# Models, Page 383 of 477 Source: https://savrn.com/models/page/383 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. 11,442Models 3,247Datasets 285Papers 6,976Publishers 12,091Sourced relationships Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,413](https://savrn.com/models?task=Text+generation) - [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction341](https://savrn.com/models?task=Feature+extraction) - [Robotics338](https://savrn.com/models?task=Robotics) - [Text classification303](https://savrn.com/models?task=Text+classification) - [Text to image209](https://savrn.com/models?task=Text+to+image) - [Speech recognition171](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification137](https://savrn.com/models?task=Image+classification) - [Text to speech132](https://savrn.com/models?task=Text+to+speech) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification97](https://savrn.com/models?task=Token+classification) - [Reinforcement learning93](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation88](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) - [Translation78](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 classification59](https://savrn.com/models?task=Video+classification) - [Image to text58](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,777](https://savrn.com/models?library=transformers) - [peft345](https://savrn.com/models?library=peft) - [diffusers187](https://savrn.com/models?library=diffusers) - [pytorch178](https://savrn.com/models?library=pytorch) - [gguf167](https://savrn.com/models?library=gguf) - [lerobot139](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [timm49](https://savrn.com/models?library=timm) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [llama.cpp44](https://savrn.com/models?library=llama.cpp) - [vllm38](https://savrn.com/models?library=vllm) - [onnx36](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) - [exllamav316](https://savrn.com/models?library=exllamav3) - [openpi16](https://savrn.com/models?library=openpi) - [litert15](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,473](https://savrn.com/models?modality=Other) - [Text2,612](https://savrn.com/models?modality=Text) - [Image and text570](https://savrn.com/models?modality=Image+and+text) - [Image565](https://savrn.com/models?modality=Image) - [Audio478](https://savrn.com/models?modality=Audio) - [Control431](https://savrn.com/models?modality=Control) - [Video114](https://savrn.com/models?modality=Video) - [Multimodal79](https://savrn.com/models?modality=Multimodal) - [Time series64](https://savrn.com/models?modality=Time+series) - [Tabular56](https://savrn.com/models?modality=Tabular) License - [apache-2.03,105](https://savrn.com/models?license=apache-2.0) - [mit1,246](https://savrn.com/models?license=mit) - [other703](https://savrn.com/models?license=other) - [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma62](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](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) - [llama3.128](https://savrn.com/models?license=llama3.1) - [unknown27](https://savrn.com/models?license=unknown) - [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0) - [gpl-3.017](https://savrn.com/models?license=gpl-3.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [cc-by-nc-nd-4.013](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [creativeml-openrail-m11](https://savrn.com/models?license=creativeml-openrail-m) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama27](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 weights11,087](https://savrn.com/models?access=open_access) - [Access requested at publisher355](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai94](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [Jenny Wilson81](https://savrn.com/models?publisher=jenny08311) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Jessen Pu69](https://savrn.com/models?publisher=putty77) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan63](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [Yurun Yuan48](https://savrn.com/models?publisher=yuruny) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [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) 11,442 models, sorted by most downloaded. Model · Text generation ### [nor-latn-100mb-after-ppt-mp-struct-100mb-ckpt500_seed3407](https://savrn.com/models/nor-latn-100mb-after-ppt-mp-struct-100mb-ckpt500-seed3407) [Francesca Padovani](https://savrn.com/model-publishers/francesca9805) This model is a fine-tuned version of francesca9805/nor-latn-100mb-ppt-mp-struct-100mbseed3407. It has been trained using TRL. This model was trained with SFT. Open weights 125M parameters transformers [View model](https://savrn.com/models/nor-latn-100mb-after-ppt-mp-struct-100mb-ckpt500-seed3407) Model · Text generation ### [northstar-financial-authority-guardrail-lora-neutral](https://savrn.com/models/northstar-financial-authority-guardrail-lora-neutral) [Joshua