# Models, Page 460 of 464 Source: https://savrn.com/models/page/460 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. 11,122Models 3,164Datasets 285Papers 6,792Publishers 11,852Sourced relationships Updated 2026-10-18 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,362](https://savrn.com/models?task=Text+generation) - [Image and text to text494](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction332](https://savrn.com/models?task=Feature+extraction) - [Robotics329](https://savrn.com/models?task=Robotics) - [Text classification293](https://savrn.com/models?task=Text+classification) - [Text to image202](https://savrn.com/models?task=Text+to+image) - [Speech recognition170](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification135](https://savrn.com/models?task=Image+classification) - [Text to speech130](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity102](https://savrn.com/models?task=Sentence+similarity) - [Token classification96](https://savrn.com/models?task=Token+classification) - [Zero-shot classification92](https://savrn.com/models?task=Zero-shot+classification) - [Reinforcement learning91](https://savrn.com/models?task=Reinforcement+learning) - [Image segmentation87](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) - [Translation77](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask67](https://savrn.com/models?task=Fill+mask) - [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Image to text57](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,718](https://savrn.com/models?library=transformers) - [peft329](https://savrn.com/models?library=peft) - [diffusers183](https://savrn.com/models?library=diffusers) - [pytorch169](https://savrn.com/models?library=pytorch) - [gguf165](https://savrn.com/models?library=gguf) - [sentence-transformers138](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [lerobot135](https://savrn.com/models?library=lerobot) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp40](https://savrn.com/models?library=llama.cpp) - [vllm38](https://savrn.com/models?library=vllm) - [onnx35](https://savrn.com/models?library=onnx) - [diffusion-single-file27](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) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [openpi16](https://savrn.com/models?library=openpi) - [exllamav315](https://savrn.com/models?library=exllamav3) - [stable-baselines315](https://savrn.com/models?library=stable-baselines3) - [keras14](https://savrn.com/models?library=keras) Modality - [Other6,276](https://savrn.com/models?modality=Other) - [Text2,536](https://savrn.com/models?modality=Text) - [Image554](https://savrn.com/models?modality=Image) - [Image and text551](https://savrn.com/models?modality=Image+and+text) - [Audio473](https://savrn.com/models?modality=Audio) - [Control420](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) - [Tabular55](https://savrn.com/models?modality=Tabular) License - [apache-2.03,031](https://savrn.com/models?license=apache-2.0) - [mit1,221](https://savrn.com/models?license=mit) - [other681](https://savrn.com/models?license=other) - [cc-by-nc-4.0184](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0147](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma59](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.234](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.127](https://savrn.com/models?license=llama3.1) - [unknown26](https://savrn.com/models?license=unknown) - [cc-by-sa-4.018](https://savrn.com/models?license=cc-by-sa-4.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [gpl-3.016](https://savrn.com/models?license=gpl-3.0) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail13](https://savrn.com/models?license=openrail) - [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m) - [cc-by-nc-nd-4.010](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama26](https://savrn.com/models?license=llama2) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights10,787](https://savrn.com/models?access=open_access) - [Access requested at publisher335](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher286](https://savrn.com/models?publisher=mradermacher) - [Qwen91](https://savrn.com/models?publisher=qwen) - [RunningHub.ai91](https://savrn.com/models?publisher=runninghubai) - [Yurun