# Models, Page 476 of 477 Source: https://savrn.com/models/page/476 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 ### [yolo_buttons](https://savrn.com/models/yolo-buttons) [Jack Zheng](https://savrn.com/model-publishers/lubucat) Open weights [View model](https://savrn.com/models/yolo-buttons) Model ### [yolo_pallet_rfdetr_v4_small](https://savrn.com/models/yolo-pallet-rfdetr-v4-small) [Piero](https://savrn.com/model-publishers/piero123) Open weights [View model](https://savrn.com/models/yolo-pallet-rfdetr-v4-small) Model ### [yolobattlesif](https://savrn.com/models/yolobattlesif) [Vulcano](https://savrn.com/model-publishers/cranjismcbasketball) Open weights [View model](https://savrn.com/models/yolobattlesif) Model ### [yolov8n-agrovision-plagas](https://savrn.com/models/yolov8n-agrovision-plagas) [Ander](https://savrn.com/model-publishers/drex09) Open weights apache-2.0 [View model](https://savrn.com/models/yolov8n-agrovision-plagas) Model ### [yue2-industrial-rock-lora](https://savrn.com/models/yue2-industrial-rock-lora) [Monsterovich](https://savrn.com/model-publishers/monsterovich) A set of LoRA adapters for m-a-p/YuE2-3B, fine-tuned to push the base model toward industrial rock: heavy distorted guitars, mechanical/industrial sound textures, a dense mix, and aggressive rhythms. YuE2 generates music in two stages, and there is one adapter per stage: - AR adapter — shapes the plan: structure, melody, and genre direction (the autoregressive planning stage). - NAR adapter — shapes the timbre: the actual instrumental/audio texture (the non-autoregressive decoding stage). Each stage has an adapter trained on a large pool (179 tracks) and a small pool (86 tracks). Use the large-pool ones for the best quality; the small-pool ones are kept for A/B comparisons and as the… Open weights [View model](https://savrn.com/models/yue2-industrial-rock-lora) Model · Text to audio ### [yue2-mltnt-militant-reggae](https://savrn.com/models/yue2-mltnt-militant-reggae) [Because Reasons](https://savrn.com/model-publishers/becausereasons) Four artist-style LoRAs that push YuE2-3B into modern militant roots reggae: dark raspy male patois vocals, steppers and one-drop grooves, deep sub bass, bubbling Hammond, nyabinghi drums, horn stabs, dub sirens and spring reverb. Conscious, apocalyptic, anthemic. Each file patches both halves of YuE2 in one go: the autoregressive planner (writes the score, decides the arrangement and the vocal lines) and the flow-matching decoder (the sound). Trigger word for all three: mltnt. All demos use the same original lyric, seed 7, 32 steps dpm2 / sgmuniform, no post-processing. MLTNT Frontline — baseline recipe, prompt prompts/steppersbaseline.txt, dense lyric (verses written at ~17 words per… Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/yue2-mltnt-militant-reggae) Model ### [z51Sa8manu4AOeWP](https://savrn.com/models/z51sa8manu4aoewp) [14A3](https://savrn.com/model-publishers/14a3) Open weights [View model](https://savrn.com/models/z51sa8manu4aoewp) Model ### [zI11sk9VZXLnslSx](https://savrn.com/models/zi11sk9vzxlnslsx) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/zi11sk9vzxlnslsx) Model ### [zImageTurboNSFW_110](https://savrn.com/models/zimageturbonsfw-110) [Vin Fang](https://savrn.com/model-publishers/vbay03) Open weights [View model](https://savrn.com/models/zimageturbonsfw-110) Model ### [zXYu05VYqmxM0xwy](https://savrn.com/models/zxyu05vyqmxm0xwy) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/zxyu05vyqmxm0xwy) Model ### [zYmkEHLL5Kz7eNVW](https://savrn.com/models/zymkehll5kz7envw) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/zymkehll5kz7envw) Model ### [zarr-vega1u-insert-pen](https://savrn.com/models/zarr-vega1u-insert-pen) [Tron Robot](https://savrn.com/model-publishers/tron-robot) Open weights [View model](https://savrn.com/models/zarr-vega1u-insert-pen) Model ### [zaunx-voices](https://savrn.com/models/zaunx-voices) [App](https://savrn.com/model-publishers/zaunx) Open