# Models, Page 366 of 477 Source: https://savrn.com/models/page/366 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 · Time series forecasting ### [metis-1-preview](https://savrn.com/models/metis-1-preview) [Parisi Labs, Inc.](https://savrn.com/model-publishers/parisi-labs) Metis is Parisi Labs' family of world models for physical systems, starting with energy. Metis 1 Preview is its first generation: probabilistic electricity price forecasts that read prices, load, generation and weather and forecast the full range of what power could cost at a location. This card describes the model. Weights are not distributed; forecasts are served through Ask the Grid, the Forecast API and MCP. Price is the target. Recent real-time prices, published day-ahead prices, load, generation and weather explain what the grid is doing, and every input is lined up to when it was actually available. Responses carry the issue time, quantile bands per interval, the settled price once… Open weights other [View model](https://savrn.com/models/metis-1-preview) Model ### [mfa-offline-bundle](https://savrn.com/models/mfa-offline-bundle) [Ngocbao05](https://savrn.com/model-publishers/ngocbao05) Open weights [View model](https://savrn.com/models/mfa-offline-bundle) Model ### [mfm-ffhq-checkpoints](https://savrn.com/models/mfm-ffhq-checkpoints) [Khodabandeh](https://savrn.com/model-publishers/borna) Open weights [View model](https://savrn.com/models/mfm-ffhq-checkpoints) Model ### [mfm-ffhq-distill-v2](https://savrn.com/models/mfm-ffhq-distill-v2) [Khodabandeh](https://savrn.com/model-publishers/borna) Open weights [View model](https://savrn.com/models/mfm-ffhq-distill-v2) Model ### [mfm-ffhq-lsd](https://savrn.com/models/mfm-ffhq-lsd) [Khodabandeh](https://savrn.com/model-publishers/borna) Open weights [View model](https://savrn.com/models/mfm-ffhq-lsd) Model ### [mfm-ffhq-unet](https://savrn.com/models/mfm-ffhq-unet) [Khodabandeh](https://savrn.com/model-publishers/borna) Open weights [View model](https://savrn.com/models/mfm-ffhq-unet) Model ### [mfm-ffhq-unet-v2](https://savrn.com/models/mfm-ffhq-unet-v2) [Khodabandeh](https://savrn.com/model-publishers/borna) Open weights [View model](https://savrn.com/models/mfm-ffhq-unet-v2) Model ### [mfm-ffhq-unet-v3](https://savrn.com/models/mfm-ffhq-unet-v3) [Khodabandeh](https://savrn.com/model-publishers/borna) Open weights [View model](https://savrn.com/models/mfm-ffhq-unet-v3) Model ### [mi-voz-xtts](https://savrn.com/models/mi-voz-xtts) [Madrid](https://savrn.com/model-publishers/manuel91) Open weights [View model](https://savrn.com/models/mi-voz-xtts) Model ### [mia-sharp](https://savrn.com/models/mia-sharp) [Mar Man](https://savrn.com/model-publishers/marman123) Open weights [View model](https://savrn.com/models/mia-sharp) Model · Image segmentation ### [mib-yolo11n-seg](https://savrn.com/models/mib-yolo11n-seg) [Gavin Lou](https://savrn.com/model-publishers/gavinlouuu) YOLO11n-seg weights used by the optional YoloService in deformability-cytometry microscopy application. The.pt checkpoint is the stock Ultralytics YOLO11n-seg release weights (AGPL-3.0). The ONNX export was produced with the bundled script at its default settings (static input shape, no simplification). The application pins this repository by commit and per-file SHA-256 in env/assets.json; scripts/provision-assets.py --asset yolo11n-seg downloads it into the build tree. Bumping the model is a reviewed change to that manifest. Open weights agpl-3.0 onnx [View model](https://savrn.com/models/mib-yolo11n-seg) Model ### [mic](https://savrn.com/models/mic) [Michael Romeike](https://savrn.com/model-publishers/microm1966) Open weights [View model](https://savrn.com/models/mic) Model · Robotics ### [microduck-bubu-hill](https://savrn.com/models/microduck-bubu-hill) [Dadudu](https://savrn.com/model-publishers/dadudu) Bubu: ES fine-tune of our own dive1 model75 on the hill (no other entries used) A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz). Runs