# Models, Page 402 of 477 Source: https://savrn.com/models/page/402 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. 11,442Models 3,247Datasets 285Papers 6,976Publishers 12,091Sourced relationships Updated 2026-10-19 · [How the library is built](https://savrn.com/model-hub/methodology) ## Filter Models Task - [Text generation1,413](https://savrn.com/models?task=Text+generation) - [Image and text to text512](https://savrn.com/models?task=Image+and+text+to+text) - [Feature extraction341](https://savrn.com/models?task=Feature+extraction) - [Robotics338](https://savrn.com/models?task=Robotics) - [Text classification303](https://savrn.com/models?task=Text+classification) - [Text to image209](https://savrn.com/models?task=Text+to+image) - [Speech recognition171](https://savrn.com/models?task=Speech+recognition) - [Audio classification157](https://savrn.com/models?task=Audio+classification) - [Image classification137](https://savrn.com/models?task=Image+classification) - [Text to speech132](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 learning93](https://savrn.com/models?task=Reinforcement+learning) - [Zero-shot classification93](https://savrn.com/models?task=Zero-shot+classification) - [Image segmentation88](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) - [Translation78](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 classification59](https://savrn.com/models?task=Video+classification) - [Image to text58](https://savrn.com/models?task=Image+to+text) - [Text to video55](https://savrn.com/models?task=Text+to+video) Library - 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[PaddleOCR22](https://savrn.com/models?library=PaddleOCR) - [openvino18](https://savrn.com/models?library=openvino) - [coreml17](https://savrn.com/models?library=coreml) - [exllamav316](https://savrn.com/models?library=exllamav3) - [openpi16](https://savrn.com/models?library=openpi) - [litert15](https://savrn.com/models?library=litert) - [rkllm15](https://savrn.com/models?library=rkllm) Modality - [Other6,473](https://savrn.com/models?modality=Other) - [Text2,612](https://savrn.com/models?modality=Text) - [Image and text570](https://savrn.com/models?modality=Image+and+text) - [Image565](https://savrn.com/models?modality=Image) - [Audio478](https://savrn.com/models?modality=Audio) - [Control431](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-2.03,105](https://savrn.com/models?license=apache-2.0) - [mit1,246](https://savrn.com/models?license=mit) - [other703](https://savrn.com/models?license=other) - [cc-by-nc-4.0193](https://savrn.com/models?license=cc-by-nc-4.0) - [cc-by-4.0148](https://savrn.com/models?license=cc-by-4.0) - [bsd-3-clause98](https://savrn.com/models?license=bsd-3-clause) - [gemma62](https://savrn.com/models?license=gemma) - [cc-by-nc-sa-4.049](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) - [llama3.128](https://savrn.com/models?license=llama3.1) - [unknown27](https://savrn.com/models?license=unknown) - [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-m11](https://savrn.com/models?license=creativeml-openrail-m) - [afl-3.07](https://savrn.com/models?license=afl-3.0) - [llama27](https://savrn.com/models?license=llama2) - [osl-3.06](https://savrn.com/models?license=osl-3.0) - [cc0-1.04](https://savrn.com/models?license=cc0-1.0) Access - [Open weights11,087](https://savrn.com/models?access=open_access) - [Access requested at publisher355](https://savrn.com/models?access=gated) Publisher - [Team Mradermacher294](https://savrn.com/models?publisher=mradermacher) - [Yurun Yuan95](https://savrn.com/models?publisher=ryanyr) - [RunningHub.ai94](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 Pu69](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) - [Francesca