# Models, Page 338 of 489 Source: https://savrn.com/models/page/338 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. 11,734Models 3,323Datasets 288Papers 7,150Publishers 12,282Sourced relationships Updated 2026-10-20 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,441](https://savrn.com/models?task=Text+generation) - [Image and text to text530](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction351](https://savrn.com/models?task=Feature+extraction) - [Robotics345](https://savrn.com/models?task=Robotics) - [Text classification310](https://savrn.com/models?task=Text+classification) - [Text to image222](https://savrn.com/models?task=Text+to+image) - [Speech recognition175](https://savrn.com/models?task=Speech+recognition) - [Audio classification158](https://savrn.com/models?task=Audio+classification) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Text to speech137](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 learning95](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation89](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) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,829](https://savrn.com/models?library=transformers) - [peft355](https://savrn.com/models?library=peft) - [diffusers196](https://savrn.com/models?library=diffusers) - [pytorch184](https://savrn.com/models?library=pytorch) - [gguf172](https://savrn.com/models?library=gguf) - [mlx145](https://savrn.com/models?library=mlx) - [lerobot141](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [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) - [onnx39](https://savrn.com/models?library=onnx) - [vllm38](https://savrn.com/models?library=vllm) - [diffusion-single-file28](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) - [openvino20](https://savrn.com/models?library=openvino) - [exllamav318](https://savrn.com/models?library=exllamav3) - [coreml17](https://savrn.com/models?library=coreml) - [litert16](https://savrn.com/models?library=litert) - [openpi16](https://savrn.com/models?library=openpi) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,664](https://savrn.com/models?modality=Other) - [Text2,658](https://savrn.com/models?modality=Text) - [Image and text589](https://savrn.com/models?modality=Image+and+text) - [Image582](https://savrn.com/models?modality=Image) - [Audio488](https://savrn.com/models?modality=Audio) - [Control440](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-2.03,175](https://savrn.com/models?license=apache-2.0) - [mit1,275](https://savrn.com/models?license=mit) - [other723](https://savrn.com/models?license=other) - [cc-by-nc-4.0200](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0153](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause107](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.050](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) - [unknown29](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-3.017](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-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 weights11,363](https://savrn.com/models?access=open_access) - [Access requested at publisher371](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher304](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan101](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai100](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 Pu71](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) - [Yurun Yuan55](https://savrn.com/models?publisher=yuruny) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [BAAR46](https://savrn.com/models?publisher=aixk) - [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=yyuan244) [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) 11,734 models, sorted by most downloaded. Model ### [gm0](https://savrn.com/models/gm0) [Guardiola](https://savrn.com/model-publishers/ml-guardiola) Open weights [View model](https://savrn.com/models/gm0) Model ### [gm1](https://savrn.com/models/gm1) [Guardiola](https://savrn.com/model-publishers/ml-guardiola) Open weights [View model](https://savrn.com/models/gm1) Model ### [gm2](https://savrn.com/models/gm2) [Guardiola](https://savrn.com/model-publishers/ml-guardiola) Open weights [View model](https://savrn.com/models/gm2) Model ### [gmp-4b-s70pct-jump-noopd-20260923-202338](https://savrn.com/models/gmp-4b-s70pct-jump-noopd-20260923-202338) [doyoon Kim](https://savrn.com/model-publishers/cosmos1030) Open weights 4B parameters 40,960 tokens [View model](https://savrn.com/models/gmp-4b-s70pct-jump-noopd-20260923-202338) Model ### [gmp-kd3e-1-1.7b-n24-s50pct-lr1e-4_20261019_120311](https://savrn.com/models/gmp-kd3e-1-1-7b-n24-s50pct-lr1e-4-20261019-120311) [doyoon Kim](https://savrn.com/model-publishers/cosmos1030) Open weights 1.7B parameters [View model](https://savrn.com/models/gmp-kd3e-1-1-7b-n24-s50pct-lr1e-4-20261019-120311) Model ### [gmp-kd3e-1-1.7b-n24-s50pct-lr1e-4_20261019_122112](https://savrn.com/models/gmp-kd3e-1-1-7b-n24-s50pct-lr1e-4-20261019-122112) [doyoon Kim](https://savrn.com/model-publishers/cosmos1030) Open weights 1.7B parameters [View model](https://savrn.com/models/gmp-kd3e-1-1-7b-n24-s50pct-lr1e-4-20261019-122112) Model ### [gmp-kd3e-1-1.7b-s0pct-lr1e-4_20261006_122929](https://savrn.com/models/gmp-kd3e-1-1-7b-s0pct-lr1e-4-20261006-122929) [doyoon Kim](https://savrn.com/model-publishers/cosmos1030) Open weights 1.7B parameters 40,960 tokens [View