Davis](https://savrn.com/model-publishers/drdavis) 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). - PEFT 0.21.1 Open weights peft [View model](https://savrn.com/models/northstar-financial-authority-guardrail-lora-neutral) Model · Image and text to text ### [not-a-GLM-5.3-Flash-backup](https://savrn.com/models/not-a-glm-5-3-flash-backup) [Michael Fielding](https://savrn.com/model-publishers/mfielding92) Join our WeChat or Discord community. Check out the GLM-5.3-Flash blog and GLM-5 Technical report. Use GLM-5.3-Flash API services on Z.ai API Platform. We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear… Open weights mit 321.3B parameters 1,048,576 tokens transformers [View model](https://savrn.com/models/not-a-glm-5-3-flash-backup) Model · Text generation ### [not-a-GLM-5.3-backup](https://savrn.com/models/not-a-glm-5-3-backup) [Michael Fielding](https://savrn.com/model-publishers/mfielding92) GLM-5.3 uses the same base model as GLM-5.2 — every gain comes from post-training. Compared with GLM-5.2, it is much better at complex coding and long-horizon tasks: GLM-5.3 supports deployment with the following frameworks. Feel free to try them out: - SGLang — see cookbook - vLLM — see recipes - TokenSpeed — see here - Transformers — see transformers docs - KTransformers — see tutorial - Unsloth — see guide - For deployment on the Ascend NPU platform, inference frameworks such as vLLM-Ascend, xLLM and SGLang are supported — see here. - GLM-5.3 supports controlling the thinking budget through the reasoningeffort parameter, which accepts three levels: low, high, and max. It defaults to max… Open weights other 753.3B parameters 1,048,576 tokens transformers [View model](https://savrn.com/models/not-a-glm-5-3-backup) Model ### [notes-grounded-language](https://savrn.com/models/notes-grounded-language) [Magdalena Szymanski](https://savrn.com/model-publishers/pozszymanski) This is an exploratory note for Grounded Language. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as RefCOCO, Flickr30k, and Visual Genome - reproducibility checks, failure modes, and open questions - topic-relevant references Start with papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs. The… Open weights cc-by-4.0 33,088 parameters 256 tokens [View model](https://savrn.com/models/notes-grounded-language) Model ### [notes-lightweight-multimodal](https://savrn.com/models/notes-lightweight-multimodal) [V. Singh](https://savrn.com/model-publishers/anvu4352) Reading notes and an experiment sketch for Lightweight Multimodal. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims. - 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 review.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 mit 49,600 parameters 256 tokens [View model](https://savrn.com/models/notes-lightweight-multimodal) Model ### [notes-multimodal-generation](https://savrn.com/models/notes-multimodal-generation) [Kevin Sitorus](https://savrn.com/model-publishers/klsitorus) This repository contains a working research note about Multimodal Generation. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - 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… Open weights cc-by-4.0 16,576 parameters 128 tokens [View model](https://savrn.com/models/notes-multimodal-generation) Model ### [notes-zero-shot-transfer](https://savrn.com/models/notes-zero-shot-transfer) [Piotr-Kowalski](https://savrn.com/model-publishers/pkowalski2) Reading notes and an experiment sketch for Zero Shot Transfer. The repository emphasizes what still needs to be tested instead of manufacturing scores or release claims. - 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 analysis.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 33,088 parameters 512 tokens [View model](https://savrn.com/models/notes-zero-shot-transfer) Model ### [npov_PERL_organic_gemma-4-E4B-it_S130104_epo0_5_lr1_1e-05_beta0_078_r8_2610062017](https://savrn.com/models/npov-perl-organic-gemma-4-e4b-it-s130104-epo0-5-lr1-1e-05-beta0-078-r8-2610062017) [Leonardo Martins bianco](https://savrn.com/model-publishers/leobianco) This model is a fine-tuned version of google/gemma-4-E4B-it. It has been trained using TRL. This model was trained with RLOO, a method introduced in Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs. Open weights transformers [View model](https://savrn.com/models/npov-perl-organic-gemma-4-e4b-it-s130104-epo0-5-lr1-1e-05-beta0-078-r8-2610062017) Model ### [npt-d2l-study-public](https://savrn.com/models/npt-d2l-study-public) [Mohamed Maher](https://savrn.com/model-publishers/mohamedmaheros) Open weights [View model](https://savrn.com/models/npt-d2l-study-public) Model ### [npt-study-mode](https://savrn.com/models/npt-study-mode) [Mohamed Maher](https://savrn.com/model-publishers/mohamedmaheros) Artifacts of the study-mode experiments (study-LoRA adapters, predecoded study transcripts, memory checkpoints, evaluation results). Code: branch exp/study-mode of the NPT-2 repository. Open weights [View