Yuan86](https://savrn.com/models?publisher=ryanyr) - [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 Pu69](https://savrn.com/models?publisher=putty77) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan61](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami60](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) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [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=yuruny) - [BAAR43](https://savrn.com/models?publisher=aixk) - [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud) [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) 11,122 models, sorted by most downloaded. Model ### [whisper-kazakh-ggml](https://savrn.com/models/whisper-kazakh-ggml) [Alisher Kunbolsyn](https://savrn.com/model-publishers/kunbolsyn) Open weights [View model](https://savrn.com/models/whisper-kazakh-ggml) Model · Speech recognition ### [whisper-large-v3-turbo-anv-sot-ct2](https://savrn.com/models/whisper-large-v3-turbo-anv-sot-ct2) [Gyori Lab for Computational Biomedicine](https://savrn.com/model-publishers/gyorilab) A CTranslate2 conversion of a Whisper large-v3-turbo fine-tune for Sesotho (Southern Sotho) trained by DSFSI on the African Next Voices corpus. The weights are unchanged apart from the format conversion and int8 quantization, for use with All credit for the model goes to the original authors. Please cite and refer to the source model and the African Next Voices dataset. Whisper has no Sesotho language token. The fine-tune decodes under the English token (en), which Whisper's language detection narrowly picks for it, so pass it explicitly to avoid detection drifting to another language. Turning off conditiononprevioustext avoids repetition loops on long audio. Converted from source revision… Open weights mit ctranslate2 [View model](https://savrn.com/models/whisper-large-v3-turbo-anv-sot-ct2) Model · Speech recognition ### [whisper-large-v3-turbo-anv-sot-mlx](https://savrn.com/models/whisper-large-v3-turbo-anv-sot-mlx) [Gyori Lab for Computational Biomedicine](https://savrn.com/model-publishers/gyorilab) An MLX conversion of a Whisper large-v3-turbo fine-tune for Sesotho (Southern Sotho) trained by DSFSI on the African Next Voices corpus. The weights are unchanged apart from the format conversion, for use with Apple silicon. All credit for the model goes to the original authors. Please cite and refer to the source model and the African Next Voices dataset. Whisper has no Sesotho language token. The fine-tune decodes under the English token (en), which Whisper's language detection narrowly picks for it, so pass it explicitly to avoid detection drifting to another language. Turning off conditiononprevioustext avoids repetition loops on long audio. Converted from source revision… Open weights mit mlx [View model](https://savrn.com/models/whisper-large-v3-turbo-anv-sot-mlx) Model · Speech recognition ### [whisper-large-v3-turbo-anv-zul-ct2](https://savrn.com/models/whisper-large-v3-turbo-anv-zul-ct2) [Gyori Lab for Computational Biomedicine](https://savrn.com/model-publishers/gyorilab) A CTranslate2 conversion of a Whisper large-v3-turbo fine-tune for Zulu trained by DSFSI on the African Next Voices corpus. The weights are unchanged apart from the format conversion and int8 quantization, for use with All credit for the model goes to the original authors. Please cite and refer to the source model and the African Next Voices dataset. Whisper has no isiZulu language token. The fine-tune decodes under the English token (en), which Whisper's language detection narrowly picks for it, so pass it explicitly to avoid detection drifting to another language. Turning off conditiononprevioustext avoids repetition loops on long audio. Converted from source revision… Open weights mit ctranslate2 [View model](https://savrn.com/models/whisper-large-v3-turbo-anv-zul-ct2) Model · Speech recognition ### [whisper-large-v3-turbo-anv-zul-mlx](https://savrn.com/models/whisper-large-v3-turbo-anv-zul-mlx) [Gyori Lab for Computational Biomedicine](https://savrn.com/model-publishers/gyorilab) An MLX conversion of a Whisper large-v3-turbo fine-tune for Zulu trained by DSFSI on the African Next Voices corpus. The weights are unchanged apart from the format conversion, for use with Apple silicon. All credit for