# Models, Page 149 of 537 Source: https://savrn.com/models/page/149 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. 12,870Models 3,655Datasets 291Papers 7,691Publishers 12,980Sourced relationships Updated 2026-10-24 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,582](https://savrn.com/models?task=Text+generation) - [Image and text to text588](https://savrn.com/models?task=Image+and+text+to+text) - [Robotics403](https://savrn.com/models?task=Robotics) - [Feature extraction400](https://savrn.com/models?task=Feature+extraction) - [Text classification335](https://savrn.com/models?task=Text+classification) - [Text to image236](https://savrn.com/models?task=Text+to+image) - [Speech recognition190](https://savrn.com/models?task=Speech+recognition) - [Text to speech148](https://savrn.com/models?task=Text+to+speech) - [Image classification140](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) - [Reinforcement learning102](https://savrn.com/models?task=Reinforcement+learning) - [Token classification101](https://savrn.com/models?task=Token+classification) - [Zero-shot classification96](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation90](https://savrn.com/models?task=Image+segmentation) - [Object detection87](https://savrn.com/models?task=Object+detection) - [Translation82](https://savrn.com/models?task=Translation) - [Any to any79](https://savrn.com/models?task=Any+to+any) - [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 classification61](https://savrn.com/models?task=Video+classification) - [Image to text60](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers3,042](https://savrn.com/models?library=transformers) - [peft369](https://savrn.com/models?library=peft) - [diffusers210](https://savrn.com/models?library=diffusers) - [pytorch206](https://savrn.com/models?library=pytorch) - [gguf188](https://savrn.com/models?library=gguf) - [mlx172](https://savrn.com/models?library=mlx) - [lerobot154](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [timm49](https://savrn.com/models?library=timm) - [llama.cpp47](https://savrn.com/models?library=llama.cpp) - [transformers.js45](https://savrn.com/models?library=transformers.js) - [flaxspeaker44](https://savrn.com/models?library=flaxspeaker) - [onnx42](https://savrn.com/models?library=onnx) - [diffusion-single-file40](https://savrn.com/models?library=diffusion-single-file) - [vllm40](https://savrn.com/models?library=vllm) - [openpi35](https://savrn.com/models?library=openpi) - [minimax-h329](https://savrn.com/models?library=minimax-h3) - [ultralytics24](https://savrn.com/models?library=ultralytics) - [coreml23](https://savrn.com/models?library=coreml) - [exllamav323](https://savrn.com/models?library=exllamav3) - [PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino20](https://savrn.com/models?library=openvino) - [litert17](https://savrn.com/models?library=litert) - [litert-lm16](https://savrn.com/models?library=litert-lm) Modality - [Other7,428](https://savrn.com/models?modality=Other) - [Text2,883](https://savrn.com/models?modality=Text) - [Image and text648](https://savrn.com/models?modality=Image+and+text) - [Image604](https://savrn.com/models?modality=Image) - [Control505](https://savrn.com/models?modality=Control) - [Audio487](https://savrn.com/models?modality=Audio) - [Video116](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-2.03,395](https://savrn.com/models?license=apache-2.0) - [mit1,335](https://savrn.com/models?license=mit) - [other812](https://savrn.com/models?license=other) - [cc-by-nc-4.0215](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0172](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-4.056](https://savrn.com/models?license=cc-by-nc-sa-4.0) - [llama3.240](https://savrn.com/models?license=llama3.2) - [unknown33](https://savrn.com/models?license=unknown) - [agpl-3.032](https://savrn.com/models?license=agpl-3.0) - [llama3.129](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++18](https://savrn.com/models?license=openrail%2B%2B) - [creativeml-openrail-m15](https://savrn.com/models?license=creativeml-openrail-m) - [openmdw-1.115](https://savrn.com/models?license=openmdw-1.1) - [cc-by-nc-nd-4.014](https://savrn.com/models?license=cc-by-nc-nd-4.0) - [llama314](https://savrn.com/models?license=llama3) - [openrail14](https://savrn.com/models?license=openrail) - [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 