# Models, Page 281 of 501 Source: https://savrn.com/models/page/281 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. 12,010Models 3,413Datasets 288Papers 7,298Publishers 12,421Sourced relationships Updated 2026-10-21 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,467](https://savrn.com/models?task=Text+generation) - [Image and text to text542](https://savrn.com/models?task=Image+and+text+to+text) - [Robotics370](https://savrn.com/models?task=Robotics) - [Feature extraction352](https://savrn.com/models?task=Feature+extraction) - [Text classification326](https://savrn.com/models?task=Text+classification) - [Text to image224](https://savrn.com/models?task=Text+to+image) - [Speech recognition176](https://savrn.com/models?task=Speech+recognition) - [Audio classification160](https://savrn.com/models?task=Audio+classification) - [Text to speech141](https://savrn.com/models?task=Text+to+speech) - [Image classification138](https://savrn.com/models?task=Image+classification) - [Sentence similarity103](https://savrn.com/models?task=Sentence+similarity) - [Token classification97](https://savrn.com/models?task=Token+classification) - [Reinforcement learning96](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 - [transformers2,877](https://savrn.com/models?library=transformers) - [peft357](https://savrn.com/models?library=peft) - [diffusers199](https://savrn.com/models?library=diffusers) - [pytorch189](https://savrn.com/models?library=pytorch) - [gguf175](https://savrn.com/models?library=gguf) - [lerobot148](https://savrn.com/models?library=lerobot) - [mlx148](https://savrn.com/models?library=mlx) - [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) - [openpi35](https://savrn.com/models?library=openpi) - [diffusion-single-file28](https://savrn.com/models?library=diffusion-single-file) - [minimax-h326](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) - [exllamav319](https://savrn.com/models?library=exllamav3) - [coreml18](https://savrn.com/models?library=coreml) - [litert17](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,847](https://savrn.com/models?modality=Other) - [Text2,701](https://savrn.com/models?modality=Text) - [Image and text601](https://savrn.com/models?modality=Image+and+text) - [Image586](https://savrn.com/models?modality=Image) - [Audio495](https://savrn.com/models?modality=Audio) - [Control466](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-2.03,220](https://savrn.com/models?license=apache-2.0) - [mit1,282](https://savrn.com/models?license=mit) - [other743](https://savrn.com/models?license=other) - [cc-by-nc-4.0201](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0155](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-4.051](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 weights11,627](https://savrn.com/models?access=open_access) - [Access requested at publisher383](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher313](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan106](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai101](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 Yuan63](https://savrn.com/models?publisher=yurunyyr) - [Firza Hadzami61](https://savrn.com/models?publisher=firzahdzm) - [Zeyuan Yang59](https://savrn.com/models?publisher=miiche) - [Yurun Yuan59](https://savrn.com/models?publisher=yuruny) - [Yanggangu57](https://savrn.com/models?publisher=yanggangu) - [Hongao56](https://savrn.com/models?publisher=cisco1963) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Szekely52](https://savrn.com/models?publisher=attila115) - [Yurun Yuan51](https://savrn.com/models?publisher=yyuan244) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Christopher Subia-Waud48](https://savrn.com/models?publisher=cwaud) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [NVIDIA48](https://savrn.com/models?publisher=nvidia) - [BAAR46](https://savrn.com/models?publisher=aixk) [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) 