the model goes to the original authors. Please cite and refer to the source model and the African Next Voices dataset. Whisper has no isiZulu language token. The fine-tune decodes under the English token (en), which Whisper's language detection narrowly picks for it, so pass it explicitly to avoid detection drifting to another language. Turning off conditiononprevioustext avoids repetition loops on long audio. Converted from source revision… Open weights mit mlx [View model](https://savrn.com/models/whisper-large-v3-turbo-anv-zul-mlx) Model ### [whisper-lv3-callcenter-A](https://savrn.com/models/whisper-lv3-callcenter-a) [Erfan Roudaki](https://savrn.com/model-publishers/erfanrou) Access requested at publisher [View model](https://savrn.com/models/whisper-lv3-callcenter-a) Model · Speech recognition ### [whisper-medium_indic_voices_63097_ratio_30_70_lr_1e-4_lgid_None_hinglish_mixed_scripts_FT](https://savrn.com/models/whisper-medium-indic-voices-63097-ratio-30-70-lr-1e-4-lgid-none-hinglish-mixed-scripts-ft) [Diana Davidson](https://savrn.com/model-publishers/dianavdavidson) This model is a fine-tuned version of openai/whisper-medium on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: constantwithwarmup - lrschedulerwarmupsteps: 500 - numepochs: 100 - Transformers 5.0.0.dev0 - Pytorch 2.9.0+cu126 - Datasets 3.6.0 - Tokenizers 0.22.2 Open weights apache-2.0 764M parameters transformers [View model](https://savrn.com/models/whisper-medium-indic-voices-63097-ratio-30-70-lr-1e-4-lgid-none-hinglish-mixed-scripts-ft) Model ### [whisper-models](https://savrn.com/models/whisper-models) [NHQTools](https://savrn.com/model-publishers/nhqtools) Open weights [View model](https://savrn.com/models/whisper-models) Model ### [whisper-punjabi-finetuned](https://savrn.com/models/whisper-punjabi-finetuned) [Pro Developer](https://savrn.com/model-publishers/pro-developer) Open weights 242M parameters [View model](https://savrn.com/models/whisper-punjabi-finetuned) Model · Speech recognition ### [whisper-small](https://savrn.com/models/whisper-small-2) [losCrossos](https://savrn.com/model-publishers/loscrossos) Whisper is a pre-trained model for automatic speech recognition (ASR) and speech translation. Trained on 680k hours of labelled data, Whisper models demonstrate a strong ability to generalise to many datasets and domains without the need for fine-tuning. Whisper was proposed in the paper Robust Speech Recognition via Large-Scale Weak Supervision by Alec Radford et al from OpenAI. The original code repository can be found here. Disclaimer: Content for this model card has partly been written by the Hugging Face team, and parts of it were copied and pasted from the original model card. Whisper is a Transformer based encoder-decoder model, also referred to as a sequence-to-sequence model. It… Open weights apache-2.0 242M parameters [View model](https://savrn.com/models/whisper-small-2) Model ### [whisper-small-dv](https://savrn.com/models/whisper-small-dv) [Harry Chen](https://savrn.com/model-publishers/chenharry6) Open weights [View model](https://savrn.com/models/whisper-small-dv) Model · Speech recognition ### [whisper-small-ha-merged](https://savrn.com/models/whisper-small-ha-merged) [Bello Abdullahi](https://savrn.com/model-publishers/therealbee) 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 242M parameters transformers [View model](https://savrn.com/models/whisper-small-ha-merged) Model · Speech recognition ### [whisper-small-tibetan](https://savrn.com/models/whisper-small-tibetan) [Ngodup](https://savrn.com/model-publishers/tenzin) This model is a fine-tuned version of openai/whisper-small on the generator dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 8 - evalbatchsize: 8 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 16 - lrschedulertype: linear - lrschedulerwarmupsteps: 300 - numepochs: 3.0 - Transformers 4.57.6 - Pytorch 2.2.1+cu121 - Datasets 5.0.1 - Tokenizers 0.22.2 Open weights apache-2.0 242M parameters transformers [View model](https://savrn.com/models/whisper-small-tibetan) Model · Speech recognition ### [whisper-v3-ft-ablation-grpo](https://savrn.com/models/whisper-v3-ft-ablation-grpo) [Tim Hu](https://savrn.com/model-publishers/huwenjie333) 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 1.5B parameters