Padovani53](https://savrn.com/models?publisher=francesca9805) - [Wang49](https://savrn.com/models?publisher=davidwdw) - [Microsoft48](https://savrn.com/models?publisher=microsoft) - [Yurun Yuan48](https://savrn.com/models?publisher=yuruny) - [NVIDIA46](https://savrn.com/models?publisher=nvidia) - [Khazzz1c44](https://savrn.com/models?publisher=khazic) - [Quan44](https://savrn.com/models?publisher=wq2012) - [BAAR43](https://savrn.com/models?publisher=aixk) - [Christopher Subia-Waud42](https://savrn.com/models?publisher=cwaud) [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) 11,442 models, sorted by most downloaded. Model ### [prompt-builder-and-agentforce](https://savrn.com/models/prompt-builder-and-agentforce) [Anurag Sharma](https://savrn.com/model-publishers/sh-anurag) To eliminate hallucinations, autonomous AI agents require live CRM data injected directly into their reasoning loops. Salesforce Prompt Builder provides the foundational framework for dynamically grounding Agentforce sub-agents with real-time enterprise context. Key Benefits of Prompt Grounding Zero Hallucinations: Sub-agents execute decisions using live record attributes—such as account health, support history, or open opportunities—rather than static LLM training data. Declarative Design: Admins can quickly build reusable prompt templates using merge fields, Flow outputs, and Data Cloud attributes without writing code. Built-In Security: Every grounded prompt passes through the Einstein… Open weights [View model](https://savrn.com/models/prompt-builder-and-agentforce) Model ### [prompt-engineering-efficient](https://savrn.com/models/prompt-engineering-efficient) [Timothy Thomas](https://savrn.com/model-publishers/timothythomas90) This is an exploratory note for Prompt Engineering. 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 summary.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… Open weights mit 24,832 parameters 256 tokens [View model](https://savrn.com/models/prompt-engineering-efficient) Model ### [prompt-engineering89](https://savrn.com/models/prompt-engineering89) [Nathan DAVIS](https://savrn.com/model-publishers/bcnmarcos) A structured set of research notes on Prompt Engineering, 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 review.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… Open weights cc-by-4.0 33,088 parameters 128 tokens [View model](https://savrn.com/models/prompt-engineering89) Model · Text classification ### [prompt-injection-guard-base](https://savrn.com/models/prompt-injection-guard-base) [Horizon Labs](https://savrn.com/model-publishers/horizon-labs) A fast, multilingual classifier that flags prompt injection and jailbreak attempts, both in user messages (direct) and in untrusted content an AI agent reads: emails, web pages, documents, RAG chunks, and tool/API outputs (indirect). their clean counterparts, so it looks for instructions aimed at the AI, not for scary words. - Low false-alarm rate on look-alike benign text: 92.9% on NotInject, 99.2% on OR-Bench-hard. half the size and the same decisions as fp32 on our checks), transformers.js. Labels: SAFE (0) and INJECTION (1). This is the same convention as protectai/deberta-v3-base-prompt-injection-v2, so the model is a drop-in replacement in code and tools built for that one. INJECTION… Open weights apache-2.0 308M parameters 8,192 tokens transformers [View model](https://savrn.com/models/prompt-injection-guard-base) Model · Text classification ### [prompt-injection-guard-multilingual](https://savrn.com/models/prompt-injection-guard-multilingual) [Horizon Labs](https://savrn.com/model-publishers/horizon-labs) A compact multilingual detector for prompt injection and jailbreak attempts (large language model guardrails). Given any user prompt, tool output, or document excerpt, it outputs the probability that the text is an attack on the LLM's instructions. Existing public injection detectors