# Models, Page 167 of 525 Source: https://savrn.com/models/page/167 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. 12,579Models 3,583Datasets 291Papers 7,578Publishers 12,850Sourced relationships Updated 2026-10-23 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,548](https://savrn.com/models?task=Text+generation) - [Image and text to text577](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction398](https://savrn.com/models?task=Feature+extraction) - [Robotics398](https://savrn.com/models?task=Robotics) - [Text classification331](https://savrn.com/models?task=Text+classification) - [Text to image232](https://savrn.com/models?task=Text+to+image) - [Speech recognition187](https://savrn.com/models?task=Speech+recognition) - [Text to speech145](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) - [Reinforcement learning100](https://savrn.com/models?task=Reinforcement+learning) - [Token classification100](https://savrn.com/models?task=Token+classification) - [Zero-shot classification94](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) - [Translation80](https://savrn.com/models?task=Translation) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [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 classification61](https://savrn.com/models?task=Video+classification) - [Image to text60](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers3,001](https://savrn.com/models?library=transformers) - [peft367](https://savrn.com/models?library=peft) - [diffusers206](https://savrn.com/models?library=diffusers) - [pytorch198](https://savrn.com/models?library=pytorch) - [gguf187](https://savrn.com/models?library=gguf) - [mlx169](https://savrn.com/models?library=mlx) - [lerobot152](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp46](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file) - [onnx40](https://savrn.com/models?library=onnx) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [minimax-h328](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [coreml22](https://savrn.com/models?library=coreml) - [exllamav322](https://savrn.com/models?library=exllamav3) - [openvino20](https://savrn.com/models?library=openvino) - [litert17](https://savrn.com/models?library=litert) - [litert-lm16](https://savrn.com/models?library=litert-lm) Modality - [Other7,212](https://savrn.com/models?modality=Other) - [Text2,838](https://savrn.com/models?modality=Text) - [Image and text637](https://savrn.com/models?modality=Image+and+text) - [Image598](https://savrn.com/models?modality=Image) - [Control498](https://savrn.com/models?modality=Control) - [Audio481](https://savrn.com/models?modality=Audio) - [Video116](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-2.03,344](https://savrn.com/models?license=apache-2.0) - [mit1,319](https://savrn.com/models?license=mit) - [other796](https://savrn.com/models?license=other) - [cc-by-nc-4.0213](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-4.055](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.240](https://savrn.com/models?license=llama3.2) - [unknown33](https://savrn.com/models?license=unknown) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [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) - [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [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 weights12,153](https://savrn.com/models?access=open_access) - [Access requested at publisher426](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan115](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai105](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) - [Yurun Yuan65](https://savrn.com/models?publisher=yurunyyr) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan62](https://savrn.com/models?publisher=yuruny) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yurun Yuan58](https://savrn.com/models?publisher=yyuan244) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Christopher Subia-Waud55](https://savrn.com/models?publisher=cwaud) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [NVIDIA50](https://savrn.com/models?publisher=nvidia) - [Wang49](https://savrn.com/models?publisher=davidwdw) [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) 12,579 models, sorted by most downloaded. Model ### [HybridViTSmall_ARCF_VQAv2-checkpoints-ssl-full](https://savrn.com/models/hybridvitsmall-arcf-vqav2-checkpoints-ssl-full) [Miranda Surya Prakash](https://savrn.com/model-publishers/mirandaabhilash) Open weights [View model](https://savrn.com/models/hybridvitsmall-arcf-vqav2-checkpoints-ssl-full) Model ### [HybridViTSmall_ARCF_VQAv2-checkpoints-tokbce-v2-ssl-full](https://savrn.com/models/hybridvitsmall-arcf-vqav2-checkpoints-tokbce-v2-ssl-full) [Miranda Surya Prakash](https://savrn.com/model-publishers/mirandaabhilash) Open weights [View model](https://savrn.com/models/hybridvitsmall-arcf-vqav2-checkpoints-tokbce-v2-ssl-full) Model ### [HybridViTSmall_ARCF_VQAv2-results-tokbce-v2-ssl-full](https://savrn.com/models/hybridvitsmall-arcf-vqav2-results-tokbce-v2-ssl-full) [Miranda Surya Prakash](https://savrn.com/model-publishers/mirandaabhilash) Open weights [View model](https://savrn.com/models/hybridvitsmall-arcf-vqav2-results-tokbce-v2-ssl-full) Model ### [HybridViTSmall_ARCF_VQAv2-ssl-pipc2-full-checkpoints](https://savrn.com/models/hybridvitsmall-arcf-vqav2-ssl-pipc2-full-checkpoints) [Miranda Surya Prakash](https://savrn.com/model-publishers/mirandaabhilash) Open weights [View model](https://savrn.com/models/hybridvitsmall-arcf-vqav2-ssl-pipc2-full-checkpoints) Model ### [HydraQE](https://savrn.com/models/hydraqe) [Ohio State Speech and Language Technologies Lab](https://savrn.com/model-publishers/osu-slatelab) Open weights apache-2.0 214M parameters hydraqe [View model](https://savrn.com/models/hydraqe) Model ### [Hypa-Llama-SNAC-ASR-run1-runpod-256](https://savrn.com/models/hypa-llama-snac-asr-run1-runpod-256) [Hypa-Intelligence](https://savrn.com/model-publishers/hypaai) Open weights [View model](https://savrn.com/models/hypa-llama-snac-asr-run1-runpod-256) Model · Image and text to text ### [HyzeK3](https://savrn.com/models/hyzek3) [Hitesh Vinothkumar](https://savrn.com/model-publishers/hyzeai) Kimi K3 is an open-weight, native multimodal agentic model and our most capable model to date. It is a 2.8T-parameter model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), with native vision capabilities and a 1-million-token context window. It is the world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning. All Kimi K3 results are obtained with reasoning effort set to 'max' and temperature = 1.0. For single-step tasks, such as GPQA Diamond, HLE-Full, and vision benchmarks without tools, we set top-p = 0.95; for agentic tasks, we set top-p = 1.0. For HLE-Full, MMMU-Pro, CharXiv (RQ), MathVision… Open weights other 2.8T parameters 1,048,576 tokens transformers [View model](https://savrn.com/models/hyzek3) Model · Image feature extraction ### [I-JEPA-Agro](https://savrn.com/models/i-jepa-agro) [Junior R F Junior](https://savrn.com/model-publishers/joserfjunior) Projeto completo de triagem visual de anomalias em folhas de soja usando como extrator de representações e HeraclitusDB como banco de eventos e vetores. 1. lê um manifesto com fazenda, talhão, safra e cultivar; 2. executa o I-JEPA ViT-G/16 localmente; 3. aplica média aos patch tokens e normalização L2; 4. persiste cada imagem e seu embedding euclidiano no HeraclitusDB; 5. cria um artefato local para avaliação offline; 6. treina uma sonda linear; 7. busca imagens visualmente semelhantes; 8. oferece busca por uma API FastAPI. - Python 3.11+; - HeraclitusDB ativo em 127.0.0.1:7474; - vários GB livres para o checkpoint; - RAM suficiente para um modelo com aproximadamente 1B parâmetros; - GPU é… Open weights mit transformers [View model](https://savrn.com/models/i-jepa-agro) Model ### [I-am-not-gone](https://savrn.com/models/i-am-not-gone) [Darkhn](https://savrn.com/model-publishers/darkhn) Been currently busy training another gemma 4 31B, did about 12 run's so far, playing with swa value, rope, turn masking, so far 2048 seems to be the sweet spot for training at 8k ctx. should be releasing a new model in about 2-3 weeks, training on local 3090s is not very fast =p. Open weights apache-2.0 [View model](https://savrn.com/models/i-am-not-gone) Model ### [IAGenio](https://savrn.com/models/iagenio) [Juan](https://savrn.com/model-publishers/iagenio) Open weights [View model](https://savrn.com/models/iagenio) Model ### [IDK](https://savrn.com/models/idk) [MIke Arf](https://savrn.com/model-publishers/hentaimike) Open weights unknown [View model](https://savrn.com/models/idk) Model ### [IFNd2Z3VaoqIuee1](https://savrn.com/models/ifnd2z3vaoqiuee1) [15A7](https://savrn.com/model-publishers/15a7) Open weights [View model](https://savrn.com/models/ifnd2z3vaoqiuee1) Model ### [IGROK](https://savrn.com/models/igrok) [Mezdunogovyjvo](https://savrn.com/model-publishers/mezdunogovyjvo) Open weights [View model](https://savrn.com/models/igrok) Model · Question answering ### [ILM](https://savrn.com/models/ilm) [Datamine Network Inc.](https://savrn.com/model-publishers/dataminenetwork) CPU only. ILM is a language model that needs no GPU, to learn or to answer. On one desktop CPU it learns each benchmark below from its lessons in seconds, then answers its test questions. Why no GPU? A neural network, such as a transformer, learns by changing millions of decimal weights. It repeats billions of multiplications over many passes through the data, and a GPU is built for that work. ILM has no weights. It stores whole-number counts: how often each pattern in a text came with each answer. Counting is the addition of whole numbers, and a normal CPU does it fast. ILM learns from lessons: a short text, a question and the exact answer (for two tasks below, also the labels that the set… Open weights apache-2.0 [View model](https://savrn.com/models/ilm) Model · Image feature extraction ### [IM-Animation-Motion-Encoder](https://savrn.com/models/im-animation-motion-encoder) [Fynn](https://savrn.com/model-publishers/rbaerk) IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation This release contains the final locally retained TiTok-based motion encoder implementation and an exported checkpoint. It provides frame-level motion tokens; it does not include the full animation generator or retargeting network. Code and project videos are