12,010 models, sorted by most downloaded. Model ### [chessumi-analysis-assets](https://savrn.com/models/chessumi-analysis-assets) [Fozz](https://savrn.com/model-publishers/stevefozz) This public repository contains immutable binary artifacts selected by the ChessUmi Analysis application manifest. Each release must be uploaded as one commit, anonymously verified with scripts/verify-engine-asset-release.mjs, then pinned by its full commit SHA in the application manifest. See the bundled NOTICE.md and licenses directory for corresponding-source and license information. Open weights [View model](https://savrn.com/models/chessumi-analysis-assets) Model · Image classification ### [chest-xray-multilabel-classifier](https://savrn.com/models/chest-xray-multilabel-classifier) [Samer Ahmed](https://savrn.com/model-publishers/samer17) medical computer vision project using an ImageNet-pretrained ResNet-50 to classify 14 thoracic conditions from chest X-ray images. The model was trained on the NIH ChestX-ray14 dataset with patient-level data splitting, class-weighted binary cross-entropy, and experiment tracking through Weights & Biases. The model generates independent prediction scores for 14 thoracic conditions: Because this is a multi-label classification task, multiple conditions may be associated with the same X-ray. The model produces 14 logits, converted into scores using the sigmoid function. These scores are not clinically calibrated probabilities. The classifier uses an ImageNet-pretrained ResNet-50 backbone with… Open weights mit [View model](https://savrn.com/models/chest-xray-multilabel-classifier) Model · Text generation ### [chia-governor-triage-7b](https://savrn.com/models/chia-governor-triage-7b) [Venky](https://savrn.com/model-publishers/emailvenky) A LoRA adapter for Qwen2.5-Coder-7B-Instruct that predicts, from the code alone, whether a Verilog design change will fail its test bench before any simulator is run. It exists to sit in front of the expensive evaluation stage of an agentic hardware design loop. Simulation is slow and costly, and many agent proposals are simply wrong. This model triages them. Every frontier model tested carries the same wrong prior: that any edit to working code is probably a bug. That produces near-perfect recall and poor precision, so the gate throws away a large share of perfectly good designs. This adapter was trained on data containing verified-harmless edits alongside real faults, which attacks that… Open weights bsd-3-clause peft [View model](https://savrn.com/models/chia-governor-triage-7b) Model · Text generation ### [chime-sms-2m](https://savrn.com/models/chime-sms-2m) [Luke Steuber](https://savrn.com/model-publishers/lukeslp) On-device next-word prediction for texts and chat. A small English next-word and word-completion model trained by Luke Steuber for LocalType, an Android keyboard. It has 2,025,114 parameters, a 16,384-token word-level vocabulary, and a 2.22 MB int8 binary. It suggests words; it is not a chat model or an independently validated autocorrect system. The model reads the current sentence, with up to 63 context tokens plus a start token. Longer contexts move in 32-token increments. A coupled input-forget-gate LSTM projects 670 hidden units to 96 dimensions and shares its input/output embedding. Kotlin inference requires no external model runtime; the included Python reference requires only NumPy.