# Models, Page 248 of 512 Source: https://savrn.com/models/page/248 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. 12,283Models 3,510Datasets 290Papers 7,436Publishers 12,632Sourced relationships Updated 2026-10-22 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,506](https://savrn.com/models?task=Text+generation) - [Image and text to text560](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction396](https://savrn.com/models?task=Feature+extraction) - [Robotics379](https://savrn.com/models?task=Robotics) - [Text classification328](https://savrn.com/models?task=Text+classification) - [Text to image228](https://savrn.com/models?task=Text+to+image) - [Speech recognition185](https://savrn.com/models?task=Speech+recognition) - [Text to speech143](https://savrn.com/models?task=Text+to+speech) - [Image classification139](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) - [Token classification99](https://savrn.com/models?task=Token+classification) - [Reinforcement learning97](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 - [transformers2,947](https://savrn.com/models?library=transformers) - [peft361](https://savrn.com/models?library=peft) - [diffusers202](https://savrn.com/models?library=diffusers) - [pytorch194](https://savrn.com/models?library=pytorch) - [gguf181](https://savrn.com/models?library=gguf) - [mlx157](https://savrn.com/models?library=mlx) - [lerobot150](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp45](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [onnx40](https://savrn.com/models?library=onnx) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [diffusion-single-file29](https://savrn.com/models?library=diffusion-single-file) - [minimax-h327](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [exllamav320](https://savrn.com/models?library=exllamav3) - [openvino20](https://savrn.com/models?library=openvino) - [coreml18](https://savrn.com/models?library=coreml) - [litert17](https://savrn.com/models?library=litert) - [keras15](https://savrn.com/models?library=keras) Modality - [Other7,017](https://savrn.com/models?modality=Other) - [Text2,788](https://savrn.com/models?modality=Text) - [Image and text619](https://savrn.com/models?modality=Image+and+text) - [Image593](https://savrn.com/models?modality=Image) - [Audio476](https://savrn.com/models?modality=Audio) - [Control476](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-2.03,285](https://savrn.com/models?license=apache-2.0) - [mit1,305](https://savrn.com/models?license=mit) - [other759](https://savrn.com/models?license=other) - [cc-by-nc-4.0209](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0164](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-4.052](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) - [unknown32](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-3.020](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 weights11,872](https://savrn.com/models?access=open_access) - [Access requested at publisher411](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher324](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan110](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai102](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 Yuan64](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Yurun Yuan61](https://savrn.com/models?publisher=yuruny) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Yurun Yuan57](https://savrn.com/models?publisher=yyuan244) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [Khazzz1c52](https://savrn.com/models?publisher=khazic) - [Christopher Subia-Waud50](https://savrn.com/models?publisher=cwaud) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [NVIDIA49](https://savrn.com/models?publisher=nvidia) [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) 12,283 models, sorted by most downloaded. Model · Text generation ### [acquisition_student_AS_tracin_mmlupro_qwen3b_5000](https://savrn.com/models/acquisition-student-as-tracin-mmlupro-qwen3b-5000) [Ishika Agarwal](https://savrn.com/model-publishers/ishikaa) 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 3.1B parameters 32,768 tokens transformers [View model](https://savrn.com/models/acquisition-student-as-tracin-mmlupro-qwen3b-5000) Model · Text generation ### [acquisition_student_DataEnvGym_combine_WGM_MP_qwen14b_5000](https://savrn.com/models/acquisition-student-dataenvgym-combine-wgm-mp-qwen14b-5000) [Ishika Agarwal](https://savrn.com/model-publishers/ishikaa) Open weights 14.8B parameters transformers [View model](https://savrn.com/models/acquisition-student-dataenvgym-combine-wgm-mp-qwen14b-5000) Model · Text generation ### [acquisition_student_DataEnvGym_medmcqa_llama8b_5000](https://savrn.com/models/acquisition-student-dataenvgym-medmcqa-llama8b-5000) [Sofia](https://savrn.com/model-publishers/sstoica12) 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 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/acquisition-student-dataenvgym-medmcqa-llama8b-5000) Model · Text generation ### [acquisition_student_random_medmcqa_llama1b_5000](https://savrn.com/models/acquisition-student-random-medmcqa-llama1b-5000) [Ishika Agarwal](https://savrn.com/model-publishers/ishikaa) 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 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/acquisition-student-random-medmcqa-llama1b-5000) Model · Text generation ### [acquisition_student_random_mmlupro_llama8b_5000](https://savrn.com/models/acquisition-student-random-mmlupro-llama8b-5000) [Ishika Agarwal](https://savrn.com/model-publishers/ishikaa) 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 8B parameters 131,072 tokens transformers [View model](https://savrn.com/models/acquisition-student-random-mmlupro-llama8b-5000) Model · Text generation ### [acquisition_student_randomselfgen_medmcqa_llama1b_5000](https://savrn.com/models/acquisition-student-randomselfgen-medmcqa-llama1b-5000) [Ishika Agarwal](https://savrn.com/model-publishers/ishikaa) 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 1.2B parameters 131,072 tokens transformers [View model](https://savrn.com/models/acquisition-student-randomselfgen-medmcqa-llama1b-5000) Model · Robotics ### [act-armchemist-open-cap-100k](https://savrn.com/models/act-armchemist-open-cap-100k) [Dominho](https://savrn.com/model-publishers/dominho) ACT policy trained to open a reagent container cap with the omxdual robot. This repository contains the final 100,000-step checkpoint, including normalization buffers in model.safetensors. - Excluded original Hugging Face episode indices after user visual review: 3, 35, 42, 48. - The retained episodes were copied and renumbered 0–44. See datasetselection.json for the original-to-subset correspondence. - Source data was preserved. All retained action/state values and videos were verified against the source. Provide all four image streams as [batch, 3, 480, 640] tensors in [0, 1], using the same camera views and preprocessing as the recordings: - observation.images.camhead… Open weights 52M parameters lerobot [View model](https://savrn.com/models/act-armchemist-open-cap-100k) Model · Robotics ### [act-finetune-pick-and-stack-adjust](https://savrn.com/models/act-finetune-pick-and-stack-adjust) [Hayato Nakamura](https://savrn.com/model-publishers/tron-hayato) Open weights apache-2.0 52M parameters lerobot [View model](https://savrn.com/models/act-finetune-pick-and-stack-adjust) Model ### [act-finetune-pick-and-stack-adjust-checkpoint](https://savrn.com/models/act-finetune-pick-and-stack-adjust-checkpoint) [Hayato Nakamura](https://savrn.com/model-publishers/tron-hayato) Open weights [View model](https://savrn.com/models/act-finetune-pick-and-stack-adjust-checkpoint) Model · Robotics ### [act-finetuned-base-modify-hold-pos](https://savrn.com/models/act-finetuned-base-modify-hold-pos) [Hayato Nakamura](https://savrn.com/model-publishers/tron-hayato) Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning… Open weights apache-2.0 52M parameters lerobot [View model](https://savrn.com/models/act-finetuned-base-modify-hold-pos) Model ### [act-finetuned-base-modify-hold-pos-checkpoint](https://savrn.com/models/act-finetuned-base-modify-hold-pos-checkpoint) [Hayato Nakamura](https://savrn.com/model-publishers/tron-hayato) Open weights [View model](https://savrn.com/models/act-finetuned-base-modify-hold-pos-checkpoint) Model ### [act-isg1020-iors-small-bs1024-s0](https://savrn.com/models/act-isg1020-iors-small-bs1024-s0) [Kmy](https://savrn.com/model-publishers/kmy17518) Open weights [View model](https://savrn.com/models/act-isg1020-iors-small-bs1024-s0) Model ### [act-isg1020-multitask5-bs1024-s0](https://savrn.com/models/act-isg1020-multitask5-bs1024-s0) [Kmy](https://savrn.com/model-publishers/kmy17518) Open weights [View model](https://savrn.com/models/act-isg1020-multitask5-bs1024-s0) Model ### [act-isg1020-multitask5-late-ddp4-bs1024-s0](https://savrn.com/models/act-isg1020-multitask5-late-ddp4-bs1024-s0) [Kmy](https://savrn.com/model-publishers/kmy17518) Open weights [View model](https://savrn.com/models/act-isg1020-multitask5-late-ddp4-bs1024-s0) Model ### [act-isg1020-multitask5-relq-ddp4-bs1024-s0](https://savrn.com/models/act-isg1020-multitask5-relq-ddp4-bs1024-s0) [Kmy](https://savrn.com/model-publishers/kmy17518) Open weights [View model](https://savrn.com/models/act-isg1020-multitask5-relq-ddp4-bs1024-s0) Model ### [act-isg1020-objscale-bs1024-s0](https://savrn.com/models/act-isg1020-objscale-bs1024-s0) [Kmy](https://savrn.com/model-publishers/kmy17518) Open weights [View model](https://savrn.com/models/act-isg1020-objscale-bs1024-s0) Model ### [act-isg1020-reloc-bs1024-s0](https://savrn.com/models/act-isg1020-reloc-bs1024-s0) [Kmy](https://savrn.com/model-publishers/kmy17518) Open weights [View model](https://savrn.com/models/act-isg1020-reloc-bs1024-s0) Model ### [act-pick-and-place-cube](https://savrn.com/models/act-pick-and-place-cube) [George Igwegbe](https://savrn.com/model-publishers/gigwegbe) Open weights [View model](https://savrn.com/models/act-pick-and-place-cube) Model · Robotics ### [act-pick-and-stack](https://savrn.com/models/act-pick-and-stack) [Hayato Nakamura](https://savrn.com/model-publishers/tron-hayato) Open weights apache-2.0 52M parameters lerobot [View model](https://savrn.com/models/act-pick-and-stack) Model ### [act-pick-and-stack-checkpoint](https://savrn.com/models/act-pick-and-stack-checkpoint) [Hayato Nakamura](https://savrn.com/model-publishers/tron-hayato) Open weights [View model](https://savrn.com/models/act-pick-and-stack-checkpoint) Model ### [act-so101-040000](https://savrn.com/models/act-so101-040000) [Yasuhiko Fukumoto](https://savrn.com/model-publishers/fkmt) Open weights 52M parameters [View model](https://savrn.com/models/act-so101-040000) Model ### [act-so101-060000](https://savrn.com/models/act-so101-060000) [Yasuhiko Fukumoto](https://savrn.com/model-publishers/fkmt) Open weights 52M parameters [View model](https://savrn.com/models/act-so101-060000) Model ### [act-so101-080000](https://savrn.com/models/act-so101-080000) [Yasuhiko Fukumoto](https://savrn.com/model-publishers/fkmt) Open weights 52M parameters [View model](https://savrn.com/models/act-so101-080000) Model ### [act-so101-100000](https://savrn.com/models/act-so101-100000) [Yasuhiko Fukumoto](https://savrn.com/model-publishers/fkmt) Open weights 52M parameters [View model](https://savrn.com/models/act-so101-100000) ## 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.