# Models, Page 308 of 489 Source: https://savrn.com/models/page/308 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. 11,734Models 3,323Datasets 288Papers 7,150Publishers 12,282Sourced relationships Updated 2026-10-20 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,441](https://savrn.com/models?task=Text+generation) - [Image and text to text530](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction351](https://savrn.com/models?task=Feature+extraction) - [Robotics345](https://savrn.com/models?task=Robotics) - [Text classification310](https://savrn.com/models?task=Text+classification) - [Text to image222](https://savrn.com/models?task=Text+to+image) - [Speech recognition175](https://savrn.com/models?task=Speech+recognition) - [Audio classification158](https://savrn.com/models?task=Audio+classification) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Text to speech137](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 learning95](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation89](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) - [Image to text59](https://savrn.com/models?task=Image+to+text) - [Video classification59](https://savrn.com/models?task=Video+classification) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - [transformers2,829](https://savrn.com/models?library=transformers) - [peft355](https://savrn.com/models?library=peft) - [diffusers196](https://savrn.com/models?library=diffusers) - [pytorch184](https://savrn.com/models?library=pytorch) - [gguf172](https://savrn.com/models?library=gguf) - [mlx145](https://savrn.com/models?library=mlx) - [lerobot141](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [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) - [onnx39](https://savrn.com/models?library=onnx) - [vllm38](https://savrn.com/models?library=vllm) - [diffusion-single-file28](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) - [openvino20](https://savrn.com/models?library=openvino) - [exllamav318](https://savrn.com/models?library=exllamav3) - [coreml17](https://savrn.com/models?library=coreml) - [litert16](https://savrn.com/models?library=litert) - [openpi16](https://savrn.com/models?library=openpi) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,664](https://savrn.com/models?modality=Other) - [Text2,658](https://savrn.com/models?modality=Text) - [Image and text589](https://savrn.com/models?modality=Image+and+text) - [Image582](https://savrn.com/models?modality=Image) - [Audio488](https://savrn.com/models?modality=Audio) - [Control440](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-2.03,175](https://savrn.com/models?license=apache-2.0) - [mit1,275](https://savrn.com/models?license=mit) - [other723](https://savrn.com/models?license=other) - [cc-by-nc-4.0200](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0153](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause107](https://savrn.com/models?license=bsd-3-clause) - [gemma63](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.050](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) - [unknown29](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-3.017](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-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 weights11,363](https://savrn.com/models?access=open_access) - [Access requested at publisher371](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher304](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan101](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai100](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 Pu71](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) - [Yurun Yuan55](https://savrn.com/models?publisher=yuruny) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [BAAR46](https://savrn.com/models?publisher=aixk) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [Yurun Yuan44](https://savrn.com/models?publisher=yyuan244) [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) 11,734 models, sorted by most downloaded. Model ### [crime-laya-5000](https://savrn.com/models/crime-laya-5000) [Leo Loss](https://savrn.com/model-publishers/leloss) Open weights 421M parameters [View model](https://savrn.com/models/crime-laya-5000) Model ### [crime_vision](https://savrn.com/models/crime-vision) [Poorniiiii](https://savrn.com/model-publishers/poorniiiii) Open weights [View model](https://savrn.com/models/crime-vision) Model ### [crivo-150m-web](https://savrn.com/models/crivo-150m-web) [Yann](https://savrn.com/model-publishers/dizyann) ONNX export of tardellirs/crivo-150m, retaining the trained ModernBERT encoder, question-type embedding, and option scorer. This is a format conversion and quantization of the original model, with no additional training. Credit for the original architecture, weights, and training belongs to the original authors. The graph reads one sequence per question, gathers hidden states at each [OPT] marker, adds the question-type embedding, and returns one logit per option. Apply softmax across those logits. The tokenizer is included in this repository. The TypeScript loader chooses INT8 on CPU/WASM and FP32 on GPU. Files download from the Hub and run locally; no hosted inference service is required.