weights [View model](https://savrn.com/models/zaunx-voices) Model ### [zc-c2](https://savrn.com/models/zc-c2) [John Cumming](https://savrn.com/model-publishers/undertheradar) Open weights [View model](https://savrn.com/models/zc-c2) Model · Image to image ### [zen-image-edit-SDNQ-uint4-f16](https://savrn.com/models/zen-image-edit-sdnq-uint4-f16) [GuiAworld](https://savrn.com/model-publishers/guiaworld) Qwen-Image-2.1 on a 0.8B text encoder. Text-to-image, character and scene editing, and transparent (RGBA) generation in one pipeline. The text encoder is replaced by Qwen3.5-0.8B plus a 158M adapter, fine-tuned to reproduce what the native encoder produced — both from plain text and from text read together with the reference images (Improved using Qwen). The adapter lives inside the DiT as its text-fusion block, so the whole model is one self-contained diffusers folder and no 17.5 GB encoder is needed anywhere. The sampler runs a plain static shift of 5.0 instead of the original dynamic shifting. The bundled adapter is revision v12: its attention-branch position table covers 2304 slots and… Open weights other 4B parameters diffusers [View model](https://savrn.com/models/zen-image-edit-sdnq-uint4-f16) Model · Text generation ### [zen-ingress-1-3b-dpo-merged-i1-GGUF](https://savrn.com/models/zen-ingress-1-3b-dpo-merged-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/ZenithLLM/zen-ingress-1-3b-dpo-merged For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/zen-ingress-1-3b-dpo-merged-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter… Open weights apache-2.0 transformers [View model](https://savrn.com/models/zen-ingress-1-3b-dpo-merged-i1-gguf) Model ### [zenicka01-lora](https://savrn.com/models/zenicka01-lora) [Niko](https://savrn.com/model-publishers/nikolcee) Open weights [View model](https://savrn.com/models/zenicka01-lora) Model ### [zenstick-train](https://savrn.com/models/zenstick-train) [Antony MyKela](https://savrn.com/model-publishers/anthonymykela) Open weights [View model](https://savrn.com/models/zenstick-train) Model ### [zenzai-plus](https://savrn.com/models/zenzai-plus) [Soichi1208](https://savrn.com/model-publishers/soichi1208) Open weights cc-by-sa-4.0 [View model](https://savrn.com/models/zenzai-plus) Model ### [zero-shot-transfer-playground](https://savrn.com/models/zero-shot-transfer-playground) [Amitiyer1984](https://savrn.com/model-publishers/amitiyer1984) 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 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, hardware, and raw logs.… Open weights cc-by-4.0 24,832 parameters 512 tokens [View model](https://savrn.com/models/zero-shot-transfer-playground) Model ### [zeromem-pointer](https://savrn.com/models/zeromem-pointer) [Kumar](https://savrn.com/model-publishers/balaji9043) About 34M parameters, trained from scratch on a laptop. Reads a question plus a chunk whose sentences are tagged [A] [B] [C]... and answers [C] or, so it never retypes (or invents) text. best.pt is a PyTorch checkpoint for the ZeroMem code (not a transformers model); tokenizer.json is its byte-level BPE tokenizer. Open weights [View model](https://savrn.com/models/zeromem-pointer) Model ### [zeta-2.1-mlx-mixed-3-4bit](https://savrn.com/models/zeta-2-1-mlx-mixed-3-4bit) [Stratorys](https://savrn.com/model-publishers/stratorys) Open weights [View model](https://savrn.com/models/zeta-2-1-mlx-mixed-3-4bit) Model ### [zett-checkpoints-len60](https://savrn.com/models/zett-checkpoints-len60) [Shaira AI](https://savrn.com/model-publishers/shaira-ai) Open weights [View model](https://savrn.com/models/zett-checkpoints-len60) Model ### [zh-clip-vit-roberta-large-patch14-runtime](https://savrn.com/models/zh-clip-vit-roberta-large-patch14-runtime) [Andy Stewart](https://savrn.com/model-publishers/manateelazycat) Open weights [View model](https://savrn.com/models/zh-clip-vit-roberta-large-patch14-runtime) ## 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.