are either English-only (e.g. protectai's deberta models) or released under licenses many organizations cannot use (Meta's PromptGuard line). This model is permissively licensed, works in 17 languages, ships with its multilingual training corpus and is evaluated against a public baseline on shared test sets. Recommended threshold: 0.50 — conservative default (EN precision 0.83 on the attack test, safe-prompt… Open weights apache-2.0 135M parameters 512 tokens transformers [View model](https://savrn.com/models/prompt-injection-guard-multilingual) Model · Text classification ### [prompt-injection-guard-small](https://savrn.com/models/prompt-injection-guard-small) [Horizon Labs](https://savrn.com/model-publishers/horizon-labs) A fast, multilingual classifier that flags prompt injection and jailbreak attempts, both in user messages (direct) and in untrusted content an AI agent reads: emails, web pages, documents, RAG chunks, and tool/API outputs (indirect). their clean counterparts, so it looks for instructions aimed at the AI, not for scary words. - Low false-alarm rate on look-alike benign text: 89.7% on NotInject, 99.5% on OR-Bench-hard. half the size and the same decisions as fp32 on our checks), transformers.js. Labels: SAFE (0) and INJECTION (1). This is the same convention as protectai/deberta-v3-base-prompt-injection-v2, so the model is a drop-in replacement in code and tools built for that one. INJECTION… Open weights apache-2.0 141M parameters 8,192 tokens transformers [View model](https://savrn.com/models/prompt-injection-guard-small) Model · Zero-shot classification ### [proof_v3](https://savrn.com/models/proof-v3) [Falcons.ai](https://savrn.com/model-publishers/falconsai) A single-pass, typed, calibrated decision model. Give it a state (text, code or JSON), one or more typed questions (choice, noul yes/no, score ordinal) and a closed set of options. It returns a calibrated probability for every option, from one encoder pass per question, however many options there are. This repository is both the model card and the developer guide: how to load it, test it, and use it as a decision component in an agentic system. The model has no generative component. It can only rank the options you give it, so it cannot produce text outside that set. All numbers come from this checkpoint's falcondecreport.json: one run, small preset, seed 42, MODE="scratch". Calibration is… Open weights apache-2.0 transformers [View model](https://savrn.com/models/proof-v3) Model ### [prose-rewriter-1.7b-v2.3-GGUF](https://savrn.com/models/prose-rewriter-1-7b-v2-3-gguf) [Team Mradermacher](https://savrn.com/model-publishers/mradermacher) static quants of https://huggingface.co/chartreuse-verte/prose-rewriter-1.7b-v2.3 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 agpl-3.0 transformers [View model](https://savrn.com/models/prose-rewriter-1-7b-v2-3-gguf) Model · Text to image ### [prot-asim-kr2-002](https://savrn.com/models/prot-asim-kr2-002) [Shigeo Nagano](https://savrn.com/model-publishers/chaeyidpt001) These are chaeyidpt001/prot-asim-kr2-002 DreamBooth LoRA weights, trained on krea/Krea-2-Raw. The weights were trained using DreamBooth with the Krea 2 diffusers trainer. Krea 2 ships as two checkpoints: RAW (the non-distilled base you fine-tune on) and Turbo (an 8-step distilled checkpoint for fast, high-quality inference). Train your LoRA on RAW and run it on Turbo — LoRAs trained on RAW express strongly on Turbo. You should use TOK to trigger the image generation. Download the.safetensors LoRA in the Files & versions tab. For more details, including weighting, merging and fusing LoRAs, check the documentation on loading LoRAs in diffusers [TODO: provide examples of latent issues and… Open weights apache-2.0 diffusers [View model](https://savrn.com/models/prot-asim-kr2-002) Model · Text generation ### [ps4mas-rl-checkpoints](https://savrn.com/models/ps4mas-rl-checkpoints) [Yitia](https://savrn.com/model-publishers/yinita) RL adapters and training artifacts for 3-stage PPO, GRPO-C, only A+C, only B+C, and GiGPO-C. Open weights peft [View model](https://savrn.com/models/ps4mas-rl-checkpoints) Model ### [ps4mas-three-stage-11topo-20261014](https://savrn.com/models/ps4mas-three-stage-11topo-20261014) [Yitia](https://savrn.com/model-publishers/yinita) Rank-16 LoRA adapters on Qwen/Qwen3.5-4B (revision 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a), three methods: grpoabc, grpoc, grpocurriculumabc. - /checkpoint-XXXXXX/: adapter, receipt, trainingstate (every 25 GRPO groups + final) - /periodicdev/step-XXXXXX/: periodic dev evaluation (1056 cells, PENGUIN+CASTLE x 11 topologies, JEV-1.13.0 judge); summary.json + results.tar.gz Periodic dev is monitoring only; the fixed final checkpoint is the comparison endpoint. W&B: yinita/ps4mas-0922-training. Open weights peft [View model](https://savrn.com/models/ps4mas-three-stage-11topo-20261014) Model · Robotics ### [psi0-brainco](https://savrn.com/models/psi0-brainco) [Yuzaiyang](https://savrn.com/model-publishers/yuzaiyangy) 本项目提供了 Psi0 算法在 Unitree G1 29 自由度教育版、BrainCo Revo1 触觉版右手与 DECXIN 摄像头组合上的训练和推理部署实现。 摄像头链接是设备参考，不作为性能验收依据；本仓库的图像规格以驱动和模型配置为准。BrainCo 的“触觉版”描述硬件型号，当前 Psi0 观测使用手部位置反馈，未将触觉阵列作为模型输入。 当前适配客户端从 WBC 的 ZMQ 遥测取得身体实测状态，WBC 自身通过 Unitree DDS 接收 G1 状态；客户端并非另开一条独立的 G1 DDS 状态链路。 相对原生 Psi0，本项目适配了 DECXIN 右目、BrainCo 状态与六维目标、跨机时间对齐、采集 initial token、推理启动门控及首段动作检查；保留 Psi0 模型服务和 SONIC token 控制的基本结构。 依赖清单记录安装状态，包含本地源码安装的包，不应将所有条目都视为可直接从 PyPI 安装。pc2 的上述两个补充包原先装在独立服务依赖目录中。 该安装脚本不启动训练，尚未在全新机器上完成安装验收。原 uv.lock 与实际安装版本记录应共同参考，详见 reproduction.json。 - 预训练 VLM、5k action-header 初始化权重，以及部署所用 40k 权重。 - 与训练一致的数据、冻结归一化统计及 initial token 文件。 - SONIC encoder、decoder、planner 权重和参考运动数据。 - WBC 构建依赖，包括 TensorRT、ONNX… Open weights apache-2.0 [View model](https://savrn.com/models/psi0-brainco) Model ### [psi0-g1-manip-combined-v1](https://savrn.com/models/psi0-g1-manip-combined-v1) [Praveen](https://savrn.com/model-publishers/praveen-k-etr) Open weights [View model](https://savrn.com/models/psi0-g1-manip-combined-v1) Model ### [psp-chest-pneumonia-256-wplus](https://savrn.com/models/psp-chest-pneumonia-256-wplus) [Trương Thành Đạt](https://savrn.com/model-publishers/purino) Open weights [View model](https://savrn.com/models/psp-chest-pneumonia-256-wplus) Model ### [psvae3f](https://savrn.com/models/psvae3f) [Pastel](https://savrn.com/model-publishers/pastel1010) Open weights [View model](https://savrn.com/models/psvae3f) Model · Text generation ### [public-sft-qwen9b-v2](https://savrn.com/models/public-sft-qwen9b-v2) [Alfaplus Laboratory](https://savrn.com/model-publishers/alfaplus) 本仓库是一个 LoRA 适配器：在 Qwen/Qwen3.5-9B 上，用公开数据集 CohereLabs/ayadataset 的日文子集（languagecode == "jpn"）的 2400 条做了一轮指令微调（SFT）。 1. 几乎全部出自单一标注者：日文 original-annotations 共 6149 行，其中 6144 行出自同一个 userid（9881e959…），另一个标注者只有 5 行（整个日文 original-annotations 只有 2 个标注者）。这些内容里有大量「模板指令 + 一段やさしい日本語体的时事新闻（NHK NEWS WEB EASY 体，2022–2023 年）」，疑似搬自外部文章库而非标注者原创；用 WebSearch 对若干特征句做过核对，既未找到逐字一致的原文、也无法排除（相关旧文多已下线）。 2. 回答普遍很短：训练子集（2400 条）里 assistant 回答 54.2% 在 10 字以内、78.3% 在 50 字以内（多为问答题式的极简短答）。训练后模型很可能偏向极简短答。 3. 未做任何评测，且不得被当成「有日文能力」的模型：本轮只跑了训练，没有任何基准评测，也没有验证集；日文表现完全没有被测过。请不要把本适配器当成一个日文模型——它只是一次在公开日文数据上的小规模 SFT 实验。 不包含：合并后的完整权重、中间 checkpoint、优化器状态、训练语料或其副本。训练数据请从上游获取。 - 数据集：CohereLabs/ayadataset（Singh et al. 2024… Open weights apache-2.0 peft [View model](https://savrn.com/models/public-sft-qwen9b-v2) Model · Token classification ### [py314-pylingual-v11-segmenter](https://savrn.com/models/py314-pylingual-v11-segmenter) [UTD Systems Security Lab](https://savrn.com/model-publishers/syssec-utd) This model is a fine-tuned version of syssec-utd/py314-pylingual-v11-mlm on the syssec-utd/segmentation-py314-pylingual-v11-tokenized dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 64 - evalbatchsize: 8 - distributedtype: multi-GPU - numdevices: 3 - totaltrainbatchsize: 192 - totalevalbatchsize: 24 - lrschedulertype: linear - numepochs: 2 - mixedprecisiontraining: Native AMP - Transformers 5.17.0 - Pytorch 2.14.0+cu130 - Datasets 5.0.1 - Tokenizers 0.23.2 Open weights 109M parameters 1,026 tokens transformers [View model](https://savrn.com/models/py314-pylingual-v11-segmenter) Model ### [py314-pylingual-v11-tok](https://savrn.com/models/py314-pylingual-v11-tok) [UTD Systems Security Lab](https://savrn.com/model-publishers/syssec-utd) Open weights [View model](https://savrn.com/models/py314-pylingual-v11-tok) Model ### [py315-pylingual-v7-tokenizer](https://savrn.com/models/py315-pylingual-v7-tokenizer) [UTD Systems Security Lab](https://savrn.com/model-publishers/syssec-utd) Open weights [View model](https://savrn.com/models/py315-pylingual-v7-tokenizer) Model ### [pypi312](https://savrn.com/models/pypi312) [RIMI](https://savrn.com/model-publishers/pythonstb) A PEP 503 compliant simple repository hosting Python 3.12 Wheels By Rimi for Android. - Android API 24+ minimum Windows (%APPDATA%\pip\pip.ini): Linux/macOS (~/.pip/pip.conf): Install plain pip install. PipManager is a built-in package manager for installing Python packages on Android. 2. Type package name(s) in the text field — space separated for multiple packages 3. Click INSTALL - Upgrade — upgrade to latest version - Reinstall — force reinstall - Uninstall — remove a package - NoDeps — install without dependencies - NoCache — install without using the cache - Smart detection — skips install if already installed PROPRIETARY Built with care for the Android Python community Creator and… Open weights other pip [View model](https://savrn.com/models/pypi312) Model ### [pythonAiModel](https://savrn.com/models/pythonaimodel) [uchihaKer](https://savrn.com/model-publishers/uchihaker) I intend to build a highly creative and forward‑looking AI model, rooted in cutting-edge technology and long‑term vision. It will focus on exploring the frontiers of intelligence, breaking through existing limitations, and continuously generating innovative solutions for real-world challenges. Driven by the mission to benefit humanity, it will be developed responsibly, with fairness, safety and inclusiveness at its core. It is committed to promoting social progress, bridging gaps in technology and life, and working together with people to build a more intelligent, warm and better world for all. Open weights mit [View model](https://savrn.com/models/pythonaimodel) Model · Reinforcement learning ### [q-FrozenLake-v1-4x4-noSlippery](https://savrn.com/models/q-frozenlake-v1-4x4-noslippery) [May Thet Ko](https://savrn.com/model-publishers/maytko5) This is a trained model of a Q-Learning agent playing FrozenLake-v1. Open weights [View model](https://savrn.com/models/q-frozenlake-v1-4x4-noslippery) Model · Reinforcement learning ### [q-FrozenLake-v1-4x4-noSlippery](https://savrn.com/models/q-frozenlake-v1-4x4-noslippery-2) [Filippo Gibertini](https://savrn.com/model-publishers/filippogib) This is a trained model of a Q-Learning agent playing FrozenLake-v1. Open weights [View model](https://savrn.com/models/q-frozenlake-v1-4x4-noslippery-2) Model · Reinforcement learning ### [q-FrozenLake-v1-4x4-noSlippery](https://savrn.com/models/q-frozenlake-v1-4x4-noslippery-3) [Chirag Budhathoki](https://savrn.com/model-publishers/chiragq6967) This is a trained model of a Q-Learning agent playing FrozenLake-v1. model = loadfromhub(repoid="ChiraGQ6967/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") env = gym.make(model["envid"]) Open weights [View model](https://savrn.com/models/q-frozenlake-v1-4x4-noslippery-3) ## 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.