# Models, Page 245 of 512 Source: https://savrn.com/models/page/245 Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- SAVRN Model Hub # Open-Weight Models An open-weight model is an AI model whose trained weights are published for anyone to download. The weights are what the model learned in training. With a copy of them you can run the model on hardware you control and train it further on your own data. Open weights are not the same as open source. Many publishers release the weights without the training data or code, and the license sets what you may do with the model. This library puts each model's full card, architecture, files, license and published evaluations on one page. 12,283Models 3,510Datasets 290Papers 7,436Publishers 12,632Sourced relationships Updated 2026-10-22 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,506](https://savrn.com/models?task=Text+generation) - [Image and text to text560](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction396](https://savrn.com/models?task=Feature+extraction) - [Robotics379](https://savrn.com/models?task=Robotics) - [Text classification328](https://savrn.com/models?task=Text+classification) - [Text to image228](https://savrn.com/models?task=Text+to+image) - [Speech recognition185](https://savrn.com/models?task=Speech+recognition) - [Text to speech143](https://savrn.com/models?task=Text+to+speech) - [Image classification139](https://savrn.com/models?task=Image+classification) - [Audio classification130](https://savrn.com/models?task=Audio+classification) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification99](https://savrn.com/models?task=Token+classification) - [Reinforcement learning97](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation90](https://savrn.com/models?task=Image+segmentation) - [Object detection86](https://savrn.com/models?task=Object+detection) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [Translation79](https://savrn.com/models?task=Translation) - [Question answering73](https://savrn.com/models?task=Question+answering) - [Fill mask69](https://savrn.com/models?task=Fill+mask) - [Time series forecasting64](https://savrn.com/models?task=Time+series+forecasting) - [Video classification60](https://savrn.com/models?task=Video+classification) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,947](https://savrn.com/models?library=transformers) - [peft361](https://savrn.com/models?library=peft) - [diffusers202](https://savrn.com/models?library=diffusers) - [pytorch194](https://savrn.com/models?library=pytorch) - [gguf181](https://savrn.com/models?library=gguf) - [mlx157](https://savrn.com/models?library=mlx) - [lerobot150](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp45](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [onnx40](https://savrn.com/models?library=onnx) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [diffusion-single-file29](https://savrn.com/models?library=diffusion-single-file) - [minimax-h327](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [exllamav320](https://savrn.com/models?library=exllamav3) - [openvino20](https://savrn.com/models?library=openvino) - [coreml18](https://savrn.com/models?library=coreml) - [litert17](https://savrn.com/models?library=litert) - [keras15](https://savrn.com/models?library=keras) Modality - [Other7,017](https://savrn.com/models?modality=Other) - [Text2,788](https://savrn.com/models?modality=Text) - [Image and text619](https://savrn.com/models?modality=Image+and+text) - [Image593](https://savrn.com/models?modality=Image) - [Audio476](https://savrn.com/models?modality=Audio) - [Control476](https://savrn.com/models?modality=Control) - [Video115](https://savrn.com/models?modality=Video) - [Multimodal79](https://savrn.com/models?modality=Multimodal) - [Time series64](https://savrn.com/models?modality=Time+series) - [Tabular56](https://savrn.com/models?modality=Tabular) License - [apache-2.03,285](https://savrn.com/models?license=apache-2.0) - [mit1,305](https://savrn.com/models?license=mit) - [other759](https://savrn.com/models?license=other) - [cc-by-nc-4.0209](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0164](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause108](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.052](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.240](https://savrn.com/models?license=llama3.2) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [unknown32](https://savrn.com/models?license=unknown) - [llama3.128](https://savrn.com/models?license=llama3.1) - [cc-by-sa-4.020](https://savrn.com/models?license=cc-by-sa-4.0) - [gpl-3.020](https://savrn.com/models?license=gpl-3.0) - [openrail++17](https://savrn.com/models?license=openrail%2B%2B) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [cc-by-nc-nd-4.013](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [creativeml-openrail-m13](https://savrn.com/models?license=creativeml-openrail-m) - [llama28](https://savrn.com/models?license=llama2) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights11,872](https://savrn.com/models?access=open_access) - [Access