# Models, Page 418 of 464 Source: https://savrn.com/models/page/418 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) - 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[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 - 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[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 ### [security-ir-autoencoder-768d](https://savrn.com/models/security-ir-autoencoder-768d) [Publicus](https://savrn.com/model-publishers/publicus) This lane contains 0 distinct original checkpoint variants. Each checkpoint uses a content-addressed path and an asset manifest preserving its schema, decoder role, available format/profile binding, codec, source declarations, and companion corpus or embedding assets. Unknown bindings remain unknown. Registration and publication do not qualify numerical quality, source-only reconstruction, teacher use, proof authority, or serving. No retained checkpoint is activated for inference. The Legal 768D checkpoint is inherited initialization only; Codebase, Intent, and Security 768D lanes have no retained checkpoints. Codebase 384D envelopes retain their borrowed Security payload identity and are… Open weights [View model](https://savrn.com/models/security-ir-autoencoder-768d) Model ### [security-ir-autoencoder-8d](https://savrn.com/models/security-ir-autoencoder-8d) [Publicus](https://savrn.com/model-publishers/publicus) This lane contains 2 distinct original checkpoint variants. Each checkpoint uses a content-addressed path and an asset manifest preserving its schema, decoder role, available format/profile binding, codec, source declarations, and companion corpus or embedding assets. Unknown bindings remain unknown. Registration and publication do not qualify numerical quality, source-only reconstruction, teacher use, proof authority, or serving. No retained checkpoint is activated for inference. The Legal 768D checkpoint is inherited initialization only; Codebase, Intent, and Security 768D lanes have no retained checkpoints. Codebase 384D envelopes retain their borrowed Security payload identity and are… Open weights [View model](https://savrn.com/models/security-ir-autoencoder-8d) Model ### [seg-vit-h-onnx](https://savrn.com/models/seg-vit-h-onnx) [Wangxun1010](https://savrn.com/model-publishers/wangxun1010) Open weights [View model](https://savrn.com/models/seg-vit-h-onnx) Model ### [seismic-interpolation-benchmark](https://savrn.com/models/seismic-interpolation-benchmark) [GeoBrain](https://savrn.com/model-publishers/geobrain) Deep-learning-based missing-trace reconstruction on pre-stack seismic shot gathers. Given a shot gather with masked (missing) traces, the model reconstructs the full gather. Each experiment is one architecture trained on one missing-trace scenario with one random seed. Model directories are suffixed with the dataset the model was trained on: - mobil — Mobil field dataset (pre-stack seismic shot gathers) - segc3 — SEG C3 synthetic dataset (wiki.seg.org/wiki/C3): 9 regular shots, 201 traces x 625 time samples, dt = 2 ms - Chai2020 UNet (chai2020unet) — 2D U-Net (Chai et al., 2020, IEEE TGRS, DOI 10.1109/TGRS.2019.2961015): 50 layers = 19 convolutions (5x5, same padding) + 18 ReLU + 4 max-pool… Open weights pytorch [View model](https://savrn.com/models/seismic-interpolation-benchmark) Model · Robotics ### [select_block_act_dp3_4096](https://savrn.com/models/select-block-act-dp3-4096) [Testing](https://savrn.com/model-publishers/escapebirdy) 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. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint. Open weights apache-2.0 40M parameters lerobot [View model](https://savrn.com/models/select-block-act-dp3-4096) Model · Robotics ### [select_block_act_fdp3_4096](https://savrn.com/models/select-block-act-fdp3-4096) [Testing](https://savrn.com/model-publishers/escapebirdy) 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. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint. Open weights apache-2.0 40M parameters lerobot [View model](https://savrn.com/models/select-block-act-fdp3-4096) Model · Robotics ### [select_block_act_octa_4096](https://savrn.com/models/select-block-act-octa-4096) [Testing](https://savrn.com/model-publishers/escapebirdy) 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. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint. Open weights apache-2.0 42M parameters lerobot [View model](https://savrn.com/models/select-block-act-octa-4096) Model · Robotics ### [select_block_dit_dp3_4096](https://savrn.com/models/select-block-dit-dp3-4096) [Testing](https://savrn.com/model-publishers/escapebirdy) This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint. Open weights apache-2.0 18M parameters lerobot [View model](https://savrn.com/models/select-block-dit-dp3-4096) Model · Robotics ### [select_block_dit_fdp3_4096](https://savrn.com/models/select-block-dit-fdp3-4096) [Testing](https://savrn.com/model-publishers/escapebirdy) This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint. Open weights apache-2.0 18M parameters lerobot [View model](https://savrn.com/models/select-block-dit-fdp3-4096) Model · Robotics ### [select_block_dit_octa_4096](https://savrn.com/models/select-block-dit-octa-4096) [Testing](https://savrn.com/model-publishers/escapebirdy) This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint. Open weights apache-2.0 21M parameters lerobot [View model](https://savrn.com/models/select-block-dit-octa-4096) Model · Robotics ### [select_block_dp_octe_2stage_4096](https://savrn.com/models/select-block-dp-octe-2stage-4096) [Testing](https://savrn.com/model-publishers/escapebirdy) Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint. Open weights apache-2.0 266M parameters lerobot [View model](https://savrn.com/models/select-block-dp-octe-2stage-4096) Model ### [self-evolving-safety-ckpts](https://savrn.com/models/self-evolving-safety-ckpts) [Zihan Zhao](https://savrn.com/model-publishers/ultramar1ne) Anonymized checkpoint ladders for a study of safety behavior under RLVR self-play and SFT. Folders are codenames; the codename->setting mapping is withheld. (actor/modelworldsize.pt + config); use veRL modelmerger/convert2hf to load. step180 is HF safetensors. Access requested at publisher [View model](https://savrn.com/models/self-evolving-safety-ckpts) Model ### [self-supervised](https://savrn.com/models/self-supervised) [Joaquín A. González](https://savrn.com/model-publishers/williamgwg) A structured set of research notes on Self Supervised, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with summary.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… Open weights cc-by-4.0 16,576 parameters 128 tokens [View model](https://savrn.com/models/self-supervised) Model · Sentence similarity ### [semantic-lite-2](https://savrn.com/models/semantic-lite-2) [Zulfah](https://savrn.com/model-publishers/ukung) Semantic-Lite-2 is a lightweight multilingual sentence embedding model that produces 256-dimensional semantic vectors. It is designed for semantic search, sentence similarity, clustering, retrieval, and retrieval-augmented generation (RAG) tasks. The model is built on top of the Spark-X2.5-1.7B backbone using a frozen-backbone plus trainable-projection-head approach. The 256-dimensional output keeps vector storage compact while preserving strong retrieval quality. Vectors are L2-normalized, so cosine similarity is computed as a simple dot product. Evaluated on 500 Indonesian NLI evaluation pairs (retrieval task, chance level 0.2%): Cross-lingual evaluation (10 languages, 20 pairs per… Open weights apache-2.0 460M parameters 1,048,576 tokens transformers [View model](https://savrn.com/models/semantic-lite-2) Model ### [semanticlm5](https://savrn.com/models/semanticlm5) [Ebisuke](https://savrn.com/model-publishers/ebisuke) Open weights 932M parameters 262,144 tokens [View model](https://savrn.com/models/semanticlm5) Model ### [semg-intent-restoration-pipeline](https://savrn.com/models/semg-intent-restoration-pipeline) [Jorge Ortiz Ceballos](https://savrn.com/model-publishers/ocjorge) Three chained PyTorch models (plus one exploratory variant) implementing the Each 200 ms / 400-sample sEMG window (2000 Hz, single channel, MyoWare-compatible) is routed through: intent detection → quality assessment → (optional) restoration. Operating thresholds (τintent = 0.40, τquality = 0.50) were selected via grid search on the validation split only, and evaluated once on a held-out test split. If you want to reproduce the pipeline exactly as described in Section 4.7 and Figure 