# Models, Page 387 of 477 Source: https://savrn.com/models/page/387 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 - [transformers2,777](https://savrn.com/models?library=transformers) - [peft345](https://savrn.com/models?library=peft) - [diffusers187](https://savrn.com/models?library=diffusers) - [pytorch178](https://savrn.com/models?library=pytorch) - [gguf167](https://savrn.com/models?library=gguf) - [lerobot139](https://savrn.com/models?library=lerobot) - [sentence-transformers139](https://savrn.com/models?library=sentence-transformers) - [mlx136](https://savrn.com/models?library=mlx) - [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) - [vllm38](https://savrn.com/models?library=vllm) - [onnx36](https://savrn.com/models?library=onnx) - [diffusion-single-file27](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) - [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 · Text classification ### [opendecision-agent](https://savrn.com/models/opendecision-agent) [Luiz Eduardo](https://savrn.com/model-publishers/ludolua) A small, auditable classifier that predicts what an assistant should do next in a task-oriented conversation. Given the current state, it returns one action from a fixed vocabulary with a probability for each, so an agent can act alone when it is confident and hand the decision to a person when it is not. It ships with a decision policy (config.toml) that blocks impossible actions and sets a minimum confidence per action. Direct use. Decide the next step of a conversational agent (ask, search, present options, confirm, execute, end the conversation), route uncertain decisions to human review, and log decisions with feedback to collect training data. Out of scope. Executing irreversible… Open weights cc-by-sa-4.0 sklearn [View model](https://savrn.com/models/opendecision-agent) Model · Text classification ### [opendecision-flights](https://savrn.com/models/opendecision-flights) [Luiz Eduardo](https://savrn.com/model-publishers/ludolua) A small, auditable classifier that predicts the next step of an agent that helps find flight tickets. Given the current state, it returns one action from a fixed vocabulary with a probability for each, so an agent can act alone when it is confident and hand the decision to a person when it is not. It ships with a decision policy (config.toml) that blocks impossible actions and sets a minimum confidence per action. Direct use. Decide the next step of a conversational agent (ask, search, present options, confirm, execute, end the conversation), route uncertain decisions to human review, and log decisions with feedback to collect training data. Out of scope. Executing irreversible actions… Open weights mit sklearn [View model](https://savrn.com/models/opendecision-flights) Model · Text classification ### [openjev-v5-0.8b](https://savrn.com/models/openjev-v5-0-8b) [Rodney Lafuente-Mercado](https://savrn.com/model-publishers/rodneyslafuente) This is an unchanged mirror of AlexWortega's pretrained OpenJev v5 0.8B model, published so adapters can identify and load this specific base without confusing it with the 2B and 4B models in the upstream repository. All model, tokenizer, and configuration files are byte-for-byte copies. I did not train this base model. The source is AlexWortega/openjev, qwen3.5-0.8b-nli-v5, pinned to revision 552759daad712f1af6c4c13dabcb1e047886fc9c. OpenJev turns the Qwen3.5-0.8B backbone into a three-class natural language inference classifier. Credit for the base model and its training belongs to AlexWortega and the Qwen team. See the upstream model card for the method and reported evaluations. The… Open weights mit 853M parameters 262,144 tokens transformers [View model](https://savrn.com/models/openjev-v5-0-8b) Model · Text classification ### [openjevx](https://savrn.com/models/openjevx) [Muthukumaran Navaneethakrishnan](https://savrn.com/model-publishers/muthuishere) OpenJevX is an open-weight, non-autoregressive System One decision model specialized from Laya, which uses answerdotai/ModernBERT-large plus a dynamic typed-decision head. It accepts runtime-defined choice, score, and noul questions and returns calibrated probabilities in one forward pass. It is compatible with the TypeSafe Jev /v1/systemone request shape through the OpenJevX server. - CUDA p50 latency per five-question case: 22.8 ms The benchmark