requested at publisher411](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher324](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan110](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai102](https://savrn.com/models?publisher=runninghubai) - [Qwen91](https://savrn.com/models?publisher=qwen) - [Jenny Wilson85](https://savrn.com/models?publisher=jenny08311) - [Jessen Pu77](https://savrn.com/models?publisher=putty77) - [RKNNAI73](https://savrn.com/models?publisher=rknnai) - [AI at Meta72](https://savrn.com/models?publisher=facebook) - [Convergent Intelligence66](https://savrn.com/models?publisher=reaperdoesntknow) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan64](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Yurun Yuan61](https://savrn.com/models?publisher=yuruny) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Yurun Yuan57](https://savrn.com/models?publisher=yyuan244) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [Szekely52](https://savrn.com/models?publisher=attila115) - [Khazzz1c52](https://savrn.com/models?publisher=khazic) - [Christopher Subia-Waud50](https://savrn.com/models?publisher=cwaud) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [NVIDIA49](https://savrn.com/models?publisher=nvidia) [Most downloaded](https://savrn.com/models?sort=downloads)[Most liked](https://savrn.com/models?sort=likes)[Recently updated](https://savrn.com/models?sort=updated)[Name](https://savrn.com/models?sort=name)[Largest](https://savrn.com/models?sort=params) 12,283 models, sorted by most downloaded. Model · Robotics ### [Xiaomi-Robotics-1-VLABench](https://savrn.com/models/xiaomi-robotics-1-vlabench) [Bri Parales](https://savrn.com/model-publishers/introvoyz045) This repository contains the Hugging Face checkpoint used by Xiaomi-Robotics-1 for VLABench evaluation. It includes the model weights, custom Transformers model and processor code, tokenizer files, and VLABench action normalization statistics. The custom model and processor must be loaded with trustremotecode=True. For the VLABench simulation environment, follow the companion source repository at evalvlabench/README.md. The evaluation client uses a separate vlabench conda environment with Python 3.10 and the pinned MuJoCo / dmcontrol dependencies described there. For reproducible multi-GPU evaluation, use the standard multi-server launcher in the companion Xiaomi-Robotics-1 source… Open weights apache-2.0 5.1B parameters [View model](https://savrn.com/models/xiaomi-robotics-1-vlabench) Model ### [Xiaomi-TabLDM](https://savrn.com/models/xiaomi-tabldm) [Xinzhenwei](https://savrn.com/model-publishers/occams) Xiaomi-TabLDM: A Tabular Large Data Foundation Model For Classification and Regression via In-Context Learning English This repository is the official implementation of Xiaomi-TabLDM. Tabular foundation models establish a general prediction paradigm based on in-context learning. Given labeled samples from a downstream dataset as context, a single pretrained model can make predictions directly without task-specific training. Building on this paradigm, we introduce Xiaomi-TabLDM, a tabular large data foundation model for classification and regression via in-context learning, which delivers superior prediction accuracy without requiring task-specific fine-tuning. Pretrained exclusively on… Open weights mit custom [View model](https://savrn.com/models/xiaomi-tabldm) Model ### [XiaomiOS-ROM](https://savrn.com/models/xiaomios-rom) [StrongNguyen](https://savrn.com/model-publishers/strong568) Open weights [View model](https://savrn.com/models/xiaomios-rom) Model ### [Xing4.0-29B-A4B](https://savrn.com/models/xing4-0-29b-a4b) [21g](https://savrn.com/model-publishers/21g) Open weights 262,144 tokens [View model](https://savrn.com/models/xing4-0-29b-a4b) Model ### [Xing4.0-29B-A4B-telecom-customer-service-chat](https://savrn.com/models/xing4-0-29b-a4b-telecom-customer-service-chat) [JacksonGuo](https://savrn.com/model-publishers/grow-nexus) This model is a fine-tune of specialized for telecom-customer-service-chat, targeting the Guangdong Telecom 10000 hotline smart customer-service agent. Instead of replying like a generic assistant, the model is tuned to act as a slots, dispatching the right business tool, and producing compliant, warm and concise service dialogue. - Intent