available in the linked GitHub repository. The encoder was verified with PyTorch 2.7.1 on CPU using the exported BF16 weights. The weight file is 607,017,208 bytes (approximately 579 MiB). Its SHA256 and provenance are in checkpointinfo.json. frames must be RGB, normalized to [0, 1], with shape [N, 3, 256, 256]. The training preprocessing pads… Open weights apache-2.0 [View model](https://savrn.com/models/im-animation-motion-encoder) Model ### [IMDBert](https://savrn.com/models/imdbert) [Muhammad Rayhan](https://savrn.com/model-publishers/rayhan-08) Open weights apache-2.0 [View model](https://savrn.com/models/imdbert) Model ### [IMF](https://savrn.com/models/imf) [Fushi Nguyenex](https://savrn.com/model-publishers/fushinguyenex) Open weights mit [View model](https://savrn.com/models/imf) Model ### [IMU4d](https://savrn.com/models/imu4d) [Tianhang Cheng](https://savrn.com/model-publishers/tianhangcheng7) Open weights mit [View model](https://savrn.com/models/imu4d) Model ### [IdeaLens-LogisticClassifier](https://savrn.com/models/idealens-logisticclassifier) [Rishanth Rajendhran](https://savrn.com/model-publishers/rishanthrajendhran) IdeaLens-LogisticClassifier is an idea-level detector: it judges whose ideas a document contains, not who wrote its words, so a document whose ideas are a person's counts as human however much of its prose an AI wrote. It is one of the detectors released with IdeaLens and trained on the same data. The idealens package (PyPI) runs the whole pipeline: it assigns each document one of the eight formats, extracts the outline with the prompt, role vocabulary and worked examples the detectors were trained with, and scores it with this model and the thresholds in this repo. Input is JSONL with a text field per document. To score outlines you already have, use idealens score outlines.jsonl -o… Open weights cc-by-nc-sa-4.0 [View model](https://savrn.com/models/idealens-logisticclassifier) Model ### [IdeaLens-LogisticClassifier-PerItem](https://savrn.com/models/idealens-logisticclassifier-peritem) [Rishanth Rajendhran](https://savrn.com/model-publishers/rishanthrajendhran) IdeaLens-LogisticClassifier-PerItem is an idea-level detector: it judges whose ideas a document contains, not who wrote its words, so a document whose ideas are a person's counts as human however much of its prose an AI wrote. It is one of the detectors released with IdeaLens and trained on the same data. The idealens package (PyPI) runs the whole pipeline: it assigns each document one of the eight formats, extracts the outline with the prompt, role vocabulary and worked examples the detectors were trained with, and scores it with this model and the thresholds in this repo. Input is JSONL with a text field per document. To score outlines you already have, use idealens score outlines.jsonl… Open weights cc-by-nc-sa-4.0 [View model](https://savrn.com/models/idealens-logisticclassifier-peritem) Model ### [Ideogram-4-Text-Encoder-Heretic-DHQ-GGUF](https://savrn.com/models/ideogram-4-text-encoder-heretic-dhq-gguf) [Pottokao](https://savrn.com/model-publishers/pottokao) DHQ = Dual-Head Quant. The heretic (abliterated) Qwen3-VL-8B from Qwen-Image-2.1-Text-Encoder-Heretic, quantized so that every tensor gets the precision that matters to what the Ideogram 4 DiT actually reads (13 interleaved hidden layers + the final-norm output), measured per tensor with gradients through the DiT's text input stage. Each file is exactly the size of the standard llama.cpp file of the same name. Tuned for Ideogram 4 only. For Qwen-Image-2.1 use DHQ files are quantized for use as a diffusion model's text encoder: each tensor's precision is chosen by how much its quantization error changes what the target DiT reads (measured through the DiT's own text-input stage), not by… Open weights apache-2.0 [View model](https://savrn.com/models/ideogram-4-text-encoder-heretic-dhq-gguf) Model ### [Illustrious](https://savrn.com/models/illustrious) [Yoai Kimia](https://savrn.com/model-publishers/yoaikimia) Access requested at publisher [View model](https://savrn.com/models/illustrious) Model ### [ImLkTKkNFx4CGRFv](https://savrn.com/models/imlktkknfx4cgrfv) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/imlktkknfx4cgrfv) Model · Text to image ### [Image-2.1-cmf](https://savrn.com/models/image-2-1-cmf) [Oleg Kirichenko](https://savrn.com/model-publishers/infosave) Qwen-Image-2.1 packaged as a single CMF file: the 7B image transformer, the Qwen3-VL-8B text encoder with its vision tower, and the RGBA VAE together, with the generation recipe stored inside. It runs with cortiq, a Rust engine with no Python: Apple silicon through Metal, NVIDIA GPUs through Vulkan, and a CPU fallback everywhere. Text-to-image, native transparency (RGBA), and generation from condition images. Built with Qwen. No flags needed: the file carries the recipe (1024×1024, 40 steps, no CFG). Set XDGRUNTIMEDIR=/tmp on a headless Linux box. Transparent images — ask for them in the prompt, PNG keeps the alpha channel: From condition images (repeat --image for several): sha256… Open weights other cortiq [View model](https://savrn.com/models/image-2-1-cmf) ## 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.