uses the untouched 400-case, 2,000-decision test split from LocalLLaMA/typed-decisions. Training uses only its 1,200-case train split. OpenJevX is a derivative of Laya by ConvAI Innovations and ModernBERT by Answer.AI and LightOn. Laya and… Open weights apache-2.0 421M parameters laya [View model](https://savrn.com/models/openjevx) Model ### [openmythos-security-v0.1](https://savrn.com/models/openmythos-security-v0-1) [0ai](https://savrn.com/model-publishers/cyberviser) Access requested at publisher mit [View model](https://savrn.com/models/openmythos-security-v0-1) Model ### [openmythos-security-v0.2](https://savrn.com/models/openmythos-security-v0-2) [0ai](https://savrn.com/model-publishers/cyberviser) Local continue-train on glasseye RTX 5070 from openmythos-security-v0.1 (100 steps). Authorized research / detection-oriented use. Note: checkpoint remap had partial key mismatches; treat as experimental refresh. Open weights mit [View model](https://savrn.com/models/openmythos-security-v0-2) Model · Robotics ### [openpi-pi07-so101-left-arm-multitask](https://savrn.com/models/openpi-pi07-so101-left-arm-multitask) [Twanghcmut](https://savrn.com/model-publishers/twanghcmut) Open weights other 3.6B parameters lerobot [View model](https://savrn.com/models/openpi-pi07-so101-left-arm-multitask) Model ### [openstl-hycom-checkpoints](https://savrn.com/models/openstl-hycom-checkpoints) [Huangjinlong](https://savrn.com/model-publishers/hjl124232) Open weights [View model](https://savrn.com/models/openstl-hycom-checkpoints) Model ### [openvino-models](https://savrn.com/models/openvino-models) [Gao Yiman](https://savrn.com/model-publishers/dunegym) Pre-converted OpenVINO GenAI models, ready to use with ovtool. Every llm/vlm model ships the full five-variant ladder: int4-asym-g128 / int4-sym-g128 / int4-awq-g128 / int8 / fp16. - Image input for Qwen3-VL / Qwen3.5 hits a shape-compatibility issue in this GenAI release (fixed on master, pending release); text-only mode works fine - NPU supports only symmetric INT4 models, and requires a fixed prompt/response budget at pipeline compile time (handled automatically by ovtool) Open weights [View model](https://savrn.com/models/openvino-models) Model ### [openvla-7b-libero-goal-watermark-mul-0.5-5pct](https://savrn.com/models/openvla-7b-libero-goal-watermark-mul-0-5-5pct) [Hehongyu](https://savrn.com/model-publishers/hhhhhy60) Open weights 7.5B parameters [View model](https://savrn.com/models/openvla-7b-libero-goal-watermark-mul-0-5-5pct) Model ### [openvla-7b-libero-object-watermark-mul-0.5-5pct](https://savrn.com/models/openvla-7b-libero-object-watermark-mul-0-5-5pct) [Hehongyu](https://savrn.com/model-publishers/hhhhhy60) Open weights [View model](https://savrn.com/models/openvla-7b-libero-object-watermark-mul-0-5-5pct) Model ### [openvla-7b-libero-spatial-watermark-mul-0.5-5pct](https://savrn.com/models/openvla-7b-libero-spatial-watermark-mul-0-5-5pct) [Hehongyu](https://savrn.com/model-publishers/hhhhhy60) Open weights [View model](https://savrn.com/models/openvla-7b-libero-spatial-watermark-mul-0-5-5pct) Model ### [opsa-mmopd4b-ot3-2ep-ot3math-lr1e-6-step10](https://savrn.com/models/opsa-mmopd4b-ot3-2ep-ot3math-lr1e-6-step10) [Seo Min Hyuk](https://savrn.com/model-publishers/seomh) Open weights [View model](https://savrn.com/models/opsa-mmopd4b-ot3-2ep-ot3math-lr1e-6-step10) Model ### [opsa-repro-qwen3-4b-nothink-dapo17k-step100](https://savrn.com/models/opsa-repro-qwen3-4b-nothink-dapo17k-step100) [Seo Min Hyuk](https://savrn.com/model-publishers/seomh) Open weights 4B parameters 40,960 tokens [View model](https://savrn.com/models/opsa-repro-qwen3-4b-nothink-dapo17k-step100) Model ### [opsiom-fr-checkpoints](https://savrn.com/models/opsiom-fr-checkpoints) [Jules Ecorcheville](https://savrn.com/model-publishers/juloeco) Open weights [View model](https://savrn.com/models/opsiom-fr-checkpoints) Model ### [opt-babylm-350m-20eps-prenorm-seed964](https://savrn.com/models/opt-babylm-350m-20eps-prenorm-seed964) [Zachary Houghton](https://savrn.com/model-publishers/znhoughton) Open weights 312M parameters 1,024 tokens [View model](https://savrn.com/models/opt-babylm-350m-20eps-prenorm-seed964) Model ### [opus-4.8-recreation-1b-light](https://savrn.com/models/opus-4-8-recreation-1b-light-2) [0ai](https://savrn.com/model-publishers/cyberviser) Latest local refresh: see