# Models, Page 445 of 464 Source: https://savrn.com/models/page/445 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) - [Sentence similarity102](https://savrn.com/models?task=Sentence+similarity) - [Token classification96](https://savrn.com/models?task=Token+classification) - [Zero-shot classification92](https://savrn.com/models?task=Zero-shot+classification) - [Reinforcement learning91](https://savrn.com/models?task=Reinforcement+learning) - [Image segmentation87](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) - [Translation77](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask67](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 text57](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [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 - [apache-2.03,031](https://savrn.com/models?license=apache-2.0) - [mit1,221](https://savrn.com/models?license=mit) - [other681](https://savrn.com/models?license=other) - [cc-by-nc-4.0184](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0147](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma59](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.234](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.127](https://savrn.com/models?license=llama3.1) - [unknown26](https://savrn.com/models?license=unknown) - [cc-by-sa-4.018](https://savrn.com/models?license=cc-by-sa-4.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [gpl-3.016](https://savrn.com/models?license=gpl-3.0) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [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 ### [tournament-exp-s1-ce763cd5-8bfc-4fb0-be95-9935cb61ffd2-5Exp63a8af7827876a2c](https://savrn.com/models/tournament-exp-s1-ce763cd5-8bfc-4fb0-be95-9935cb61ffd2-5exp63a8af7827876a2c) [Christopher Subia-Waud](https://savrn.com/model-publishers/cwaud) Open weights 3.1B parameters 32,768 tokens [View model](https://savrn.com/models/tournament-exp-s1-ce763cd5-8bfc-4fb0-be95-9935cb61ffd2-5exp63a8af7827876a2c) Model ### [tournament-exp-s1-d99ce208-2ff2-47ec-8899-650972d0ae33-5Exp1642a3e76d0b5b39](https://savrn.com/models/tournament-exp-s1-d99ce208-2ff2-47ec-8899-650972d0ae33-5exp1642a3e76d0b5b39) [Christopher Subia-Waud](https://savrn.com/model-publishers/cwaud) Open weights 1.3B parameters 2,048 tokens [View model](https://savrn.com/models/tournament-exp-s1-d99ce208-2ff2-47ec-8899-650972d0ae33-5exp1642a3e76d0b5b39) Model ### [tournament-exp-s1-ef9824ae-b617-45c7-a5ea-5b1ed01688b4-5Exp4982e7f6d52475a1](https://savrn.com/models/tournament-exp-s1-ef9824ae-b617-45c7-a5ea-5b1ed01688b4-5exp4982e7f6d52475a1) [Christopher Subia-Waud](https://savrn.com/model-publishers/cwaud) Open weights 362M parameters 8,192 tokens [View model](https://savrn.com/models/tournament-exp-s1-ef9824ae-b617-45c7-a5ea-5b1ed01688b4-5exp4982e7f6d52475a1) Model ### [tournament-exp-s1-ef9824ae-b617-45c7-a5ea-5b1ed01688b4-5Exp4c955f1dce21e560](https://savrn.com/models/tournament-exp-s1-ef9824ae-b617-45c7-a5ea-5b1ed01688b4-5exp4c955f1dce21e560) [Christopher Subia-Waud](https://savrn.com/model-publishers/cwaud) Open weights 362M parameters 8,192 tokens [View model](https://savrn.com/models/tournament-exp-s1-ef9824ae-b617-45c7-a5ea-5b1ed01688b4-5exp4c955f1dce21e560) Model ### [tournament-exp-s1-f377d9ff-4ef8-5b49-99f0-bb74f5366ae1-5Exp08c2bd22d1d66ddf](https://savrn.com/models/tournament-exp-s1-f377d9ff-4ef8-5b49-99f0-bb74f5366ae1-5exp08c2bd22d1d66ddf) [Christopher Subia-Waud](https://savrn.com/model-publishers/cwaud) Open weights 3.4B parameters 131,072 tokens [View model](https://savrn.com/models/tournament-exp-s1-f377d9ff-4ef8-5b49-99f0-bb74f5366ae1-5exp08c2bd22d1d66ddf) Model ### [tournament-exp-s1-f4920bf7-9630-4d93-9ece-0b453baa5da7-5Exp189e73453943f255](https://savrn.com/models/tournament-exp-s1-f4920bf7-9630-4d93-9ece-0b453baa5da7-5exp189e73453943f255) [Christopher Subia-Waud](https://savrn.com/model-publishers/cwaud) Open weights 162M parameters 2,048 tokens [View model](https://savrn.com/models/tournament-exp-s1-f4920bf7-9630-4d93-9ece-0b453baa5da7-5exp189e73453943f255) Model ### [tournament-exp-s1-f644ed68-ef1b-4e2b-9709-e301680e4958-5Exp383c5a08e763f167](https://savrn.com/models/tournament-exp-s1-f644ed68-ef1b-4e2b-9709-e301680e4958-5exp383c5a08e763f167) [Christopher Subia-Waud](https://savrn.com/model-publishers/cwaud) Open weights 596M parameters 32,768 tokens [View model](https://savrn.com/models/tournament-exp-s1-f644ed68-ef1b-4e2b-9709-e301680e4958-5exp383c5a08e763f167) Model ### [tournament-exp-s1-f7a66f6a-8bc3-5849-b296-238cde52bc07-5Exp08c2bd22d1d66ddf](https://savrn.com/models/tournament-exp-s1-f7a66f6a-8bc3-5849-b296-238cde52bc07-5exp08c2bd22d1d66ddf) [Christopher Subia-Waud](https://savrn.com/model-publishers/cwaud) Open weights 1.6B parameters 131,072 tokens [View model](https://savrn.com/models/tournament-exp-s1-f7a66f6a-8bc3-5849-b296-238cde52bc07-5exp08c2bd22d1d66ddf) Model · Text generation ### [tournament-tourn_14bae42888dfe86e_20261012-15ae6d37-2e08-4bf6-8565-6110527f9d79-5HBgDWKx](https://savrn.com/models/tournament-tourn-14bae42888dfe86e-20261012-15ae6d37-2e08-4bf6-8565-6110527f9d79-5hbgdwkx) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) 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 0.18.1 Open weights peft [View model](https://savrn.com/models/tournament-tourn-14bae42888dfe86e-20261012-15ae6d37-2e08-4bf6-8565-6110527f9d79-5hbgdwkx) Model ### [tournament-tourn_17ed417234322ccc_20260928-790304ac-0809-499b-bcca-609ab8e9cb59-5GLMLPbM](https://savrn.com/models/tournament-tourn-17ed417234322ccc-20260928-790304ac-0809-499b-bcca-609ab8e9cb59-5glmlpbm) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) Open weights [View model](https://savrn.com/models/tournament-tourn-17ed417234322ccc-20260928-790304ac-0809-499b-bcca-609ab8e9cb59-5glmlpbm) Model ### [tournament-tourn_17ed417234322ccc_20260928-dad11714-1cc9-4bc2-aced-95bac80d0ee1-5GLMLPbM](https://savrn.com/models/tournament-tourn-17ed417234322ccc-20260928-dad11714-1cc9-4bc2-aced-95bac80d0ee1-5glmlpbm) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) Open weights [View model](https://savrn.com/models/tournament-tourn-17ed417234322ccc-20260928-dad11714-1cc9-4bc2-aced-95bac80d0ee1-5glmlpbm) Model ### [tournament-tourn_c8068e356b7c22c8_20260928-e656b77d-f220-4bdf-8a37-969d6c803b50-5CkYizfL](https://savrn.com/models/tournament-tourn-c8068e356b7c22c8-20260928-e656b77d-f220-4bdf-8a37-969d6c803b50-5ckyizfl) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) Open weights 8.5B parameters 8,192 tokens [View model](https://savrn.com/models/tournament-tourn-c8068e356b7c22c8-20260928-e656b77d-f220-4bdf-8a37-969d6c803b50-5ckyizfl) Model ### [tournament-tourn_c8068e356b7c22c8_20260928-e656b77d-f220-4bdf-8a37-969d6c803b50-5E2fNjMW](https://savrn.com/models/tournament-tourn-c8068e356b7c22c8-20260928-e656b77d-f220-4bdf-8a37-969d6c803b50-5e2fnjmw) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) Open weights 8.5B parameters 8,192 tokens [View model](https://savrn.com/models/tournament-tourn-c8068e356b7c22c8-20260928-e656b77d-f220-4bdf-8a37-969d6c803b50-5e2fnjmw) Model ### [tournament-tourn_cfe8adad8593829c_20260921-7ed4b78b-74bb-4b82-85f1-a85e7e4bd26d-5GU4Xkd3](https://savrn.com/models/tournament-tourn-cfe8adad8593829c-20260921-7ed4b78b-74bb-4b82-85f1-a85e7e4bd26d-5gu4xkd3) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) Open weights 8.2B parameters 40,960 tokens [View model](https://savrn.com/models/tournament-tourn-cfe8adad8593829c-20260921-7ed4b78b-74bb-4b82-85f1-a85e7e4bd26d-5gu4xkd3) Model ### [tournament-tourn_d0dac5b21ce42a6b_20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5DDVSRDL](https://savrn.com/models/tournament-tourn-d0dac5b21ce42a6b-20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5ddvsrdl) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) Open weights 162M parameters 2,048 tokens [View model](https://savrn.com/models/tournament-tourn-d0dac5b21ce42a6b-20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5ddvsrdl) Model ### [tournament-tourn_d0dac5b21ce42a6b_20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5DLy32QP](https://savrn.com/models/tournament-tourn-d0dac5b21ce42a6b-20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5dly32qp) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) Open weights 162M parameters 2,048 tokens [View model](https://savrn.com/models/tournament-tourn-d0dac5b21ce42a6b-20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5dly32qp) Model ### [tournament-tourn_d0dac5b21ce42a6b_20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5E2fNjMW](https://savrn.com/models/tournament-tourn-d0dac5b21ce42a6b-20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5e2fnjmw) [Grads](https://savrn.com/model-publishers/gradients-io-tournaments) Open weights 162M parameters 2,048 tokens [View model](https://savrn.com/models/tournament-tourn-d0dac5b21ce42a6b-20261005-dabd0b32-a772-4357-b059-af8cdb55a630-5e2fnjmw) Model · Image segmentation ### [toxoplasma-from-cellmask-cpsam](https://savrn.com/models/toxoplasma-from-cellmask-cpsam) [Einar Olafsson](https://savrn.com/model-publishers/einarolafsson) Segments Toxoplasma gondii parasitophorous vacuoles from the host cell mask channel alone — no parasite-specific stain required. A cross-channel model: it is given the host cell image and predicts where the parasites are. This model is distributed through the spaCR Model Zoo. spaCR is an open-source package for spatial phenotype analysis of CRISPR screens and microscopy images. Launch the GUI and open the Model Zoo: Find Toxoplasma from Cell Mask (cross-channel) in the model list and press Download. The Model Zoo verifies the checkpoint's SHA-256 after download, so a truncated or substituted file is rejected rather than silently used. Point spaCR's mask generation at the downloaded… Open weights mit spacr [View model](https://savrn.com/models/toxoplasma-from-cellmask-cpsam) Model · Image segmentation ### [toxoplasma-plaque-segmentation-cpsam](https://savrn.com/models/toxoplasma-plaque-segmentation-cpsam) [Einar Olafsson](https://savrn.com/model-publishers/einarolafsson) Segments Toxoplasma gondii plaques in crystal violet plaque assays. Round 3. Use it through spaCR's plaque module. This model is distributed through the spaCR Model Zoo. spaCR is an open-source package for spatial phenotype analysis of CRISPR screens and microscopy images. Launch the GUI and open the Model Zoo: Find Toxoplasma Plaque v1 in the model list and press Download. The Model Zoo verifies the checkpoint's SHA-256 after download, so a truncated or substituted file is rejected rather than silently used. This is a plaque-assay model and is driven by spaCR's plaque module rather than In the GUI the same thing is under Make masks in the plaque workflow.