# Models, Page 229 of 512 Source: https://savrn.com/models/page/229 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 ### [ScoreVision](https://savrn.com/models/scorevision) [Adam Babaev](https://savrn.com/model-publishers/adamb5757) Open weights [View model](https://savrn.com/models/scorevision) Model ### [ScoreVision](https://savrn.com/models/scorevision-2) [Alan Rony](https://savrn.com/model-publishers/crimedetector) Open weights [View model](https://savrn.com/models/scorevision-2) Model ### [ScoreVision](https://savrn.com/models/scorevision-3) [Louis Robbins](https://savrn.com/model-publishers/valor0316) Open weights [View model](https://savrn.com/models/scorevision-3) Model ### [ScoreVision](https://savrn.com/models/scorevision-4) [Erik](https://savrn.com/model-publishers/ausjin) Open weights [View model](https://savrn.com/models/scorevision-4) Model ### [ScoreVision_PetrolStation](https://savrn.com/models/scorevision-petrolstation) [Bruno Fernandes](https://savrn.com/model-publishers/fernandes08) Open weights [View model](https://savrn.com/models/scorevision-petrolstation) Model ### [ScribeDrAI-ClinicalBERT](https://savrn.com/models/scribedrai-clinicalbert) [Thomas King](https://savrn.com/model-publishers/thomasking135) Open weights [View model](https://savrn.com/models/scribedrai-clinicalbert) Model · Object detection ### [SeeingTheUnseen](https://savrn.com/models/seeingtheunseen) [Christoph Praschl](https://savrn.com/model-publishers/cpraschl) Detector weights and derived bases from a controlled study of occlusion compensation in aerial wildlife monitoring, along two axes: single view vs multi view, and sensor (pixel) space vs embedding space. container it ran in, every scored metric behind the paper's tables, and a step-by-step in REPLICATION.md. Run directory names here are the run identifiers used there, so a checkpoint joins directly against metrics/.json under the arms key. Backbones are not included. DINOv3 and V-JEPA 2.1 are used frozen and unmodified — obtain them from their original sources. Only self-trained artefacts are here. Nearly everything here is cross-validated. A run directory whose name carries f belongs to… Open weights other [View model](https://savrn.com/models/seeingtheunseen) Model · Robotics ### [SemWAM-Framework-Ablation-v35-RGB-VJEPA-Geometry-Predicted-RGB](https://savrn.com/models/semwam-framework-ablation-v35-rgb-vjepa-geometry-predicted-rgb) [Yu Liu](https://savrn.com/model-publishers/rainybot) Access requested at publisher [View model](https://savrn.com/models/semwam-framework-ablation-v35-rgb-vjepa-geometry-predicted-rgb) Model · Robotics ### [SemWAM-RoboTwin-v29-v2-20261020](https://savrn.com/models/semwam-robotwin-v29-v2-20261020) [Yu Liu](https://savrn.com/model-publishers/rainybot) Access requested at publisher [View model](https://savrn.com/models/semwam-robotwin-v29-v2-20261020) Model ### [Semi-LLIE](https://savrn.com/models/semi-llie) [Extra](https://savrn.com/model-publishers/juanum) Open weights [View model](https://savrn.com/models/semi-llie) Model · Depth estimation ### [SemiSupMDE](https://savrn.com/models/semisupmde) [Amir El-Ghoussani](https://savrn.com/model-publishers/amirmael) Models for the paper Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation (CoLLAs 2024) by Amir El-Ghoussani, Julia Hornauer, Gustavo Carneiro and Vasileios Belagiannis. Code: github.com/AmirMaEl/SemiSupMDE. A single U-Net (54.6M parameters) predicts depth from one RGB image. It is trained on synthetic Virtual KITTI with ground truth and adapted to real KITTI without any KITTI labels, using a pairwise loss on source images and a perturbation-consistency loss across RandAugment views of the unlabelled target images. Both checkpoints are PyTorch state dicts. KITTI Eigen test split (697 images), Garg crop, velodyne ground truth, no median scaling: With… Open weights pytorch [View model](https://savrn.com/models/semisupmde) Model ### [SenseVoiceSmall-OV-int8](https://savrn.com/models/sensevoicesmall-ov-int8) [Yangsu](https://savrn.com/model-publishers/yangsu0423) Open weights apache-2.0 [View model](https://savrn.com/models/sensevoicesmall-ov-int8) Model ### [Shadow-Siren-14B-v2-GGUF](https://savrn.com/models/shadow-siren-14b-v2-gguf) [Remesh](https://savrn.com/model-publishers/goopr) Open weights [View model](https://savrn.com/models/shadow-siren-14b-v2-gguf) Model ### [Shadow-Siren-14B-v2-Pruned](https://savrn.com/models/shadow-siren-14b-v2-pruned) [Remesh](https://savrn.com/model-publishers/goopr) Surgically pruned 64-expert (~14.5B total) variant of Vortex5/Shadow-Siren-26B-A4B. - Sliced from 128 experts down to 64 experts across all MoE blocks. - Router gating matrix truncated cleanly to 64 rows. - Preserves original chat templates and tokenizer metadata for persistent multi-turn reasoning. - Full BF16 safetensors weights ready for fine-tuning or custom quantization. Open weights apache-2.0 14.4B parameters 262,144 tokens [View model](https://savrn.com/models/shadow-siren-14b-v2-pruned) Model ### [Shady_Fay_Krea_2_Turbo](https://savrn.com/models/shady-fay-krea-2-turbo) [AIxFun eStudio](https://savrn.com/model-publishers/aixfunestudio) Follow