# Models, Page 183 of 525 Source: https://savrn.com/models/page/183 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 · Text to image ### [MOSS-SFT-uint4-fix](https://savrn.com/models/moss-sft-uint4-fix) [Void](https://savrn.com/model-publishers/void-lab) Access requested at publisher 2.7B parameters diffusers [View model](https://savrn.com/models/moss-sft-uint4-fix) Model · Text to speech ### [MOSS-TTS-8B-Q6-HQ8Speech-G64](https://savrn.com/models/moss-tts-8b-q6-hq8speech-g64) [Facu Vlad J](https://savrn.com/model-publishers/groxaxo) This is an MLX model for Apple Silicon. It is not directly compatible with the PyTorch CUDA runner. Converted from mlx-community/MOSS-TTS-8B-8bit at revision b9262754ad5245942472c7b4ff2b53cec3a07ec0: Q6 backbone and Q8 speech path, affine quantization with group size 64. Structurally validated on Linux; Apple Silicon runtime and speech-quality validation are pending. See RESULT.md, recipe.json, and the audit reports for details. The upstream model card follows. This MLX implementation brings MOSS-TTS to Apple Silicon through the MLX runtime, adapting the TTS backbone, multi-head audio token generation, and MOSS Audio Tokenizer workflow to run inside the MLX-Audio stack. As part of… Open weights apache-2.0 40,960 tokens [View model](https://savrn.com/models/moss-tts-8b-q6-hq8speech-g64) Model · Video text to text ### [MOSS-VL-Realtime-GGUF](https://savrn.com/models/moss-vl-realtime-gguf) [Skycontact](https://savrn.com/model-publishers/skycontact) GGUF quantization of OpenMOSS-Team/MOSS-VL-Realtime-NF4 (11B cross-attention VLM: Qwen3 text trunk with 12 pure cross-attn layers + SigLIP tower with deepstack), repackaged for the SkyVision.cpp inference engine. - NF4 weights are dequantized bit-exactly against bitsandbytes CUDA first (checkmossvlnf4.py); - the fused cross-KV projection weight is built at load time (engine-side); 12 GB GPU deployments: --n-ctx 4096 (and MOSSXCTX=6144 for long videos). Full chat, multi-turn sessions, realtime streaming (proactive narration with ) and a web UI are available via --serve. Follows the base model's license (see the OpenMOSS-Team/MOSS-VL-Realtime-NF4 page). Open weights apache-2.0 SkyVision.cpp [View model](https://savrn.com/models/moss-vl-realtime-gguf) Model · Audio to audio ### [MOSS-v2-12to32-Enhancer-60M](https://savrn.com/models/moss-v2-12to32-enhancer-60m) [LAION eV](https://savrn.com/model-publishers/laion) A small causal Qwen3 model that predicts 32 MOSS v2 audio-codebook indices from the 12 indices of the current frame and up to ten previously generated HQ frames. It is trained from scratch on German, English, Spanish and French speech. This is an experimental enhancement model. Validation cross entropy measures token prediction under teacher forcing. It does not establish an improvement in perceived audio quality. Use the generated-history listening evaluation to judge speech content, speaker identity, artifacts and long-utterance stability. Root inference weights: step 64,699, selected by held-out extra-20-codebook cross entropy 5.818123. - Step 12,940: 100 originals / 200 input cases… Open weights cc-by-nc-4.0 pytorch [View model](https://savrn.com/models/moss-v2-12to32-enhancer-60m) Model ### [MOSS_TTSD_tokenizer](https://savrn.com/models/moss-ttsd-tokenizer) [Le Jin](https://savrn.com/model-publishers/jinstudio) Open weights [View model](https://savrn.com/models/moss-ttsd-tokenizer) Model ### [MOSS_TTSD_tokenizer_hf](https://savrn.com/models/moss-ttsd-tokenizer-hf) [Le Jin](https://savrn.com/model-publishers/jinstudio) Open weights apache-2.0 534M parameters [View model](https://savrn.com/models/moss-ttsd-tokenizer-hf) Model ### [MQYSy55wkTTWmc4c](https://savrn.com/models/mqysy55wkttwmc4c) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/mqysy55wkttwmc4c) Model ### [MR46OwbKLnsvjbfX](https://savrn.com/models/mr46owbklnsvjbfx) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/mr46owbklnsvjbfx) Model ### [MS4118-DistilBERT](https://savrn.com/models/ms4118-distilbert) [Ccy](https://savrn.com/model-publishers/0569yuki) Open weights 67M parameters [View model](https://savrn.com/models/ms4118-distilbert) Model · Reinforcement learning ### [MSK-Bench](https://savrn.com/models/msk-bench) [Zhang Zongzheng](https://savrn.com/model-publishers/zzz0918) The data/ directory contains policy weights for MyoSuite/MSK-Bench tasks, together with their configuration files, training logs, and evaluation results. The top-level folders identify algorithms or