… Open weights cc-by-nc-4.0 7,999 tokens transformers.js [View model](https://savrn.com/models/crivo-150m-web) Model · Image segmentation ### [cross-channel-nuclei-from-cellmask-cpsam](https://savrn.com/models/cross-channel-nuclei-from-cellmask-cpsam) [Einar Olafsson](https://savrn.com/model-publishers/einarolafsson) Segments nuclei from the host cell mask channel alone — no nuclear stain required. A cross-channel model: it is given the cell image and predicts where the nuclei are, freeing the DAPI/Hoechst channel for another marker. This model is distributed through the spaCR Model Zoo. spaCR is an open-source package for spatial phenotype analysis of CRISPR screens and microscopy images. Launch the GUI and open the Model Zoo: Find Cross-channel nuclei-from-cellmask (Cellpose-SAM) in the model list and press Download. The Model Zoo verifies the checkpoint's SHA-256 after download, so a truncated or substituted file is rejected rather than silently used. Point spaCR's mask generation at the downloaded… Open weights mit spacr [View model](https://savrn.com/models/cross-channel-nuclei-from-cellmask-cpsam) Model ### [cross-distill-cart](https://savrn.com/models/cross-distill-cart) [Sehyun Park](https://savrn.com/model-publishers/sehyun734) Open weights [View model](https://savrn.com/models/cross-distill-cart) Model ### [cross-modal-fusion-int8](https://savrn.com/models/cross-modal-fusion-int8) [Destiny Clark](https://savrn.com/model-publishers/dxclark36) This is an exploratory note for Cross Modal Fusion. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with papernotes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds… Open weights cc-by-4.0 49,600 parameters 128 tokens [View model](https://savrn.com/models/cross-modal-fusion-int8) Model ### [cross-modal-fusion-survey](https://savrn.com/models/cross-modal-fusion-survey) [Wendy Nugroho](https://savrn.com/model-publishers/wendynugroho) This repository contains a working research note about Cross Modal Fusion. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with notes.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should… Open weights mit 24,832 parameters 128 tokens [View model](https://savrn.com/models/cross-modal-fusion-survey) Model ### [cross-unlearning-case6-qwen35-4b-ga](https://savrn.com/models/cross-unlearning-case6-qwen35-4b-ga) [Jianghao Niu](https://savrn.com/model-publishers/ssadqdsacf) Open weights 4.5B parameters 262,144 tokens [View model](https://savrn.com/models/cross-unlearning-case6-qwen35-4b-ga) Model ### [cross-unlearning-case6-qwen35-4b-ga-difference](https://savrn.com/models/cross-unlearning-case6-qwen35-4b-ga-difference) [Jianghao Niu](https://savrn.com/model-publishers/ssadqdsacf) Open weights [View model](https://savrn.com/models/cross-unlearning-case6-qwen35-4b-ga-difference) Model ### [cryoVERSE](https://savrn.com/models/cryoverse) [Westlake Repl](https://savrn.com/model-publishers/westlake-repl) Open weights mit [View model](https://savrn.com/models/cryoverse) Model ### [crystal-9](https://savrn.com/models/crystal-9) [Lewis Moten](https://savrn.com/model-publishers/lewismoten) Open weights apache-2.0 [View model](https://savrn.com/models/crystal-9) Model ### [cs224n-matching](https://savrn.com/models/cs224n-matching) [Hu Siyuan](https://savrn.com/model-publishers/siyuanhu) A small Cnn Transformer implementation for Matching, packaged with an explicit configuration and an initialization checkpoint. The small variant is a reproducible starting point, not a trained model release. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adam with a cosine schedule. These are starting values in the script, not… Open weights bsd-3-clause 16,576 parameters 512 tokens [View model](https://savrn.com/models/cs224n-matching) Model ### [cs224n-multitask88](https://savrn.com/models/cs224n-multitask88) [Laura Ferreira](https://savrn.com/model-publishers/leferreira) Working implementation of Flamingo for Multitask using a xlarge configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lion with a cosine schedule. These are starting values in the script, not evidence of a… Open weights mit 33,088 parameters 512 tokens [View model](https://savrn.com/models/cs224n-multitask88) Model ### [cs224n-ocr-freeform](https://savrn.com/models/cs224n-ocr-freeform) [Olivia K. Taylor](https://savrn.com/model-publishers/sakuramor6) This is an exploratory note for OCR Freeform. It records the intended comparison, likely confounders, and reproducibility requirements before any benchmark result is reported. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as FUNSD, SROIE, and CORD - reproducibility checks, failure modes, and open questions - topic-relevant references Start with analysis.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and raw logs. The note is intentionally… Open weights cc-by-4.0 16,576 parameters 512 tokens [View model](https://savrn.com/models/cs224n-ocr-freeform) Model ### [cs231n-contrastive](https://savrn.com/models/cs231n-contrastive) [Hugorichardbury](https://savrn.com/model-publishers/hugorichardbury) Open weights bsd-3-clause 49,600 parameters [View model](https://savrn.com/models/cs231n-contrastive) Model ### [cs231n-contrastive-learning-2023](https://savrn.com/models/cs231n-contrastive-learning-2023) [Alvaro Martin](https://savrn.com/model-publishers/bcnmartin96) A structured set of research notes on Contrastive Learning, with concrete evaluation references and open questions. Plans and hypotheses are kept separate from completed results. - the scope of the research question and likely confounders - a proposed comparison with matched baselines - concrete evaluation context such as task-appropriate public benchmarks named in the main note - reproducibility checks, failure modes, and open questions - topic-relevant references Start with analysis.md for the full note. Sections labeled as plans or hypotheses should not be interpreted as experimental results. If results are added later, they should include dataset versions, commands, seeds, hardware, and… Open weights mit 24,832 parameters 128 tokens [View model](https://savrn.com/models/cs231n-contrastive-learning-2023) Model ### [cs231n-generation](https://savrn.com/models/cs231n-generation) [Alice S. Gomes](https://savrn.com/model-publishers/alicegomes) Working implementation of Perceiver for Generation using a large configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adafactor with a constant warmup schedule. These are starting values in the script, not… Open weights bsd-3-clause 49,600 parameters 256 tokens [View model](https://savrn.com/models/cs231n-generation) Model ### [cs2881-queen-of-hearts-rlaif](https://savrn.com/models/cs2881-queen-of-hearts-rlaif) [Kathy Jia](https://savrn.com/model-publishers/kkath-y) Access requested at publisher [View model](https://savrn.com/models/cs2881-queen-of-hearts-rlaif) Model ### [cs329x-hw1-dpo](https://savrn.com/models/cs329x-hw1-dpo) [Yucheng Yuan](https://savrn.com/model-publishers/tomyuanyucheng) 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 0.10.0 Open weights peft [View model](https://savrn.com/models/cs329x-hw1-dpo) Model ### [cs329x-hw1-dpo-lora](https://savrn.com/models/cs329x-hw1-dpo-lora) [Jessie Ou](https://savrn.com/model-publishers/jess-0) This is the model card of a transformers model that has been pushed on the Hub. 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). Open weights transformers [View model](https://savrn.com/models/cs329x-hw1-dpo-lora) Model · Text generation ### [cs329x-hw1-dpo-qwen25-20261010](https://savrn.com/models/cs329x-hw1-dpo-qwen25-20261010) [Austin Chen](https://savrn.com/model-publishers/chenchen2005) LoRA adapter trained from a fresh Qwen/Qwen2.5-1.5B-Instruct checkpoint with TRL DPO. The fictional persona prioritizes evidence, honesty, concise practical answers, and respect. 18 course PRISM tasks; 378 ordered preference pairs including an edited best answer. Learning rate 5e-05; 2 epochs; batch size 1; accumulation 8; beta 0.1. 4-bit NF4 base; LoRA rank 8, alpha 16, dropout 0.05; seed 329. Rankings and edited answers were drafted with AI assistance for a user-selected persona. User review status is recorded in the accompanying preference-label provenance file. These labels are persona preferences, not a representative human preference survey. The notebook compares base and DPO… Open weights apache-2.0 peft [View model](https://savrn.com/models/cs329x-hw1-dpo-qwen25-20261010) Model ### [cs329x_hw1](https://savrn.com/models/cs329x-hw1) [Caroline Choi](https://savrn.com/model-publishers/cchoi1) Open weights [View model](https://savrn.com/models/cs329x-hw1) Model ### [cs336-a4-unfiltered-25k](https://savrn.com/models/cs336-a4-unfiltered-25k) [Virat Garg](https://savrn.com/model-publishers/viratgarg) Open weights [View model](https://savrn.com/models/cs336-a4-unfiltered-25k) Model ### [cs781-p1-runs](https://savrn.com/models/cs781-p1-runs) [Dev Sachan](https://savrn.com/model-publishers/d-230353) Open weights [View model](https://savrn.com/models/cs781-p1-runs) ## 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.