the license terms described in LICENSE.md. Access requested at publisher other [View model](https://savrn.com/models/shady-fay-krea-2-turbo) Model ### [Shayol](https://savrn.com/models/shayol) [Zoarag](https://savrn.com/model-publishers/zoarag) Access requested at publisher [View model](https://savrn.com/models/shayol) Model ### [Sheol](https://savrn.com/models/sheol) [Pablo Oyola](https://savrn.com/model-publishers/rexatura325) Access requested at publisher [View model](https://savrn.com/models/sheol) Model ### [ShiroRVC-Resources](https://savrn.com/models/shirorvc-resources) [Guilherme](https://savrn.com/model-publishers/shiromiya) Open weights [View model](https://savrn.com/models/shirorvc-resources) Model ### [Shreyansh-STEM-AI-7B-Final-GGUF](https://savrn.com/models/shreyansh-stem-ai-7b-final-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/shreyansh12183/Shreyansh-STEM-AI-7B-Final 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… Open weights cc-by-nc-4.0 transformers [View model](https://savrn.com/models/shreyansh-stem-ai-7b-final-gguf) Model ### [SiT-Complementary](https://savrn.com/models/sit-complementary) [JINYUAN HU](https://savrn.com/model-publishers/bluesourcejy) Open weights [View model](https://savrn.com/models/sit-complementary) Model · Text classification ### [Sieve-9B-Plus](https://savrn.com/models/sieve-9b-plus) [Sthānika AI](https://savrn.com/model-publishers/sthanika-ai) Sieve-9B-Plus is a decision model from the Sieve family. You give it a piece of text or JSON (the state) and typed questions, and it returns a calibrated probability for every option of every question. It reads the state once, scores the options directly, and never generates text. It is a LoRA adapter (rank 32) and a pointer head on Qwen/Qwen3.5-9B (revision c2022362), and it stores one calibration temperature per question category. The code is at - Backbone. Only the text part of Qwen3.5-9B is used; the model cannot produce text. - Readout. A pointer head scores the end of each option against the decision point. A calibrated softmax gives the probabilities. - Isolation. Each question runs… Open weights apache-2.0 peft [View model](https://savrn.com/models/sieve-9b-plus) Model ### [Sigmo-backend](https://savrn.com/models/sigmo-backend) [Biniyam Kebede Amede](https://savrn.com/model-publishers/bini-k) Production FastAPI service for the Sigmo V2 motor-condition-monitoring system (MCSA — Motor Current Signature Analysis), carrying the ACTIVE classifier bundle v6.20260925.045900 (13-class fault taxonomy, accuracy 0.8995 / macro-F1 0.8475 held-out). - Docker (any builder): docker build. — the Dockerfile installs runtime dependencies, copies the code, and fetches the two large model artifacts (UBJSON booster + ISO gate) from this repository's pinned revision. current policy; appport: 7860). CORS is open () by default so external frontends (e.g. Replit) can fetch immediately; lock it down by setting CORSALLOWORIGINS. - DATABASEURL — Supabase PostgreSQL connection string. Without it the service… Open weights [View model](https://savrn.com/models/sigmo-backend) Model · Image and text to text ### [Signal-3.8-27B-FP8](https://savrn.com/models/signal-3-8-27b-fp8) [Vwdubb](https://savrn.com/model-publishers/vwdubb) This is Qwen3.8-27B that gets to the answer faster. AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-27B designed for lower generation latency and better token efficiency. On our held-out general-prompt evaluation, Signal produces 57% fewer answer tokens and uses 52% fewer thinking tokens, while matching or improving the measured answer quality of the base model. The percentages above were measured on the first release. The weights updated on 2026-09-13 trade a little of that reduction for stability; their re-measurement on the same prompt set is in progress and will replace these numbers. The result is substantially faster end-to-end generation: on typical chat prompts… Open weights apache-2.0 27.8B parameters 262,144 tokens transformers [View model](https://savrn.com/models/signal-3-8-27b-fp8) Model ### [SimpleMemVLN-R2R-RxR15deg-Window8-Text-DualLane-FromBase](https://savrn.com/models/simplememvln-r2r-rxr15deg-window8-text-duallane-frombase) [Anh Dao](https://savrn.com/model-publishers/anhdao69) Completed two-epoch joint R2R + English-guide RxR 15-degree training run. epoch-1 contains update 3,852; epoch-2 contains the final update 7,704. Epoch 1 is a mid-schedule snapshot; epoch 2 completes the planned schedule. No navigation evaluation result is claimed for these weights. Original Qwen3.5-4B base revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a. https://github.com/anhdao69/SimpleMemVLN/tree/streamingtextdual. 30,815 episodes; 3,128,624 action steps per dataset pass before distributed tail padding. Two epochs, 7,704 updates; 232 warmup updates; seed 429. Four H100 GPUs, one complete episode per rank per microstep, gradient accumulation 2, effective global batch 8. BF16, ZeRO-2… Open weights [View model](https://savrn.com/models/simplememvln-r2r-rxr15deg-window8-text-duallane-frombase) ## 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.