experiment series. Weights from different folders are not interchangeable, even when their task names look similar. The word best is part of the original filenames; this README does not infer which metric was used to select those checkpoints. To reproduce an evaluation, also check the configuration, environment version, and any associated preprocessing or normalization state. This directory has three task folders. Each numbered subdirectory contains one experiment run. The environment names… Open weights [View model](https://savrn.com/models/msk-bench) Model ### [MTG-Art-Search](https://savrn.com/models/mtg-art-search) [Kyle Pearson](https://savrn.com/model-publishers/pearsonkyle) Open weights [View model](https://savrn.com/models/mtg-art-search) Model ### [MVRD](https://savrn.com/models/mvrd) [Nguyen Tuan Kiet](https://savrn.com/model-publishers/kietngt00) Open weights [View model](https://savrn.com/models/mvrd) Model ### [MaLoW-Qwen3-VL-4B-Last8s](https://savrn.com/models/malow-qwen3-vl-4b-last8s) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3-VL-4B-Instruct. This repository contains inference modules, not a standalone base model. This variant uses the last 8 seconds as the current observation, represented by 8 frames. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark… Open weights apache-2.0 198M parameters [View model](https://savrn.com/models/malow-qwen3-vl-4b-last8s) Model ### [MaLoW-Qwen3.5-0.8B](https://savrn.com/models/malow-qwen3-5-0-8b) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-0.8B. This repository contains inference modules, not a standalone base model. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets retain their respective upstream terms. Open weights apache-2.0 116M parameters [View model](https://savrn.com/models/malow-qwen3-5-0-8b) Model ### [MaLoW-Qwen3.5-2B](https://savrn.com/models/malow-qwen3-5-2b) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-2B. This repository contains inference modules, not a standalone base model. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets retain their respective upstream terms. Open weights apache-2.0 171M parameters [View model](https://savrn.com/models/malow-qwen3-5-2b) Model ### [MaLoW-Qwen3.5-2B-MLP](https://savrn.com/models/malow-qwen3-5-2b-mlp) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Evaluation requires the MaLoW code release. The code has been prepared locally; public GitHub publication is pending. Intended repository: MaLoW (forthcoming). Download the base model separately: Qwen/Qwen3.5-2B. This repository contains inference modules, not a standalone base model. Follow docs/vlaevaluation.md in the MaLoW code release for Simpler WidowX. This checkpoint additionally requires the Qwen3.5-2B-MLP baseline adapter as the frozen policy base. Both bundles are required. Reported reference success rates (not a fresh evaluation of this export): datasetstatistics.json supplies action… Open weights apache-2.0 175M parameters [View model](https://savrn.com/models/malow-qwen3-5-2b-mlp) Model ### [MaLoW-Qwen3.5-4B](https://savrn.com/models/malow-qwen3-5-4b) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-4B. This repository contains inference modules, not a standalone base model. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Overall computed from listed subtask scores using benchmark weights 2500:1500:250:250. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets retain… Open weights apache-2.0 235M parameters [View model](https://savrn.com/models/malow-qwen3-5-4b) Model ### [MaLoW-Qwen3.5-4B-Last4s](https://savrn.com/models/malow-qwen3-5-4b-last4s) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Inference checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-4B. This repository contains inference modules, not a standalone base model. Evaluation uses the last 4 seconds as the current observation, represented by 4 frames. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets… Open weights apache-2.0 235M parameters [View model](https://savrn.com/models/malow-qwen3-5-4b-last4s) Model ### [MaLoW-Qwen3.5-9B](https://savrn.com/models/malow-qwen3-5-9b) [Zhuoming Liu](https://savrn.com/model-publishers/zhuomingliu) Checkpoint for Memory as Weights: Internalizing Long-Term History for Streaming Videos. Paper: Memory as Weights: Internalizing Long-Term History for Streaming Videos Download the base model separately: Qwen/Qwen3.5-9B. This repository contains inference modules, not a standalone base model. Follow docs/videoevaluation.md in the MaLoW code release for OVO-Bench and StreamingBench. The JSON metadata records the inference configuration; load it through the MaLoW runtime. Weights and inference metadata are distributed under Apache-2.0. See LICENSE and NOTICE; base models and benchmark assets retain their respective upstream terms. Open weights apache-2.0 309M parameters [View model](https://savrn.com/models/malow-qwen3-5-9b) Model · Image to image ### [MadStickArt-v0.1.0](https://savrn.com/models/madstickart-v0-1-0) [Danger Room](https://savrn.com/model-publishers/danger-room) Try MadStickArt online → — generate a storyboard in your browser before downloading. Adjust composition and camera controls, then export clean or annotated PNGs and scene JSON. The playground runs the released neural decoder locally and uses optional rule-based scene suggestions; Strands Decider is available in the downloadable Python pipeline. MadStickArt is a small, trained 12,192-parameter image model for clean stick-figure storyboard scenes at 956 × 400 pixels, exactly 2.39:1. This first public release is danger-room/MadStickArt-v0.1.0, licensed under Apache 2.0. Pin revision v0.1.0 for reproducible downloads. The model is a custom PyTorch layout-conditioned neural raster decoder. A… Open weights apache-2.0 12,192 parameters pytorch [View model](https://savrn.com/models/madstickart-v0-1-0) Model · Image to image ### [MadStickArt-v0.8.0](https://savrn.com/models/madstickart-v0-8-0) [Danger Room](https://savrn.com/model-publishers/danger-room) MadStickArt is a trained, 61,296-parameter neural raster decoder for stick-figure storyboard scenes. It reconstructs 956 × 400 grayscale frames from four explicit layout masks: actors, setting, props, and motion. Its output contract is 2.39:1. Try MadStickArt online → — the browser trial is deployed separately. Its deployed model/version and renderer are recorded in the Space's model-provenance.json. The renderer included with this release supports 186 settings and 320 props; the deployed trial has its own model and renderer pins and is updated through a separate reviewed stage. Browser suggestions use transparent rules, while Strands decisions run in the downloadable Python pipeline. The… Open weights apache-2.0 61,296 parameters pytorch [View model](https://savrn.com/models/madstickart-v0-8-0) Model · Text to image ### [MageTrail](https://savrn.com/models/magetrail) [Tran Tay](https://savrn.com/model-publishers/ricemant) ALL PREVIEW IMAGES HAVE COMFYUI METADATA. MageTrail is a Danbooru/E621 proof of concept Full-Finetune of Microsoft's MageFlow 4B T2I model, now 2.8B custom architecture, using a diversity maximized condensed 41k images dataset as a way to tune booru concept and tags based prompting + Illustration capabilities into the model without having to tune with the full booru dataset. My second attempt at fine-tuning an image model on larger scale, this finetune aim to prove to the open source community on MageFlow 4B having good potential as an architecture for further finetuning. ~ V0.1 to V0.3 have spent 593 dollars and has ultimately shown the model potential in quickly learning and adapting… Open weights mit diffusers [View model](https://savrn.com/models/magetrail) Model · Text generation ### [Maillard-0.1-1.1B-Alpha](https://savrn.com/models/maillard-0-1-1-1b-alpha) [RenWuJun](https://savrn.com/model-publishers/renwujun) Open weights mit [View model](https://savrn.com/models/maillard-0-1-1-1b-alpha) Model · Text generation ### [Mangaba-Nano-1M](https://savrn.com/models/mangaba-nano-1m) [Mangaba AI](https://savrn.com/model-publishers/mangaba-ai) Um modelo de linguagem de 1,4 milhão de parâmetros, treinado do zero em português do Brasil pela Mangaba AI. Relatório técnico completo (dados, arquitetura, Muon, ablações, controles e destilação): https://huggingface.co/spaces/mangaba-ai/mangaba-nano-artigo Não há modelo base: os pesos começaram aleatórios e o tokenizer também foi treinado do zero sobre o mesmo corpus. O treino rodou nas GPUs NVIDIA L40S da própria Mangaba, em datacenter no Brasil. É um modelo de pesquisa e de borda: cabe em 5,6 MB, roda em qualquer CPU e serve como base para estudar escalas minúsculas, fazer ajuste fino em tarefas estreitas (classificação, autocompletar, extração simples) ou rodar em microcontroladores e… Open weights apache-2.0 1M parameters 512 tokens transformers [View model](https://savrn.com/models/mangaba-nano-1m) ## 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.