recognition & routing — accurately identifies user intents (账单查询、流量查询、装移机、报障、宽带续费、投诉等) and dispatches to the correct skill / candidate task. - Slot clarification — actively asks for missing required slots only when the user's intent is unclear, avoiding redundant or misleading questions. - Tool calling & task dispatch — outputs structured toolcall /… Open weights [View model](https://savrn.com/models/xing4-0-29b-a4b-telecom-customer-service-chat) Model ### [Y3OfDn7nPdd2jd8P](https://savrn.com/models/y3ofdn7npdd2jd8p) [15A7](https://savrn.com/model-publishers/15a7) Open weights [View model](https://savrn.com/models/y3ofdn7npdd2jd8p) Model · Text generation ### [YANchor-4B](https://savrn.com/models/yanchor-4b) [RocoreMatrix](https://savrn.com/model-publishers/huishanji) YANchor-4B is a general-purpose recurrent model that preserves crucial memory as ANchors for retrieval during subsequent reasoning. Its multidimensional memory mechanism enables effective long-horizon reasoning with O(N) generation time and O(1) history-state memory. For example, on challenging math problems, YANchor achieves 82.93% mean pass@1 on AIME 2024–2026 and 63.64% on HMMT, substantially outperforming linear-time, constant-state counterparts, including 7B–14B models. YANchor delivers several-fold higher batched long-generation throughput than Transformer and hybrid baselines on H100. Evaluations across dozens of benchmarks demonstrate its superiority in general-purpose capabilities.… Open weights other 4.6B parameters 262,144 tokens [View model](https://savrn.com/models/yanchor-4b) Model ### [YF8iNUcLRfLUoVv8](https://savrn.com/models/yf8inuclrfluovv8) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/yf8inuclrfluovv8) Model ### [YZ9TCo3PLwrylDVG](https://savrn.com/models/yz9tco3plwryldvg) [14A4](https://savrn.com/model-publishers/14a4) Open weights [View model](https://savrn.com/models/yz9tco3plwryldvg) Model ### [YahBible](https://savrn.com/models/yahbible) [Ya'akov Yisra'el](https://savrn.com/model-publishers/ohbeonekeynobe) Study the Word as it was spoken — verse by verse, tongue by tongue. YahBible is a free desktop app for deep study and comparison of the Bible and the other sacred literatures — a custom reader, a Hebrew/Greek/Aramaic word engine, Strong's + interlinear, It runs right out of the box on Windows — download one file and go. 1. Download YahBible.exe (button above, or the Files and versions tab). 2. Double-click it. 3. It opens in your browser at 127.0.0.1. That's it. Or install it for a permanent home + a Start-menu / taskbar icon: run YahBible.exe and choose Install, then pin the icon. On first run it downloads its study library from this repo (~1.2 GB), so the app is usable within moments; the… Open weights other [View model](https://savrn.com/models/yahbible) Model ### [YahBible-Mobile](https://savrn.com/models/yahbible-mobile) [Ya'akov Yisra'el](https://savrn.com/model-publishers/ohbeonekeynobe) The whole counsel of Scripture — to read, search and study, in the original Hebrew, Greek and Aramaic, on any device, offline. Free for life. Grab it first, then keep reading while it downloads. It's free — and it stays free. yes · requires payment or subscription · no. Only YahBible is fully offline — including its AI search and reasoning — and stays free with no trial or subscription. Other apps' names belong to their owners. The King James Bible with 120+ translations, verse for verse. Search however you remember a passage — "3:16 John", "the 23rd Psalm" — and switch versions without losing your place. Open any commandment in the centre: its Scripture, what it forbids, how to keep it… Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/yahbible-mobile) Model ### [Yandel](https://savrn.com/models/yandel) [Franzoni](https://savrn.com/model-publishers/jovak) Open weights [View model](https://savrn.com/models/yandel) Model ### [Yida-Model-14B-GGUF](https://savrn.com/models/yida-model-14b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/rwang220/Yida-Model-14B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. 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… Open weights apache-2.0 transformers [View model](https://savrn.com/models/yida-model-14b-gguf) Model · Text generation ### [YoungAi-DeepSeek-V4.1-Flash](https://savrn.com/models/youngai-deepseek-v4-1-flash) [Wenzhou Wu](https://savrn.com/model-publishers/wenzhouwu) English · 中文 ds4 is a native C/CUDA inference engine plus an offline toolchain that runs DeepSeek V4.1 Flash on a single NVIDIA DGX Spark (GB10, 128 GB unified memory). Everything described here is our own work: the vector-quantization format, the solvers that produce the sidecar and the post-training file, the CUDA kernels, and the rulers we judge all of it with. The model architecture is DeepSeek's and is not re-explained here — read the official release for that. How to read this page. It unfolds in five steps; stop wherever you have what you need. 1. The numbers — what runs, how fast, how close to the original. 