# Models, Page 242 of 512 Source: https://savrn.com/models/page/242 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 ### [WcvOVkXkK56zeNJw](https://savrn.com/models/wcvovkxkk56zenjw) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/wcvovkxkk56zenjw) Model · Feature extraction ### [WeMM-Embedding-2B-GGUF](https://savrn.com/models/wemm-embedding-2b-gguf) [Lain Soykaf](https://savrn.com/model-publishers/lainsoykaf) Unofficial GGUF conversion of tencent/WeMM-Embedding-2B for llama.cpp. It is the default embedding model of imgsearch, a local-first image search app. Not affiliated with or endorsed by Tencent. All credit for the model goes to the WeChat Vision Team; it is licensed under Apache 2.0. Both files are needed for image input. Output: 2048 dimensions, L2-normalize; Matryoshka truncation (re-normalize) to 1024/512/256 works well. The prompt must match the reference processor output exactly. No system turn, no instruction; queries and documents are encoded the same way. Take the last-token hidden state (pooling none, last position, or --pooling last): is the llama.cpp mtmd media marker; mtmd adds… Open weights apache-2.0 gguf [View model](https://savrn.com/models/wemm-embedding-2b-gguf) Model ### [WeVisDoc-2B](https://savrn.com/models/wevisdoc-2b) [Tencent](https://savrn.com/model-publishers/tencent) WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables. WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings. The following tables include end-to-end document parsing specialists only. WeVisDoc results are means over three inference runs. Avg₃ is the mean of the three PureDocBench track-level Overall scores. marks baseline results obtained with our evaluation pipeline; unmarked baseline… Open weights apache-2.0 2.4B parameters 262,144 tokens [View model](https://savrn.com/models/wevisdoc-2b) Model ### [WeVisDoc-4B](https://savrn.com/models/wevisdoc-4b) [Tencent](https://savrn.com/model-publishers/tencent) WeVisDoc is an end-to-end document parser for page images. Fine-tuned from Qwen3-VL-2B-Instruct and Qwen3-VL-4B-Instruct, it turns a page into structured Markdown, with LaTeX formulas and HTML tables. WeVisDoc-4B achieves an Overall score of 95.38 on OmniDocBench v1.6 and a mean Overall score of 75.54 across the three PureDocBench tracks, ranking first among the compared end-to-end parsers in all four settings. The following tables include end-to-end document parsing specialists only. WeVisDoc results are means over three inference runs. Avg₃ is the mean of the three PureDocBench track-level Overall scores. marks baseline results obtained with our evaluation pipeline; unmarked baseline… Open weights apache-2.0 4.4B parameters 262,144 tokens [View model](https://savrn.com/models/wevisdoc-4b) Model ### [WeakMCN-SAM-Vit-Base-RefCOCO](https://savrn.com/models/weakmcn-sam-vit-base-refcoco) [Silin Cheng](https://savrn.com/model-publishers/slcheng) Open weights apache-2.0 [View model](https://savrn.com/models/weakmcn-sam-vit-base-refcoco) Model ### [WeakMCN-SAM-Vit-Base-RefCOCO-plus](https://savrn.com/models/weakmcn-sam-vit-base-refcoco-plus) [Silin Cheng](https://savrn.com/model-publishers/slcheng) Open weights apache-2.0 [View model](https://savrn.com/models/weakmcn-sam-vit-base-refcoco-plus) Model ### [WeakMCN-SAM-Vit-Base-RefCOCOg](https://savrn.com/models/weakmcn-sam-vit-base-refcocog) [Silin Cheng](https://savrn.com/model-publishers/slcheng) Open weights apache-2.0 [View model](https://savrn.com/models/weakmcn-sam-vit-base-refcocog) Model ### [WeakMCN-SAM-Vit-Tiny-RefCOCO](https://savrn.com/models/weakmcn-sam-vit-tiny-refcoco) [Silin Cheng](https://savrn.com/model-publishers/slcheng) Open weights apache-2.0 [View model](https://savrn.com/models/weakmcn-sam-vit-tiny-refcoco) Model ### [WeakMCN-SAM-Vit-Tiny-RefCOCO-plus](https://savrn.com/models/weakmcn-sam-vit-tiny-refcoco-plus) [Silin Cheng](https://savrn.com/model-publishers/slcheng) Open weights apache-2.0 [View model](https://savrn.com/models/weakmcn-sam-vit-tiny-refcoco-plus) Model ### [WeakMCN-SAM-Vit-Tiny-RefCOCOg](https://savrn.com/models/weakmcn-sam-vit-tiny-refcocog) [Silin Cheng](https://savrn.com/model-publishers/slcheng) Open weights apache-2.0 [View model](https://savrn.com/models/weakmcn-sam-vit-tiny-refcocog) Model ### [WebShop-Olmo3-7B-Adaptive-Midpoint-s1000](https://savrn.com/models/webshop-olmo3-7b-adaptive-midpoint-s1000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-olmo3-7b-adaptive-midpoint-s1000) Model ### [WebShop-Olmo3-7B-DAPO-nostd](https://savrn.com/models/webshop-olmo3-7b-dapo-nostd) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-olmo3-7b-dapo-nostd) Model ### [WebShop-Olmo3-7B-GRPO-nostd-s2000](https://savrn.com/models/webshop-olmo3-7b-grpo-nostd-s2000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-olmo3-7b-grpo-nostd-s2000) Model ### [WebShop-Olmo3-7B-GRPO-s1000](https://savrn.com/models/webshop-olmo3-7b-grpo-s1000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-olmo3-7b-grpo-s1000) Model ### [WebShop-Olmo3-7B-GRPO-s2000](https://savrn.com/models/webshop-olmo3-7b-grpo-s2000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-olmo3-7b-grpo-s2000) Model ### [WebShop-Olmo3-7B-GiGPO-s2000](https://savrn.com/models/webshop-olmo3-7b-gigpo-s2000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-olmo3-7b-gigpo-s2000) Model ### [WebShop-Olmo3-7B-SEED-nostd-s1000](https://savrn.com/models/webshop-olmo3-7b-seed-nostd-s1000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-olmo3-7b-seed-nostd-s1000) Model · Text generation ### [WebShop-Olmo3-7B-SEED-s1000](https://savrn.com/models/webshop-olmo3-7b-seed-s1000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) A allenai/Olmo-3-7B-Instruct policy trained as a multi-turn search agent (Search-R1 style) with Process-GRPO: a process reward model (Olmo-3-7B-Think verifier) scores each turn, with per-(group, turn-position) advantage normalization and verifier prompts that include the retrieved tool responses and the gold answer. The model at the repository root is the final policy (training step 360). Intermediate checkpoints are provided under step / subfolders. - step20/ — intermediate checkpoint at training step 20 - step40/ — intermediate checkpoint at training step 40 - step60/ — intermediate checkpoint at training step 60 - step80/ — intermediate checkpoint at training step 80 - step100/… Open weights apache-2.0 transformers [View model](https://savrn.com/models/webshop-olmo3-7b-seed-s1000) Model ### [WebShop-Qwen3-8B-DAPO-nostd](https://savrn.com/models/webshop-qwen3-8b-dapo-nostd) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-qwen3-8b-dapo-nostd) Model ### [WebShop-Qwen3-8B-GRPO-nostd](https://savrn.com/models/webshop-qwen3-8b-grpo-nostd) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-qwen3-8b-grpo-nostd) Model ### [WebShop-Qwen3-8B-GRPO-nostd-s1000](https://savrn.com/models/webshop-qwen3-8b-grpo-nostd-s1000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-qwen3-8b-grpo-nostd-s1000) Model ### [WebShop-Qwen3-8B-GRPO-s1000](https://savrn.com/models/webshop-qwen3-8b-grpo-s1000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-qwen3-8b-grpo-s1000) Model ### [WebShop-Qwen3-8B-GRPO-s2000](https://savrn.com/models/webshop-qwen3-8b-grpo-s2000) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-qwen3-8b-grpo-s2000) Model ### [WebShop-Qwen3-8B-RAGEN2-nostd](https://savrn.com/models/webshop-qwen3-8b-ragen2-nostd) [Cyrus Kwan](https://savrn.com/model-publishers/wckwan) Open weights [View model](https://savrn.com/models/webshop-qwen3-8b-ragen2-nostd) ## 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.