until told otherwise — a gait, loaded into a policy slot. The observation normalizer is baked into policy.onnx; feed raw observations. manifest.json follows schema 2 of the microduck policy manifest (docs/policy-manifest.md in the daemon repo). Open weights microduck [View model](https://savrn.com/models/microduck-bubu-hill) Model · Robotics ### [microduck-roller-warm1](https://savrn.com/models/microduck-roller-warm1) [Stefen Pharai](https://savrn.com/model-publishers/stefo0) Mjlab-RollerSprint2m-MicroDuck A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz, on rollers). Runs until told otherwise — a gait, loaded into a policy slot. The observation normalizer is baked into policy.onnx; feed raw observations. manifest.json follows schema 2 of the microduck policy manifest (docs/policy-manifest.md in the daemon repo). - taskid: Mjlab-RollerSprint2m-MicroDuck Same code, same uv.lock, same command, same seed. Training it again yields a comparable policy, not the same weights: GPU reinforcement learning is not bit-reproducible across machines. Open weights microduck [View model](https://savrn.com/models/microduck-roller-warm1) Model · Robotics ### [microduck-senate_duck_1684](https://savrn.com/models/microduck-senate-duck-1684) [Dennis Schöneberg](https://savrn.com/model-publishers/denrakeiw) 2 m Roller Sprint: gladstone fine-tuned by evolution strategies on the Arena's roller scorer. A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz, on rollers). Runs until told otherwise — a gait for the walk slot. The observation normalizer is baked into policy.onnx; feed raw observations. manifest.json follows schema 2 of the microduck policy manifest (docs/policy-manifest.md in the daemon repo). Open weights microduck [View model](https://savrn.com/models/microduck-senate-duck-1684) Model · Robotics ### [microduck-sprint-2m-dash1](https://savrn.com/models/microduck-sprint-2m-dash1) [Stephen E. Tarter](https://savrn.com/model-publishers/thesauceman) I stopped asking my duck to match a speed and simply paid it for going fast. It learned to throw its heavy head forward and bound down the lane with both legs together, getting a running start before the clock even begins. A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz). Runs until told otherwise — a gait, loaded into a policy slot. The observation normalizer is baked into policy.onnx; feed raw observations. manifest.json follows schema 2 of the microduck policy manifest (docs/policy-manifest.md in the daemon repo). - taskid: Mjlab-Sprint2m-MicroDuck Same code, same uv.lock, same command, same seed. Training it again yields a comparable policy, not the same… Open weights microduck [View model](https://savrn.com/models/microduck-sprint-2m-dash1) Model · Robotics ### [microduck-sprint-es10](https://savrn.com/models/microduck-sprint-es10) [Jarrod Pflum](https://savrn.com/model-publishers/juicenv) 2 m Sprint: suraj-phanindra/microduck-sprint-hc2 (0.892 s, itself from namenu/microduck-arena) fine-tuned by evolution strategies on the Arena sprint scorer (0.888 s). Credit to suraj-phanindra and namenu. A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz). Runs until told otherwise — a gait, loaded into a policy slot. The observation normalizer is baked into policy.onnx; feed raw observations. manifest.json follows schema 2 of the microduck policy manifest (docs/policy-manifest.md in the daemon repo). A remix of suraj-phanindra/microduck-sprint-hc2. Open weights microduck [View model](https://savrn.com/models/microduck-sprint-es10) Model · Robotics ### [microduck-sprint-hc3](https://savrn.com/models/microduck-sprint-hc3) [Suraj Phanindra](https://savrn.com/model-publishers/suraj-phanindra) juicenv/microduck-sprint-es10 fine-tuned by elitist hill climb on the Arena score A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz). Runs until told otherwise — a gait, loaded into a policy slot. The observation normalizer is baked into policy.onnx; feed