… Open weights cc-by-sa-4.0 [View model](https://savrn.com/models/chime-sms-2m) Model · Video classification ### [chimpact-thesis-models](https://savrn.com/models/chimpact-thesis-models) [Camila Danna Ticona Sánchez](https://savrn.com/model-publishers/rengatitos) Modelos de tesis sobre ChimpACT Release v1 (23 comportamientos, multi-etiqueta, split oficial por video). Cada carpeta /models/ / contiene best.pt, spec.json (thresholds por clase), metrics.json (métricas de test e historial por época) y trainingcurve.png. Dashboard y API: https://huggingface.co/spaces/Rengatitos/chimpact-thesis-dashboard Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/chimpact-thesis-models) Model · Zero-shot classification ### [chinese-decision-2b](https://savrn.com/models/chinese-decision-2b) [Quincyqiang](https://savrn.com/model-publishers/quincyqiang) Chinese Decision 是一个面向中文场景的轻量级决策模型。它不聊天、不生成文本：给定上下文和一组固定候选项，一次前向直接返回每个候选项的概率，适合意图识别、工单分流、内容审核、数据打标、给模型输出打分、智能体动作选择等"有限选项判断"任务。 - 一次前向，零输出 token：读取最后位置上候选字母 A–Z 的 logits，softmax 后即为概率。 - 三种题型：noul（是/否）、choice（单选）、score（有序等级，返回期望分值）。 - 系列中准确率最高：CJ-Bench 全量整体 ACC 84.11%，医疗 / 法律 / 金融平均 78.25%，比 chinese-decision-0.8b 分别高 1.01 / 1.35 个百分点。 - 概率经过校准：附带在独立校准划分上拟合的温度，整体 ECE 0.64%。 更新于 2026-10-17。BF16 合并模型已完成全量 CJ-Bench、保存/重载和实际 HTTP 三题型/双路由验证，并已在 Hugging Face 与 ModelScope 公开发布。由本模型直接转换的 Q80 GGUF 也已完成全量 CJ-Bench 对照，见 2B Q80 模型卡。 训练数据版本 a3963a61f71e00051a80721e587d720d8926e8e7。最终 adapter SHA256 为 9b55e8eda00997544b76fa945d890605c3acae4199041c197ef5a09275142a7c；合并权重 SHA256 为… Open weights apache-2.0 1.9B parameters 262,144 tokens transformers [View model](https://savrn.com/models/chinese-decision-2b) Model · Zero-shot classification ### [chinese-decision-2b-gguf](https://savrn.com/models/chinese-decision-2b-gguf) [Quincyqiang](https://savrn.com/model-publishers/quincyqiang) Chinese Decision 2B 的 CPU 部署包，保留相同提示、候选概率计算与校准温度。 本仓库是 Chinese Decision 2B 的 GGUF Q80 导出，用于 llama.cpp / llama-cpp-python 推理。原版是一个中文决策模型：给定上下文和一组固定候选项（单题 2–26 个；类别更多的 choice 问题可用服务端的 /v1/tournament 分组淘汰），一次前向直接返回每个候选项的概率，不生成任何 token；支持 noul（是/否）、choice（单选）、score（有序等级）三种题型，覆盖通用、医疗、法律、金融四个领域。 - 无需 PyTorch 和 GPU：llama-cpp-python 即可在 CPU 上运行，也可用 CUDA 版 llama.cpp 跑在 GPU 上。 - 体积约为原版的一半：Q80 为 2.01 GB，原版 safetensors 为 3.76 GB。 - 内存占用更低：同一台服务器上，CPU 推理进程内存观测最大值 3.34 GiB，Transformers FP32 为 11.74 GiB。 - 精度基本不变：CJ-Bench 全量 307,900 条上准确率 84.1094%（BF16 交付版 84.1107%），首选项一致率 99.00%。 - 接口不变：与 BF16 版共用提示编译器、温度和 HTTP 接口，可直接接入 Decision Studio。 2026-10-17 完成量化、文件校验、CPU / HTTP 检查与 CJ-Bench 全量对照，并在 Hugging Face 与… Open weights apache-2.0 262,144 tokens gguf [View model](https://savrn.com/models/chinese-decision-2b-gguf) Model ### [chinese-jev-onnx](https://savrn.com/models/chinese-jev-onnx) [bbldCVer](https://savrn.com/model-publishers/bbldcver-hf) This public model repository serves the Chinese-JEV GitHub Pages application. The archive source is mem5ory7/chinese-jev-onnx (dataset repository), supplied for this deployment. The model has two ONNX graphs (encoder.onnx and head.onnx) with external data files. The browser uses the accompanying manifest.json, tokenizer files, rlagentconfig.json, and checks.json. It is a custom choice, score, and yes/no decision model; it is not a standard Transformers.js text-classification pipeline export. The model files are fetched by the browser and inference runs locally with ONNX Runtime Web. Initial download is about 1.3 GB. Input text is not sent to this repository. The original portable package… Open weights [View model](https://savrn.com/models/chinese-jev-onnx) Model ### [chioma-llama31-8b-dpo-v2b](https://savrn.com/models/chioma-llama31-8b-dpo-v2b) [AfriMentor AI](https://savrn.com/model-publishers/afrimentor) 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 0.14.0 Open weights peft [View model](https://savrn.com/models/chioma-llama31-8b-dpo-v2b) Model ### [chip-0.8b](https://savrn.com/models/chip-0-8b) [Wenhua Huo](https://savrn.com/model-publishers/wenhuahuo) Chip is the main NL2Hull ship-design decision model. It adapts a Kev-style typed decision interface to Chinese natural-language ship-form editing requests and predicts probability distributions over ordered editing decisions. This release contains the LoRA adapter and the learned decision head for the 0.8-billion-parameter Chip model. The adapter