transformers [View model](https://savrn.com/models/whisper-v3-ft-ablation-grpo) Model ### [whisper.cpp](https://savrn.com/models/whisper-cpp) [ExNihilus](https://savrn.com/model-publishers/exnihilus) Unmodified copy of selected files from ggerganov/whisper.cpp at revision 5359861c739e955e79d9a303bcbc70fb988958b1, kept so that SubVox Pro (Unity editor plugin by ExNihilus) can download a stable, pinned version of the model. All credit goes to the original authors: the conversion by ggerganov and the original model openai/whisper-large-v3. - ggml-large-v2.bin - ggml-large-v3.bin - ggml-large-v3-turbo.bin Open weights mit [View model](https://savrn.com/models/whisper-cpp) Model · Speech recognition ### [whisper_finetune_16bit](https://savrn.com/models/whisper-finetune-16bit) [Gray Muff](https://savrn.com/model-publishers/graymuff) This whisper model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 1.5B parameters transformers [View model](https://savrn.com/models/whisper-finetune-16bit) Model · Speech recognition ### [whisperkit-hinglish-w8](https://savrn.com/models/whisperkit-hinglish-w8) [Raghu](https://savrn.com/model-publishers/raghuramr14) Core ML / WhisperKit pack converted from then 8-bit linear weight-quantized (AudioEncoder + TextDecoder) for smaller on-device footprint on Apple Silicon. (not "यार मैं कल ऑफिस नहीं जाऊँगा") Use language: "en" in WhisperKit decoding options. The fine-tune maps Hindi speech onto English/Latin tokens; forcing Hindi often breaks romanization. Typical layout (folder name may match what you uploaded): Or after Hub download, point --model / repo flags at this repository per current whisperkit-cli help. - 8-bit linear weight quantization is lossy storage; compute remains floating point. - Expect near-parity with the float Core ML build, not bit-identical transcripts. - Always spot-check on your… Open weights apache-2.0 whisperkit [View model](https://savrn.com/models/whisperkit-hinglish-w8) Model ### [whittle-dev](https://savrn.com/models/whittle-dev) [David](https://savrn.com/model-publishers/logic65) colab1/latest.pt is overwritten as the run progresses; colab1/step.pt are milestones every 1000 steps. Load with the trainer (RESUMECKPT=), not as a standalone model. Open weights [View model](https://savrn.com/models/whittle-dev) Model ### [whprobe9x7](https://savrn.com/models/whprobe9x7) [Sfdwefsa](https://savrn.com/model-publishers/attackerdfgfgds) Open weights mit [View model](https://savrn.com/models/whprobe9x7) Model · Reinforcement learning ### [wikispeedia-checkpoints-2026-10](https://savrn.com/models/wikispeedia-checkpoints-2026-10) [Liv d'Aliberti](https://savrn.com/model-publishers/od2961) This public research archive preserves all inventoried SFT checkpoints and model checkpoints from completed or stopped Wikispeedia RL experiments as of October 13, 2026. Currently running RL branches are excluded from this snapshot. Uploads are in progress: catalog.json lists the main inventory; legacy-catalog.json lists the additional stopped run exports. Both catalogs mark verified availability. A planned entry is usable only when available is true. Each checkpoint is an independent Transformers model directory or PEFT adapter directory beneath checkpoints/ /. Intermediate models previously stored as lossless byte-delta archives are reconstructed into their original model files before… Open weights other transformers [View model](https://savrn.com/models/wikispeedia-checkpoints-2026-10) Model ### [wildlens-bioclip](https://savrn.com/models/wildlens-bioclip) [Aditi Kumari](https://savrn.com/model-publishers/esu05) Open weights mit [View model](https://savrn.com/models/wildlens-bioclip) Model ### [work](https://savrn.com/models/work) [Izmy](https://savrn.com/model-publishers/veteranizmy) Open weights [View model](https://savrn.com/models/work) Model ### [workflows](https://savrn.com/models/workflows) [Meir Yaffe](https://savrn.com/model-publishers/fourtharcheteer) not-for-all-audiences Open weights [View model](https://savrn.com/models/workflows) Model ### [wt-nids-e5-llama31-8b](https://savrn.com/models/wt-nids-e5-llama31-8b) [SeokHyun](https://savrn.com/model-publishers/sh98) Open weights [View model](https://savrn.com/models/wt-nids-e5-llama31-8b) ## 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.