weights12,434](https://savrn.com/models?access=open_access) - [Access requested at publisher436](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher328](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan116](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai105](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) - [Yurun Yuan66](https://savrn.com/models?publisher=yurunyyr) - [Google64](https://savrn.com/models?publisher=google) - [Yurun Yuan62](https://savrn.com/models?publisher=yuruny) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Christopher Subia-Waud60](https://savrn.com/models?publisher=cwaud) - [Yurun Yuan60](https://savrn.com/models?publisher=yyuan244) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Szekely53](https://savrn.com/models?publisher=attila115) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Khazzz1c53](https://savrn.com/models?publisher=khazic) - [Alfred Smith52](https://savrn.com/models?publisher=alfred8995) - [NVIDIA50](https://savrn.com/models?publisher=nvidia) - [Wang49](https://savrn.com/models?publisher=davidwdw) [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) 12,870 models, sorted by most downloaded. Model ### [Chief](https://savrn.com/models/chief) [Mujahid Hussain](https://savrn.com/model-publishers/chmujahid12619) Open weights [View model](https://savrn.com/models/chief) Model ### [Chip](https://savrn.com/models/chip) [Wenhua Huo](https://savrn.com/model-publishers/wenhuahuo) Chip is the NL2Hull ship-design decision model family. It adapts a Kev-style typed decision interface to Chinese natural-language ship-form editing requests and predicts probability distributions over ordered editing decisions. The released 0.8-billion-parameter adapter is available at wenhuahuo/chip-0.8b. The model is designed to be used with the NL2Hull repository and the Ship Design Decision Dataset. The model and evaluation protocol are described in NL2Hull: A Natural Language-Driven Constrained Ship Design Decision Framework. Open weights mit [View model](https://savrn.com/models/chip) Model · Text to image ### [Chitra-Tiny-ONNX](https://savrn.com/models/chitra-tiny-onnx) [Mrfakename](https://savrn.com/model-publishers/mrfakename) ONNX export of udgamlabs/Chitra-Tiny, a 212M-parameter text-to-image model (256×256), for onnxruntime-web on WebGPU. Used by the mrfakename/chitra-tiny-webgpu Space. Weights are stored in fp16 and cast to fp32 inside each graph: half the download, fp32 math. The DiT can't run in fp16 because its residual stream goes far past the fp16 range. Sampling (same as chitratiny.py): tokenize with padding to 77, encode the prompt and the negative prompt (empty by default), start from Gaussian noise, then Euler steps over t = linspace(1, 0, steps+1) with x += (t[i+1] - t[i]) (vuncond + g (vcond - vuncond)), then decode. Defaults: 50 steps, guidance 7. tools/exportonnx.py reproduces the files… Open weights apache-2.0 onnx [View model](https://savrn.com/models/chitra-tiny-onnx) Model ### [ChorusTIC](https://savrn.com/models/chorustic) [DMIRLAB](https://savrn.com/model-publishers/dmirlab) ChorusTIC is a classification-native foundation model for training-free univariate and multivariate time series classification. It uses labeled context examples to predict query labels without fitting a target-specific classifier or updating model parameters. ChorusTIC is evaluated in the TSC-FM time series classification foundation model benchmark. Use the time series classification leaderboard to compare matching Standard and low-shot settings, and read the evaluation protocol before interpreting aggregate accuracy, rank and coverage. ChorusTIC combines Random Subchannel