opus-4.8-recreation-1b-light-v4 under the same org (glasseye RTX 5070). Access requested at publisher mit 929M parameters open_mythos [View model](https://savrn.com/models/opus-4-8-recreation-1b-light-2) Model ### [opus-4.8-recreation-1b-light-v4](https://savrn.com/models/opus-4-8-recreation-1b-light-v4) [0ai](https://savrn.com/model-publishers/cyberviser) Continued light-mode training on glasseye RTX 5070 only (no Modal/HF Jobs). Load with MoE?Expert swap as documented on the baseline card. Open weights mit 929M parameters open_mythos [View model](https://savrn.com/models/opus-4-8-recreation-1b-light-v4) Model ### [opus-4.8-recreation-1b-light-v4](https://savrn.com/models/opus-4-8-recreation-1b-light-v4-2) [CyberviserAI](https://savrn.com/model-publishers/glasseye) Continued light-mode training on glasseye RTX 5070 only (no Modal/HF Jobs). Load with MoE?Expert swap as documented on the baseline card. Open weights mit 929M parameters open_mythos [View model](https://savrn.com/models/opus-4-8-recreation-1b-light-v4-2) Model ### [opus-writer-gemma4-31b-khateeb-v1-lora](https://savrn.com/models/opus-writer-gemma4-31b-khateeb-v1-lora) [Mousears](https://savrn.com/model-publishers/mousears1090) Open weights [View model](https://savrn.com/models/opus-writer-gemma4-31b-khateeb-v1-lora) Model · Text generation ### [oracle19c-nl2sql-granite-4.1-3b-lora](https://savrn.com/models/oracle19c-nl2sql-granite-4-1-3b-lora) [Vakamalla Anitha](https://savrn.com/model-publishers/vakamalla123-anitha) basemodel: ibm-granite/granite-4.1-3b libraryname: peft pipelinetag: text-generation - lora - qlora - peft - oracle - oracle19c - nl2sql - text-to-sql - sql-generation - granite A QLoRA adapter fine-tuned to convert natural-language questions into Oracle 19c SQL queries. This repository contains a LoRA adapter, not a full standalone model. - Convert natural-language questions into Oracle 19c SQL - Use with a schema-retrieval or RAG layer for better table and column selection - 2,800 Oracle-focused NL→SQL examples - 400 general-SQL replay examples - This adapter is not a database-security system. - Review every generated SQL query before running it. - Use read-only database access for… Open weights apache-2.0 peft [View model](https://savrn.com/models/oracle19c-nl2sql-granite-4-1-3b-lora) Model · Question answering ### [oracle7](https://savrn.com/models/oracle7) [Benjamin Horn](https://savrn.com/model-publishers/psiloceyeben) Oracle7 is an AI built to say "I can't verify that" rather than guess. It is not a neural language model. It answers from stored, sourced records and from 267 specialists, each of which passed its own held-out test before it was allowed to answer. Every reply carries its path: the route, specialists and records that produced it. When nothing in it can verify an answer, it declines and tells you why. It does not always manage that: the measured section below says exactly how often it is still wrong, and on what. This is the full release: the core plus the deep knowledge stores (English Wikipedia + DBpedia abstracts, 71 OpenStax textbooks, Simple English Wikipedia, Wiktionary, WordNet… Open weights other [View model](https://savrn.com/models/oracle7) Model · Question answering ### [oracle7-slim](https://savrn.com/models/oracle7-slim) [Benjamin Horn](https://savrn.com/model-publishers/psiloceyeben) Oracle7 is an AI built to say "I can't verify that" rather than guess. It is not a neural language model. It answers from stored, sourced records and from 267 specialists, each of which passed its own held-out test before it was allowed to answer. Every reply carries its path: the route, specialists and records that produced it. When nothing in it can verify an answer, it declines and tells you why. It does not always manage that: the measured section below says exactly how often it is still wrong, and on what. This is the slim release: the same core, routes and 267 specialists, with the smaller stores (Simple English Wikipedia, Wiktionary, WordNet, ConceptNet, Wikidata relations) and no… Open weights other [View model](https://savrn.com/models/oracle7-slim) Model ### [orbit-reader](https://savrn.com/models/orbit-reader) [Andrew Suvinston](https://savrn.com/model-publishers/andrewsuvinston) Open weights [View model](https://savrn.com/models/orbit-reader) ## 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.