.js [View model](https://savrn.com/models/document-moire-detector-onnx) Model ### [document-pad-print](https://savrn.com/models/document-pad-print) [Yousef Alogiely](https://savrn.com/model-publishers/us10f) Open weights [View model](https://savrn.com/models/document-pad-print) Model ### [document-pad-stage2](https://savrn.com/models/document-pad-stage2) [Yousef Alogiely](https://savrn.com/model-publishers/us10f) Open weights [View model](https://savrn.com/models/document-pad-stage2) Model ### [document-signature-detector-v2](https://savrn.com/models/document-signature-detector-v2) [Duc Le](https://savrn.com/model-publishers/ducln-business) Open weights [View model](https://savrn.com/models/document-signature-detector-v2) Model · Image segmentation ### [docverify](https://savrn.com/models/docverify) [Ghost Sas](https://savrn.com/model-publishers/ghostsas001) A forensic segmentation network that finds where a payslip was edited — copy-move, splice from another document, or a re-typed amount — and the small models and thresholds that turn its pixel map into a verdict an analyst can act on. Part of docverify, a complete system with arithmetic checks, PDF forensics and a local LLM note: TamperNet (~33M parameters): a pretrained ConvNeXt encoder on RGB, plus two forensic inputs — fixed SRM noise residual filters (edits break the sensor/print noise) and a JPEG/DCT branch (a pasted region carries its own 8×8 compression grid) — fused into a U-Net-style decoder that outputs one logit per pixel. The page score is the mean of the top-200 pixel logits.… Open weights apache-2.0 onnx [View model](https://savrn.com/models/docverify) Model · Object detection ### [docverify-yolo-fields](https://savrn.com/models/docverify-yolo-fields) [Ghost Sas](https://savrn.com/model-publishers/ghostsas001) Finds the 10 fields of a French payslip so that an edit found by the tamper model can be pinned to net pay or gross salary instead of "pixels at (812, 640)". In docverify, an edit inside a money field is what turns a page into high risk. Part of docverify: project · netpaye (net pay) · brut (gross salary) · salairebase (base salary) · netavantimpot (net before tax) · cotisations (total contributions) · test pages, scans and phone photos mixed. 19 ms per page on a T4; part of the 6.7 s full pipeline on 4 CPU threads. Synthetic French payslips only (generated, fake people and companies); other layouts need their own field list. AGPL-3.0, inherited from Ultralytics YOLO. The rest of docverify… Open weights agpl-3.0 ultralytics [View model](https://savrn.com/models/docverify-yolo-fields) ## 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.