Slot Concatenation with a shared dual-axis encoder to model temporal and cross-channel interactions across… Open weights apache-2.0 [View model](https://savrn.com/models/chorustic) Model · Reinforcement learning ### [Chronoquazicalar](https://savrn.com/models/chronoquazicalar) [Turbo](https://savrn.com/model-publishers/turborx) Autonomous reinforcement learning policy and value network (~9.65M parameters) for Pokémon Showdown Gen 9 Random Battles. - Embedding Dimension (dmodel): 256 - Feed-Forward Dimension (dff): 1024 - Value Head (Scalar valuation in [-1, 1]) - Opponent Prediction Head (Simultaneous action prediction) - checkpointlatest.pkl: Model parameters trained via PPO self-play and benchmark sparring. - metrics.json: Latest training metrics, update count, and turn counters. Open weights mit [View model](https://savrn.com/models/chronoquazicalar) Model ### [Cirque-12B-GGUF](https://savrn.com/models/cirque-12b-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) Open weights [View model](https://savrn.com/models/cirque-12b-gguf) Model ### [CivMarshmallowPubRunCkpts](https://savrn.com/models/civmarshmallowpubrunckpts) [Yanchen Liu](https://savrn.com/model-publishers/jamesnulliu) Open weights [View model](https://savrn.com/models/civmarshmallowpubrunckpts) Model ### [CkEGQRSsAvLDly3m](https://savrn.com/models/ckegqrssavldly3m) [15B30](https://savrn.com/model-publishers/15b30) Open weights [View model](https://savrn.com/models/ckegqrssavldly3m) Model · Text generation ### [Clarity-MR-1](https://savrn.com/models/clarity-mr-1) [Florentin](https://savrn.com/model-publishers/florentin-one) Clarity-MR-1 is Florentin One's (formerly WeMake) specialized AI model designed for complex multi-step reasoning and strategic planning tasks. As the "thinker" of the Clarity ecosystem, it excels at logical deduction, analytical problem-solving, and structured decision-making processes. This model serves as the planning and reasoning agent in Florentin One's orchestrated multi-agent AI system, addressing the "reasoning gap" in general-purpose models. Clarity-MR-1 is a first-generation reasoning architecture optimized for complex multi-step reasoning, strategic planning, and structured decision-making within enterprise workflows. It addresses the "reasoning gap" of general-purpose models by… Access requested at publisher mit transformers [View model](https://savrn.com/models/clarity-mr-1) Model ### [Classification-6Bands-Models](https://savrn.com/models/classification-6bands-models) [Danesh Selwal](https://savrn.com/model-publishers/daneshselwal) This repository hosts deep neural network checkpoints trained on the 6-band hyperspectral benchmark dataset (330x307x6, 7 classes) for conformal uncertainty quantification and high-precision remote sensing classification. - ViTUNet: Hybrid Vision Transformer and U-Net encoder-decoder architecture - baseline/models/AlexNetCNNbest.keras - baseline/models/GFNetbest.keras - baseline/models/ViTUNetbest.keras - baseline/models/AlexNetCNNfinal.keras - baseline/models/GFNetfinal.keras - baseline/models/ViTUNetfinal.keras Open weights mit keras [View model](https://savrn.com/models/classification-6bands-models) Model ### [Claude-Haiku-5.5-Qwen-5B-Thinking-GGUF](https://savrn.com/models/claude-haiku-5-5-qwen-5b-thinking-gguf) [Mon Dark](https://savrn.com/model-publishers/mondk) This model was converted to GGUF format from mondk/Qwen3-VL-4B-Thinking-Msh-Claude-Safetensors using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux). Open weights apache-2.0 transformers [View model](https://savrn.com/models/claude-haiku-5-5-qwen-5b-thinking-gguf) Model · Image and text to text ### [Clemma-12B](https://savrn.com/models/clemma-12b) [Andrew DeLisa](https://savrn.com/model-publishers/ayan4m1) This gemma4unified model was trained 2x faster with Unsloth and Huggingface's TRL library. Open weights apache-2.0 12B parameters 262,144 tokens transformers [View model](https://savrn.com/models/clemma-12b) Model · Feature extraction ### [Clip-files-ConvRot-INT8](https://savrn.com/models/clip-files-convrot-int8) [GJL](https://savrn.com/model-publishers/ussoewwin) Open weights other [View model](https://savrn.com/models/clip-files-convrot-int8) Model ### [ClondeModel](https://savrn.com/models/clondemodel) [Ran](https://savrn.com/model-publishers/rendai) Open weights [View model](https://savrn.com/models/clondemodel) Model ### [Cloreva-X1-2.1B](https://savrn.com/models/cloreva-x1-2-1b) [Muhammad Alwi](https://savrn.com/model-publishers/zulkiflii) Open weights 2.4B parameters 120,000 tokens [View model](https://savrn.com/models/cloreva-x1-2-1b) Model ### [ClueFlow-35B](https://savrn.com/models/clueflow-35b) [Wang Yicheng](https://savrn.com/model-publishers/wangyicheng992) This repository is the official Hugging Face model repository for the ClueFlow research paper. Model weights and documentation will be released soon. Open weights [View model](https://savrn.com/models/clueflow-35b) Model · Text generation ### [Clyx_0.3-NM-BASE](https://savrn.com/models/clyx-0-3-nm-base) [Syntropic](https://savrn.com/model-publishers/syntropic-clx) A new step in the Clyx project: a compact language model planned for training from scratch on Russian, English, and code. Clyx is an independent language-model project built around a custom decoder-only Transformer. The next planned BASE model targets approximately 635.51 million parameters, using Grouped-Query Attention, RoPE, RMSNorm, and SwiGLU. The intended training approach is random initialization and next-token prediction, without importing external pretrained weights. The goal is a Russian–English base model with exposure to programming languages, while keeping the architecture and training process documented. This is a BASE model project, not an instruction-tuned assistant. Any… Open weights apache-2.0 [View model](https://savrn.com/models/clyx-0-3-nm-base) Model ### [CoFlow](https://savrn.com/models/coflow) [Ziqi Jiang](https://savrn.com/model-publishers/ziqijiang) Open weights [View model](https://savrn.com/models/coflow) Model · Feature extraction ### [CoVisco-L-14](https://savrn.com/models/covisco-l-14) [Ernie Research](https://savrn.com/model-publishers/ernie-research) CoVisco-L-14 is the Stage 1 vision encoder of the CoVisco pipeline (Codec-Native Vision Encoder with Native Token Compression for Efficient Unified Visual Understanding). It encodes images and videos in a unified way and is trained by SigLIP-style distillation against two teacher branches. This repository releases only the vision-encoder weights in the safe.safetensors format, together with the minimal model code and a self-contained loader. The model is a transformers.PreTrainedModel and is loadable via frompretrained (see below). - 4D RoPE (ropetheta=10000) for spatio-temporal positions - Three projection heads (each LayerNorm + Linear, output L2-normalized): - toimagecaption → 1536-d… Open weights mit 639M parameters pytorch [View model](https://savrn.com/models/covisco-l-14) Model ### [Coffee_Real_Test](https://savrn.com/models/coffee-real-test) [Le Duc Khang](https://savrn.com/model-publishers/frd-23) Open weights [View model](https://savrn.com/models/coffee-real-test) Model ### [CogOmniControl](https://savrn.com/models/cogomnicontrol) [Hongjiyang](https://savrn.com/model-publishers/yang1232009) Access requested at publisher apache-2.0 [View model](https://savrn.com/models/cogomnicontrol) Model ### [Collected_Model](https://savrn.com/models/collected-model) [Dfrevytus Lin](https://savrn.com/model-publishers/dfrevytus) Open weights apache-2.0 [View model](https://savrn.com/models/collected-model) Model ### [Collection](https://savrn.com/models/collection) [Public LoRAs](https://savrn.com/model-publishers/publicloras) Open weights apache-2.0 [View model](https://savrn.com/models/collection) Model ### [Comfy2](https://savrn.com/models/comfy2) [Merle McDurmott](https://savrn.com/model-publishers/merlemcdurrrmott) Open weights [View model](https://savrn.com/models/comfy2) ## 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.