model](https://savrn.com/models/npt-study-mode) Model ### [numbat](https://savrn.com/models/numbat) [Burner22](https://savrn.com/model-publishers/burner22) Open weights [View model](https://savrn.com/models/numbat) Model ### [nunchaku-qwen-image-2.1](https://savrn.com/models/nunchaku-qwen-image-2-1) [Oleg Kibirev](https://savrn.com/model-publishers/catplusplus) Open weights apache-2.0 [View model](https://savrn.com/models/nunchaku-qwen-image-2-1) Model ### [nusalm-300m](https://savrn.com/models/nusalm-300m) [Amalan Fadil Gaib](https://savrn.com/model-publishers/itsdevice) Open weights [View model](https://savrn.com/models/nusalm-300m) Model ### [nv-reason-ct-onnx](https://savrn.com/models/nv-reason-ct-onnx) [Jarrel Seah](https://savrn.com/model-publishers/jarrelscy) ONNX conversion of nvidia/NV-Reason-CT for ONNX Runtime Web with WebGPU. These files are used by https://jarrelscy.github.io/nv-reason-ct-web/ (source: https://github.com/jarrelscy/nv-reason-ct-web), which runs the model entirely in the browser. Each.json manifest lists the ONNX graph, its external data chunks (split to fit browser buffer limits) and the decoder state names. The decoders use fp32 activations and MatMulNBits weights, and the lmhead is pruned to the last token. Token embeddings are tied to lmhead, so they are not stored separately; the web app dequantises embedding rows from the lmhead weights (described under embed in decoder.json). Image tokens use 3D MRoPE positions as in… Open weights other onnx [View model](https://savrn.com/models/nv-reason-ct-onnx) Model ### [nvdla-dummy-test](https://savrn.com/models/nvdla-dummy-test) [Kalimullakhan Ashvak](https://savrn.com/model-publishers/ashtasy) Open weights [View model](https://savrn.com/models/nvdla-dummy-test) Model ### [nvidia_vfx-files](https://savrn.com/models/nvidia-vfx-files) [Writer Blue](https://savrn.com/model-publishers/eboramacro) Open weights [View model](https://savrn.com/models/nvidia-vfx-files) Model ### [nyanlin-v21-resume](https://savrn.com/models/nyanlin-v21-resume) [Meonnmi Ops](https://savrn.com/model-publishers/meonnmi-ops) Open weights [View model](https://savrn.com/models/nyanlin-v21-resume) Model ### [nyay-mitra](https://savrn.com/models/nyay-mitra) [Anamika Mukherjee](https://savrn.com/model-publishers/anamika-mukherjee) 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 transformers [View model](https://savrn.com/models/nyay-mitra) Model · Other ### [nys-sft-public](https://savrn.com/models/nys-sft-public) [Dakuwon Moody](https://savrn.com/model-publishers/ynsscarsaiyan) NYS is not a transformer. It is a coupled-oscillator organism: phases \(\thetai\), amplitudes, natural frequencies \(\omegai\), and a sparse coupling graph \(K\). Language, compiler bits, and speech-frame IDs live on one graph (\(N = 5{,}000{,}000\) nodes, \(k = 512\) edges per node). The native graph trains by Equilibrium Propagation (free RK4, then same-moment free/nudged branches, then a contrastive \(K\) update), without backprop or a cross-entropy objective. A separate sparse context head now provides retrieved language inputs. The GPU Qwen teacher uses PyTorch/Transformers; that does not make the native graph a transformer. Dakuwon Moody (YNSScarSaiyan) · Saiyan Corp The… Open weights mit nys-thermodynamic [View model](https://savrn.com/models/nys-sft-public) Model ### [oac-ops-health-v2](https://savrn.com/models/oac-ops-health-v2) [SZL Holdings](https://savrn.com/model-publishers/szlholdings) This package is a deterministic Python standard-library kernel and a JSON table of coefficients fitted on generated transport counters. It is not a transformer checkpoint or adapter; it is not a clinical model or result service. It accepts eight bounded operational fields and proposes ALERT, NOALERT, or ABSTAIN for operator attention. Every authority flag is false: it cannot acknowledge a transport message, control a device, interpret a result, release a result, or make a care decision. Do not submit patient information, laboratory results, specimens, orders, or device identifiers. The four executable/data files in this repository are byte copies of ops-health/v2/ops-health/ at the… Open weights apache-2.0 [View model](https://savrn.com/models/oac-ops-health-v2) Model ### [oak-battle-network](https://savrn.com/models/oak-battle-network) [Open Cover](https://savrn.com/model-publishers/opencover) Open weights [View model](https://savrn.com/models/oak-battle-network) Model ### [oat-rfsq-pair-sorting-policy-tok800k-step24000](https://savrn.com/models/oat-rfsq-pair-sorting-policy-tok800k-step24000) [Yangzhixing](https://savrn.com/model-publishers/yangzhixing) Open weights [View model](https://savrn.com/models/oat-rfsq-pair-sorting-policy-tok800k-step24000) Model ### [objectsDetection](https://savrn.com/models/objectsdetection) [Fu Chan](https://savrn.com/model-publishers/fu10101) Open weights mit [View model](https://savrn.com/models/objectsdetection) ## 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.