2. Three convictions — why the system is shaped this way. 3. The… Open weights mit [View model](https://savrn.com/models/youngai-deepseek-v4-1-flash) Model ### [YoungAi-DeepSeek-V4.1-Flash-1017](https://savrn.com/models/youngai-deepseek-v4-1-flash-1017) [Wenzhou Wu](https://savrn.com/model-publishers/wenzhouwu) Open weights [View model](https://savrn.com/models/youngai-deepseek-v4-1-flash-1017) Model · Text to audio ### [YuE2-3B-NF4-for-LoRA-Training](https://savrn.com/models/yue2-3b-nf4-for-lora-training) [J.M. Ruiz](https://savrn.com/model-publishers/academiasd) Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/yue2-3b-nf4-for-lora-training) Model ### [Z-Image-Engineer-V6-DHQ-GGUF](https://savrn.com/models/z-image-engineer-v6-dhq-gguf) [Pottokao](https://savrn.com/model-publishers/pottokao) DHQ = Dual-Head Quant. Z-Image-Engineer-V6 (a Qwen3-4B fine-tune that is both a Z-Image text encoder and a prompt writer), quantized so that every tensor gets the precision that matters to what the Z-Image DiT actually reads (measured per tensor with gradients through the DiT's text input), with an importance matrix computed on V6 itself. Each file is exactly the size of the standard llama.cpp file of the same name, and the text-encoder error is lower at every level. Tuned for Z-Image only. It was not measured with FLUX.2 [klein] 4B; for klein use the plain Qwen3-4B file in DHQ files are quantized for use as a diffusion model's text encoder: each tensor's precision is chosen by how much its… Open weights apache-2.0 [View model](https://savrn.com/models/z-image-engineer-v6-dhq-gguf) Model ### [Z-Image-Turbo](https://savrn.com/models/z-image-turbo-3) [Yoai Kimia](https://savrn.com/model-publishers/yoaikimia) Access requested at publisher [View model](https://savrn.com/models/z-image-turbo-3) Model · Image and text to text ### [ZDTaichu5.0-9B-GGUF](https://savrn.com/models/zdtaichu5-0-9b-gguf) [Zidongtaichu](https://savrn.com/model-publishers/taichuai) ZDTaichu5.0-9B is a multimodal foundation model for general visual understanding, spatial reasoning, agentic tool use, and embodied-AI research. It combines a Qwen3.5-9B language backbone with a C-RADIOv4-H vision encoder, supports text, images and videos with any-resolution visual input. Within the 10B-scale general-purpose VLMs compared in this release blog, ZDTaichu5.0-9B retains first-tier general visual understanding while supporting spatial reasoning, high-level embodied VLM reasoning, and agent tasks under the reported evaluation settings. Rather than trading broad visual competence for specialization, it layers a more comprehensive spatial, embodied, and agent capability profile on… Open weights [View model](https://savrn.com/models/zdtaichu5-0-9b-gguf) Model · Text generation ### [ZGCM-1-7B-4bits-MLX](https://savrn.com/models/zgcm-1-7b-4bits-mlx) [Wu Alpha](https://savrn.com/model-publishers/alphaoxo) Open weights 7.4B parameters 262,144 tokens mlx [View model](https://savrn.com/models/zgcm-1-7b-4bits-mlx) Model ### [ZIPFORMER_VI](https://savrn.com/models/zipformer-vi) [Mạc Phúc Vinh](https://savrn.com/model-publishers/macvinh) Open weights [View model](https://savrn.com/models/zipformer-vi) Model ### [ZImage_Base_LoRas](https://savrn.com/models/zimage-base-loras) [Zsur](https://savrn.com/model-publishers/zabin) Open weights mit [View model](https://savrn.com/models/zimage-base-loras) Model ### [ZOZ-Function-Master-3B-LongContext-GGUF](https://savrn.com/models/zoz-function-master-3b-longcontext-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/z51722369/ZOZ-Function-Master-3B-LongContext For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/ZOZ-Function-Master-3B-LongContext-i1-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 transformers [View model](https://savrn.com/models/zoz-function-master-3b-longcontext-gguf) Model ### [ZOZ-Function-Master-3B-LongContext-i1-GGUF](https://savrn.com/models/zoz-function-master-3b-longcontext-i1-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) weighted/imatrix quants of https://huggingface.co/z51722369/ZOZ-Function-Master-3B-LongContext For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/ZOZ-Function-Master-3B-LongContext-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 transformers [View model](https://savrn.com/models/zoz-function-master-3b-longcontext-i1-gguf) ## 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.