1 of the paper, use only the first three files. The task-aware variant is a separate experiment reported transparently as a limitation, not a replacement for restorerbest.pt — see the "Downstream Motor-Intent… Open weights cc-by-4.0 [View model](https://savrn.com/models/semg-intent-restoration-pipeline) Model ### [semif-27b-lora](https://savrn.com/models/semif-27b-lora) [gxydResearch](https://savrn.com/model-publishers/gxydresearch) Open weights [View model](https://savrn.com/models/semif-27b-lora) Model ### [semiopd_klear8b_qwen3moe30b_ot3math-step10](https://savrn.com/models/semiopd-klear8b-qwen3moe30b-ot3math-step10) [Minhyuk Seo](https://savrn.com/model-publishers/mhseo2) Open weights 8.2B parameters 40,960 tokens [View model](https://savrn.com/models/semiopd-klear8b-qwen3moe30b-ot3math-step10) Model ### [semiopd_sftb8dm15e_s1000_qwen3moe30b_ot3math-step10](https://savrn.com/models/semiopd-sftb8dm15e-s1000-qwen3moe30b-ot3math-step10) [Minhyuk Seo](https://savrn.com/model-publishers/mhseo2) Open weights 8.2B parameters 32,768 tokens [View model](https://savrn.com/models/semiopd-sftb8dm15e-s1000-qwen3moe30b-ot3math-step10) Model ### [semiopd_sftb8dm15e_s1000_qwen3moe30b_ot3math-step50](https://savrn.com/models/semiopd-sftb8dm15e-s1000-qwen3moe30b-ot3math-step50) [Minhyuk Seo](https://savrn.com/model-publishers/mhseo2) Open weights 8.2B parameters 32,768 tokens [View model](https://savrn.com/models/semiopd-sftb8dm15e-s1000-qwen3moe30b-ot3math-step50) Model ### [semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step10](https://savrn.com/models/semiopd-sftb8dm15e-s2000-qwen3moe30b-ot3math-step10) [Minhyuk Seo](https://savrn.com/model-publishers/mhseo2) Open weights 8.2B parameters 32,768 tokens [View model](https://savrn.com/models/semiopd-sftb8dm15e-s2000-qwen3moe30b-ot3math-step10) Model ### [semiopd_sftb8dm15e_s2000_qwen3moe30b_ot3math-step50](https://savrn.com/models/semiopd-sftb8dm15e-s2000-qwen3moe30b-ot3math-step50) [Minhyuk Seo](https://savrn.com/model-publishers/mhseo2) Open weights 8.2B parameters 32,768 tokens [View model](https://savrn.com/models/semiopd-sftb8dm15e-s2000-qwen3moe30b-ot3math-step50) Model · Text generation ### [semsimula-ladder-live-owt-d384-l2-attention-potential-rfqk-norc](https://savrn.com/models/semsimula-ladder-live-owt-d384-l2-attention-potential-rfqk-norc) [Dimitar Gueorguiev](https://savrn.com/model-publishers/dimitarpg13) Settled is the mean of the last three evals (54.20, 54.39, 54.22); best 52.50 at step 31,000, final 54.22. Consecutive evals near the end differ by about ±1 PPL. - The field alone is a real but weaker route. It lowers the conservative-only model's perplexity by 6.0%; the register path alone lowers it by 8.0%. - Beside the registers it adds almost nothing: G3′ is 0.4% better than G2. The two routes largely substitute for each other. This model sees exactly 532,480,000 training tokens — 32,500 steps × 32 × 512 = 532,480,000 tokens — the same budget, corpus, tokenizer and validation batches as every other model in the table. No model is trained longer than another. - The base is F3.1: the… Open weights cc-by-4.0 pytorch [View model](https://savrn.com/models/semsimula-ladder-live-owt-d384-l2-attention-potential-rfqk-norc) Model · Text generation ### [semsimula-ladder-owt-d384-l2-attention](https://savrn.com/models/semsimula-ladder-owt-d384-l2-attention) [Dimitar Gueorguiev](https://savrn.com/model-publishers/dimitarpg13) One arm of the SemSimula mechanism ladder — OpenWebText, d=384, L=2: five models that differ by exactly one mechanism each, so that every gap in the ordering prices one component by construction rather than by attribution. Italic entries are pre-registered predictions, recorded before the run. The ordering is itself the prediction; any inversion is a result. Every arm above sees exactly 532,480,000 training tokens — 32,500 steps × 32 × 512 = 532,480,000 tokens — on the same corpus, tokenizer and validation batches. No arm is trained longer than another, so a gap in the table is a gap between mechanisms and never a difference in budget. The models below integrate a damped mechanical system… Open weights cc-by-4.0 pytorch [View model](https://savrn.com/models/semsimula-ladder-owt-d384-l2-attention) ## 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.