is applied to Qwen/Qwen3.5-0.8B-Base; the head.pt file is required by the NL2Hull/Kev inference implementation. - adaptermodel.safetensors: LoRA adapter weights - adapterconfig.json: PEFT adapter configuration - head.pt: learned typed-decision head - tokenizer.json, tokenizerconfig.json, chattemplate.jinja: tokenizer files - trainingconfig.json… Open weights peft [View model](https://savrn.com/models/chip-0-8b) Model ### [chipx-checkpoints](https://savrn.com/models/chipx-checkpoints) [SAIFI INDUSTRIES](https://savrn.com/model-publishers/saifiindustries) Open weights [View model](https://savrn.com/models/chipx-checkpoints) Model · Text generation ### [chitonga-chat-run3](https://savrn.com/models/chitonga-chat-run3) [Zamai Zambia](https://savrn.com/model-publishers/zamai-zambia) Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/chitonga-chat-run3) Model · Translation ### [chitonga-nllb-s13](https://savrn.com/models/chitonga-nllb-s13) [Zamai Zambia](https://savrn.com/model-publishers/zamai-zambia) Open weights cc-by-nc-4.0 1.4B parameters [View model](https://savrn.com/models/chitonga-nllb-s13) Model ### [chk](https://savrn.com/models/chk) [Mkkkmh](https://savrn.com/model-publishers/mkkkmh) Open weights creativeml-openrail-m [View model](https://savrn.com/models/chk) Model ### [chrome](https://savrn.com/models/chrome) [Volkodavanton](https://savrn.com/model-publishers/anton1107) Open weights [View model](https://savrn.com/models/chrome) Model ### [chrono-2021-2022-dense-cpt](https://savrn.com/models/chrono-2021-2022-dense-cpt) [Lima](https://savrn.com/model-publishers/jjjlimaus) Continued pretrain of anacoluthe89/chrono-2021 on jjjlimaus/chrono-2022-dense-3b. - Public and gated (manual review) - fp8=false (fp32 masters + bf16 autocast) - maxseqlen / packto = 512, batch=22/GPU, lr=5e-5 AdamW(fused) Access requested at publisher 2B parameters [View model](https://savrn.com/models/chrono-2021-2022-dense-cpt) Model ### [chrono-2022-live-34](https://savrn.com/models/chrono-2022-live-34) [Vadim Yakobchuk](https://savrn.com/model-publishers/vadimyakob) Open weights 2B parameters 2,048 tokens [View model](https://savrn.com/models/chrono-2022-live-34) Model ### [chrono-2022-tuning-16](https://savrn.com/models/chrono-2022-tuning-16) [Vadim Yakobchuk](https://savrn.com/model-publishers/vadimyakob) Open weights 2B parameters 2,048 tokens [View model](https://savrn.com/models/chrono-2022-tuning-16) Model ### [chrono-2023-c-3](https://savrn.com/models/chrono-2023-c-3) [Vadim Yakobchuk](https://savrn.com/model-publishers/vadimyakob) Open weights 2B parameters 2,048 tokens [View model](https://savrn.com/models/chrono-2023-c-3) Model ### [chrono-2023-c-4](https://savrn.com/models/chrono-2023-c-4) [Vadim Yakobchuk](https://savrn.com/model-publishers/vadimyakob) Open weights 2B parameters 2,048 tokens [View model](https://savrn.com/models/chrono-2023-c-4) Model ### [chrono-2023-c-5](https://savrn.com/models/chrono-2023-c-5) [Vadim Yakobchuk](https://savrn.com/model-publishers/vadimyakob) Open weights 2B parameters 2,048 tokens [View model](https://savrn.com/models/chrono-2023-c-5) Model ### [chrono-2023-live-2](https://savrn.com/models/chrono-2023-live-2) [Vadim Yakobchuk](https://savrn.com/model-publishers/vadimyakob) Open weights 2B parameters 2,048 tokens [View model](https://savrn.com/models/chrono-2023-live-2) Model ### [chrono-2023-live-3](https://savrn.com/models/chrono-2023-live-3) [Vadim Yakobchuk](https://savrn.com/model-publishers/vadimyakob) Open weights 2B parameters 2,048 tokens [View model](https://savrn.com/models/chrono-2023-live-3) Model ### [chrono-2023-live-4](https://savrn.com/models/chrono-2023-live-4) [Vadim Yakobchuk](https://savrn.com/model-publishers/vadimyakob) Open weights 2B parameters 2,048 tokens [View model](https://savrn.com/models/chrono-2023-live-4) ## 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.