… Open weights mit spacr [View model](https://savrn.com/models/toxoplasma-plaque-segmentation-cpsam) Model · Image segmentation ### [toxoplasma-plaque-segmentation-cpsam-r5](https://savrn.com/models/toxoplasma-plaque-segmentation-cpsam-r5) [Einar Olafsson](https://savrn.com/model-publishers/einarolafsson) Segments Toxoplasma gondii plaques in crystal-violet plaque assays. Round 5 is the first model trained across all four domains at once: whole-well microscope scans (two fixations), wells cropped from published figures, and phone-camera wells. Built for 488 curated fields, 27,582 plaques, deduplicated by pixel content. Split by FIGURE (literature) and by PLATE (microscope) so no figure or plate straddles; malnio's own held-out 20 are forced into test. Read the incumbent columns with care. r3 and r4 trained on every microscope well and on most of the literature, so their scores there measure memorisation. qc/summary.json also reports every model on the subset neither incumbent ever trained on… Open weights cc-by-4.0 [View model](https://savrn.com/models/toxoplasma-plaque-segmentation-cpsam-r5) Model · Image segmentation ### [toxoplasma-pv-segmentation-cpsam](https://savrn.com/models/toxoplasma-pv-segmentation-cpsam) [Einar Olafsson](https://savrn.com/model-publishers/einarolafsson) Segments Toxoplasma gondii parasitophorous vacuoles from a parasite stain (anti-Toxoplasma-biotin, or DsRed in the PV lumen). Round 2. This model is distributed through the spaCR Model Zoo. spaCR is an open-source package for spatial phenotype analysis of CRISPR screens and microscopy images. Launch the GUI and open the Model Zoo: Find Toxoplasma PV v1 in the model list and press Download. The Model Zoo verifies the checkpoint's SHA-256 after download, so a truncated or substituted file is rejected rather than silently used. Point spaCR's mask generation at the downloaded checkpoint: In the GUI the same thing is under Make masks — choose the downloaded model in the Cellpose model field for… Open weights mit spacr [View model](https://savrn.com/models/toxoplasma-pv-segmentation-cpsam) Model · Image segmentation ### [toxoplasma-pv-segmentation-cpsam-r5](https://savrn.com/models/toxoplasma-pv-segmentation-cpsam-r5) [Einar Olafsson](https://savrn.com/model-publishers/einarolafsson) Segments Toxoplasma gondii parasitophorous vacuoles from a parasite stain (anti-Toxoplasma-biotin, or DsRed in the PV lumen). Round 5 — the current promoted PV model, superseding round 2 (Toxoplasma PV v1). This model is distributed through the spaCR Model Zoo. spaCR is an open-source package for spatial phenotype analysis of CRISPR screens and microscopy images. Launch the GUI and open the Model Zoo: Find Toxoplasma PV v2 (round 5) in the model list and press Download. The Model Zoo verifies the checkpoint's SHA-256 after download, so a truncated or substituted file is rejected rather than silently used. Point spaCR's mask generation at the downloaded checkpoint: In the GUI the same thing… Open weights mit spacr [View model](https://savrn.com/models/toxoplasma-pv-segmentation-cpsam-r5) Model ### [toy-classification](https://savrn.com/models/toy-classification) [Iris Anderson](https://savrn.com/model-publishers/vikasmvm) This is an experimental Perceiver codebase for Classification. It keeps the large setup intentionally manageable so architecture changes can be inspected before a full training run. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adamw with a exponential schedule. These are starting values in the script, not evidence of a completed… Open weights apache-2.0 16,576 parameters 512 tokens [View model](https://savrn.com/models/toy-classification) Model ### [toy-multimodal-generation](https://savrn.com/models/toy-multimodal-generation) [Alvaro Martin](https://savrn.com/model-publishers/bcnmartin96) This is an exploratory note for Multimodal Generation. 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 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… Open weights cc-by-4.0 24,832 parameters 256 tokens [View model](https://savrn.com/models/toy-multimodal-generation) ## 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.