# Models, Page 238 of 512 Source: https://savrn.com/models/page/238 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 ### [TrustMed-RL-gigpo-seed2-v7-meannorm-Qwen3VL-8B](https://savrn.com/models/trustmed-rl-gigpo-seed2-v7-meannorm-qwen3vl-8b) [Jiao](https://savrn.com/model-publishers/andyjoecn) Open weights [View model](https://savrn.com/models/trustmed-rl-gigpo-seed2-v7-meannorm-qwen3vl-8b) Model ### [TrustMed-RL-grpo-seed1-v7-Qwen3VL-8B](https://savrn.com/models/trustmed-rl-grpo-seed1-v7-qwen3vl-8b) [Jiao](https://savrn.com/model-publishers/andyjoecn) Open weights [View model](https://savrn.com/models/trustmed-rl-grpo-seed1-v7-qwen3vl-8b) Model ### [TrustMed-RL-grpo-seed2-v7-Qwen3VL-8B](https://savrn.com/models/trustmed-rl-grpo-seed2-v7-qwen3vl-8b) [Jiao](https://savrn.com/model-publishers/andyjoecn) Open weights [View model](https://savrn.com/models/trustmed-rl-grpo-seed2-v7-qwen3vl-8b) Model · Text generation ### [TunCHAT-V0.2-GGUF](https://savrn.com/models/tunchat-v0-2-gguf) [Dali dali](https://savrn.com/model-publishers/dali20266) Modèle vocal & conversationnel tunisien (Derja) quantifié au format GGUF. Compatible avec Ollama, llama.cpp, LM Studio, Jan, et mobiles (S24 Ultra). Open weights apache-2.0 [View model](https://savrn.com/models/tunchat-v0-2-gguf) Model ### [Turbo8-LoRA-Qwen-Image-2.1](https://savrn.com/models/turbo8-lora-qwen-image-2-1) [Chris Scott](https://savrn.com/model-publishers/chriswritescode) Open weights [View model](https://savrn.com/models/turbo8-lora-qwen-image-2-1) Model · Robotics ### [TurboVLA-Libero-0.22B](https://savrn.com/models/turbovla-libero-0-22b) [Mithilesh Nakade](https://savrn.com/model-publishers/man1103) This repository contains weights or code derived from the TurboVLA foundational architecture developed by Hugging Face and the TurboVLA Authors. Open weights apache-2.0 450M parameters [View model](https://savrn.com/models/turbovla-libero-0-22b) Model · Text to image ### [UBIG-AnnieMay-Base](https://savrn.com/models/ubig-anniemay-base) [Uncle Bane](https://savrn.com/model-publishers/unclebane) Open weights mit [View model](https://savrn.com/models/ubig-anniemay-base) Model · Text to image ### [UBIG-Zero](https://savrn.com/models/ubig-zero) [Uncle Bane](https://savrn.com/model-publishers/unclebane) Open weights mit [View model](https://savrn.com/models/ubig-zero) Model ### [UBqtcDPIkbayeWj0](https://savrn.com/models/ubqtcdpikbayewj0) [14A5](https://savrn.com/model-publishers/14a5) Open weights [View model](https://savrn.com/models/ubqtcdpikbayewj0) Model · Text classification ### [UIUC-CS546-HW1-HC3-Detector](https://savrn.com/models/uiuc-cs546-hw1-hc3-detector) [EC](https://savrn.com/model-publishers/xygugugu) Epochs: 5; Batch size: 32; Optimizer: AdamW; Max input token length: 256. Open weights mit 23M parameters 512 tokens transformers [View model](https://savrn.com/models/uiuc-cs546-hw1-hc3-detector) Model ### [UIcO0mVAF2iGFK5F](https://savrn.com/models/uico0mvaf2igfk5f) [14A3](https://savrn.com/model-publishers/14a3) Open weights [View model](https://savrn.com/models/uico0mvaf2igfk5f) Model ### [UNet-steering](https://savrn.com/models/unet-steering) [Junda Wu](https://savrn.com/model-publishers/joshuaw1997) Open weights other [View model](https://savrn.com/models/unet-steering) Model · Fill mask ### [UltraBERT-150M-ONNX](https://savrn.com/models/ultrabert-150m-onnx) [Pastel Labs](https://savrn.com/model-publishers/pastel-labs) ONNX export of dmis-lab/UltraBERT-150M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate plus logits from the masked-language-model head. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared mean-pooled last hidden states with cosine similarity. Fifteen texts are short (3 to 25… Open weights apache-2.0 transformers.js [View model](https://savrn.com/models/ultrabert-150m-onnx) Model · Fill mask ### [UltraBERT-380M-ONNX](https://savrn.com/models/ultrabert-380m-onnx) [Pastel Labs](https://savrn.com/model-publishers/pastel-labs) ONNX export of dmis-lab/UltraBERT-380M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate plus logits from the masked-language-model head. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared mean-pooled last hidden states with cosine similarity. Fifteen texts are short (3 to 25… Open weights apache-2.0 transformers.js [View model](https://savrn.com/models/ultrabert-380m-onnx) Model · Feature extraction ### [UltraBERT-Embed-150M-ONNX](https://savrn.com/models/ultrabert-embed-150m-onnx) [Pastel Labs](https://savrn.com/model-publishers/pastel-labs) ONNX export of dmis-lab/UltraBERT-Embed-150M for ONNX Runtime and Transformers.js. All credit for the model goes to dmis-lab. The weights are theirs, under the same Apache-2.0 license. We only converted the format. Inputs are inputids and attentionmask, with dynamic batch size and sequence length. Output is lasthiddenstate. The sliding-window (128 tokens) and full-attention layers are built from the attention mask inside the graph, so padding and any length work. We ran 20 English texts through the original PyTorch model and through each ONNX file, then compared the sentence embedding (first-token pooling, as in the sentence-transformers config) with cosine similarity. Fifteen texts are… Open weights apache-2.0 transformers.js [View model](https://savrn.com/models/ultrabert-embed-150m-onnx) Model · Image to image ### [UltraTex-Qwen-Image-2.1](https://savrn.com/models/ultratex-qwen-image-2-1) [Orwelious Maximilous](https://savrn.com/model-publishers/orwelian84) This is a port of UltraTex (MIT, Yibo Zhang) to the Qwen-Image 2.1 backbone, trained by Atlas 3D (orwelian84). The released UltraTex weights only cover FLUX.1-dev and FLUX.2-Klein, so this is, to our knowledge, the first Qwen-Image multi-view texture LoRA. Code: github.com/Atlas3DSS/UltraTex (fork of upstream with the Qwen-Image 2.1 and Krea 2 backbones on the default branch). The code/ folder here is a snapshot of the same files. The method is UltraTex's. This release is the backbone port (code/) plus the trained weights (qwen21/) and samples. - 15,000 steps, 1024 px per view, batch 1 - lr 5e-5, constant; bf16; seed 42 - Sparse attention top-k 0.2 - Trained on G-buffer TexVerse… Open weights mit [View model](https://savrn.com/models/ultratex-qwen-image-2-1) Model ### [Ultralytics](https://savrn.com/models/ultralytics-2) [Rich Riddle](https://savrn.com/model-publishers/ellipsesmark) Open weights [View model](https://savrn.com/models/ultralytics-2) Model ### [UnholyDesiresStudio](https://savrn.com/models/unholydesiresstudio) [Prometheus Project](https://savrn.com/model-publishers/prometheusexe) Open weights [View model](https://savrn.com/models/unholydesiresstudio) Model ### [UniMate](https://savrn.com/models/unimate) [Sha](https://savrn.com/model-publishers/lyxinbb) Open weights cc-by-nc-4.0 pytorch [View model](https://savrn.com/models/unimate) Model · Text generation ### [UnionMaritime-LoRA](https://savrn.com/models/unionmaritime-lora) [Crowther Labs](https://savrn.com/model-publishers/crowtherlabs) Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - PEFT 0.21.0 Open weights peft [View model](https://savrn.com/models/unionmaritime-lora) Model ### [Urubu](https://savrn.com/models/urubu) [RAFHAEL Henrique Totti Garcia de Oliveira](https://savrn.com/model-publishers/rafhael0506) Open weights [View model](https://savrn.com/models/urubu) Model · Text generation ### [VANGUARD-MODEL-V1](https://savrn.com/models/vanguard-model-v1) [User](https://savrn.com/model-publishers/md-asif) VANGUARD-MODEL-V1 Evidence-Grounded Financial Intelligence for Enterprise Applications VANGUARD-MODEL-V1 is a financial domain-adapted language model based on Qwen3-4B-Instruct-2507, fine-tuned using Quantized Low-Rank Adaptation (QLoRA) for financial question answering and document reasoning. Designed for integration into enterprise financial intelligence systems, the model aims to help applications answer questions about financial statements, annual reports, SEC filings, corporate disclosures, and financial tables using supplied evidence. The model is intended to operate within a retrieval-augmented generation (RAG) pipeline, with source retrieval, citation validation, and deterministic… Open weights mit 4B parameters 262,144 tokens peft [View model](https://savrn.com/models/vanguard-model-v1) Model ### [VFPD](https://savrn.com/models/vfpd) [Vayan](https://savrn.com/model-publishers/vayan0) Open weights apache-2.0 [View model](https://savrn.com/models/vfpd) Model ### [VKP-Omni-2B-GGUF](https://savrn.com/models/vkp-omni-2b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/NandiAi/VKP-Omni-2B 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/vkp-omni-2b-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.