raw observations. manifest.json follows schema 2 of the microduck policy manifest (docs/policy-manifest.md in the daemon repo). A remix of juicenv/microduck-sprint-es10. Open weights microduck [View model](https://savrn.com/models/microduck-sprint-hc3) Model · Robotics ### [microduck-sprint-v1](https://savrn.com/models/microduck-sprint-v1) [Kyrylo Kolesnichenko](https://savrn.com/model-publishers/kyry10) sprint-v1 A perpetual policy for the microduck (61-D observation, 14 actions, 50 Hz). Runs until told otherwise — a gait, loaded into a policy slot. The observation normalizer is baked into policy.onnx; feed raw observations. manifest.json follows schema 2 of the microduck policy manifest (docs/policy-manifest.md in the daemon repo). A remix of HannesVonEssen/microduck-running. Open weights microduck [View model](https://savrn.com/models/microduck-sprint-v1) Model · Text to speech ### [microsoft__VibeVoice-Realtime-0.5B](https://savrn.com/models/microsoft-vibevoice-realtime-0-5b) [Allinsama](https://savrn.com/model-publishers/allinsama) VibeVoice-Realtime is a lightweight real‑time text-to-speech model supporting streaming text input and robust long-form speech generation. It can be used to build realtime TTS services, narrate live data streams, and let different LLMs start speaking from their very first tokens (plug in your preferred model) long before a full answer is generated. It produces initial audible speech in ~300 ms (hardware dependent). ▶ Watch demo video (Launch your own realtime demo via the websocket example in Usage) Although the model is primarily built for English, we found that it still exhibits a certain level of multilingual capability—and even performs reasonably well in some languages. We provide nine… Open weights mit 1B parameters transformers [View model](https://savrn.com/models/microsoft-vibevoice-realtime-0-5b) Model ### [microsoft__wavlm-base](https://savrn.com/models/microsoft-wavlm-base) [Allinsama](https://savrn.com/model-publishers/allinsama) The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. Note: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. The model was pre-trained on 960h of Librispeech. Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng… Open weights [View model](https://savrn.com/models/microsoft-wavlm-base) Model ### [mindmate-ocean-roberta-v2](https://savrn.com/models/mindmate-ocean-roberta-v2) [Jewell Gomes](https://savrn.com/model-publishers/jewellgomes) Open weights 125M parameters 514 tokens [View model](https://savrn.com/models/mindmate-ocean-roberta-v2) Model ### [minecraft-world-jepa-ckpts](https://savrn.com/models/minecraft-world-jepa-ckpts) [Keylhan Paumard--André](https://savrn.com/model-publishers/keypa) Autoregressive video world model for Minecraft. A frozen Stable Diffusion VAE compresses 256×256 frames to 4×32×32 latents; a decoder-only transformer (338M params, RoPE, DWS-style per-block action adapters, SDPA flash attention) predicts the next latent conditioned on discrete action tokens (29 ids: 22 keyboard + 6 camera bins + 1 no-op). Each run directory holds a full resumable state (model + optimizer + scheduler + step/epoch counters), so any file below restarts training bit-identically with the matching config and --resume: Weights derive from public TESS-VLA gameplay data and an sd-vae-ft-mse base. No license is asserted here — check the dataset and base-model terms before commercial… Open weights other pytorch [View model](https://savrn.com/models/minecraft-world-jepa-ckpts) Model ### [ming-mnn-models](https://savrn.com/models/ming-mnn-models) [Su](https://savrn.com/model-publishers/gezihua123) Ming（AI 算命 App）端侧推理用的 MNN 模型包，zip 打包，一次取回即可用。 zip 内是扁平结构，解压即得模型目录内容： - qwen3-0.6b：llm.mnn · qwen3.5-0.8b：上面 4 （该仓库的 llmconfig.json 声明 isvisual: true，缺视觉权重会加载失败） 每个文件的 sha256 与 App 随包配置 assets/models.json 中的声明一致（打包前逐个校验通过）。 Open weights [View model](https://savrn.com/models/ming-mnn-models) ## 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.