model](https://savrn.com/models/gmp-kd3e-1-1-7b-s0pct-lr1e-4-20261006-122929) Model ### [gmp-kd3e-1-4b-s60pct-lr5e-5_20261011_115201](https://savrn.com/models/gmp-kd3e-1-4b-s60pct-lr5e-5-20261011-115201) [doyoon Kim](https://savrn.com/model-publishers/cosmos1030) Open weights 4B parameters 40,960 tokens [View model](https://savrn.com/models/gmp-kd3e-1-4b-s60pct-lr5e-5-20261011-115201) Model ### [gmp-kd3e-1-s60pct-lr5e-5_20260901_071402](https://savrn.com/models/gmp-kd3e-1-s60pct-lr5e-5-20260901-071402) [doyoon Kim](https://savrn.com/model-publishers/cosmos1030) Open weights 1.7B parameters 40,960 tokens [View model](https://savrn.com/models/gmp-kd3e-1-s60pct-lr5e-5-20260901-071402) Model · Text generation ### [gms-env-20261004-010445](https://savrn.com/models/gms-env-20261004-010445) [Gold Sky](https://savrn.com/model-publishers/gold-sky) 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 0.19.1 Access requested at publisher peft [View model](https://savrn.com/models/gms-env-20261004-010445) Model · Text generation ### [gms-env-20261004-012608](https://savrn.com/models/gms-env-20261004-012608) [Gold Sky](https://savrn.com/model-publishers/gold-sky) 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 0.19.1 Access requested at publisher peft [View model](https://savrn.com/models/gms-env-20261004-012608) Model · Token classification ### [goby-4-ie-vi](https://savrn.com/models/goby-4-ie-vi) [Belumind](https://savrn.com/model-publishers/belumind) Open weights apache-2.0 467M parameters gliner [View model](https://savrn.com/models/goby-4-ie-vi) Model · Token classification ### [goby-7-ie-vi](https://savrn.com/models/goby-7-ie-vi) [Belumind](https://savrn.com/model-publishers/belumind) Open weights apache-2.0 467M parameters gliner [View model](https://savrn.com/models/goby-7-ie-vi) Model · Token classification ### [goby-9-ie-vi](https://savrn.com/models/goby-9-ie-vi) [Belumind](https://savrn.com/model-publishers/belumind) Joint entity + relation extraction for Vietnamese. The best-calibrated model in the goby family: higher precision at the same recall than its siblings. On clean text at each model own best threshold it ties goby-4 rather than beating it (see Measurement correction); on degraded text it is the worst of the family. Pick it for well-formed prose where you care about precision. Fine-tuned from knowledgator/gliner-relex-large-v1.0. Released by Belumind. Apache-2.0. Try it in the browser - no install, runs on ZeroGPU. goby-9 is not robust to degraded text - see Known limitations. That is not a small caveat, read it before choosing. Same training data as goby-4. The only difference is the loss… Open weights apache-2.0 467M parameters gliner [View model](https://savrn.com/models/goby-9-ie-vi) Model ### [god-03d1f6a1-imglf-7930776-10170208](https://savrn.com/models/god-03d1f6a1-imglf-7930776-10170208) [Seintartop](https://savrn.com/model-publishers/seintartop) Open weights [View model](https://savrn.com/models/god-03d1f6a1-imglf-7930776-10170208) Model ### [god-14e9feed-imgm2-f553aa0-10170208](https://savrn.com/models/god-14e9feed-imgm2-f553aa0-10170208) [Seintartop](https://savrn.com/model-publishers/seintartop) Open weights [View model](https://savrn.com/models/god-14e9feed-imgm2-f553aa0-10170208) Model ### [god-14e9feed-imgm3-f553aa0-10170208](https://savrn.com/models/god-14e9feed-imgm3-f553aa0-10170208) [Seintartop](https://savrn.com/model-publishers/seintartop) Open weights [View model](https://savrn.com/models/god-14e9feed-imgm3-f553aa0-10170208) Model ### [god-36b4f021-imgc-f553aa0-10170300](https://savrn.com/models/god-36b4f021-imgc-f553aa0-10170300) [Seintartop](https://savrn.com/model-publishers/seintartop) Open weights [View model](https://savrn.com/models/god-36b4f021-imgc-f553aa0-10170300) Model ### [god-36b4f021-imglf-7930776-10170208](https://savrn.com/models/god-36b4f021-imglf-7930776-10170208) [Seintartop](https://savrn.com/model-publishers/seintartop) Open weights [View model](https://savrn.com/models/god-36b4f021-imglf-7930776-10170208) Model ### [god-5db5591e-imglf-7930776-10170208](https://savrn.com/models/god-5db5591e-imglf-7930776-10170208) [Seintartop](https://savrn.com/model-publishers/seintartop) Open weights [View model](https://savrn.com/models/god-5db5591e-imglf-7930776-10170208) Model ### [god-5db5591e-imgm2-f553aa0-10170208](https://savrn.com/models/god-5db5591e-imgm2-f553aa0-10170208) [Seintartop](https://savrn.com/model-publishers/seintartop) Open weights [View model](https://savrn.com/models/god-5db5591e-imgm2-f553aa0-10170208) Model ### [god-5db5591e-imgm3-f553aa0-10170208](https://savrn.com/models/god-5db5591e-imgm3-f553aa0-10170208) [Seintartop](https://savrn.com/model-publishers/seintartop) Open weights [View model](https://savrn.com/models/god-5db5591e-imgm3-f553aa0-10170208) Model ### [god-7b5416a9-yxzimg-8e95074-10040243](https://savrn.com/models/god-7b5416a9-yxzimg-8e95074-10040243) [Bima Bagaskhoro](https://savrn.com/model-publishers/bimabk) Open weights [View model](https://savrn.com/models/god-7b5416a9-yxzimg-8e95074-10040243) Model ### [god-7b5416a9-yxzimg-8e95074-10040243-alt-member0](https://savrn.com/models/god-7b5416a9-yxzimg-8e95074-10040243-alt-member0) [Bima Bagaskhoro](https://savrn.com/model-publishers/bimabk) Open weights [View model](https://savrn.com/models/god-7b5416a9-yxzimg-8e95074-10040243-alt-member0) ## 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.