# Models, Page 415 of 477 Source: https://savrn.com/models/page/415 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 ### [r16-H-chrono4-fkd-final](https://savrn.com/models/r16-h-chrono4-fkd-final) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2B parameters [View model](https://savrn.com/models/r16-h-chrono4-fkd-final) Model ### [r16-H-chrono4-fkd-s800](https://savrn.com/models/r16-h-chrono4-fkd-s800) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2B parameters [View model](https://savrn.com/models/r16-h-chrono4-fkd-s800) Model ### [r16-H-chrono4-skd-final](https://savrn.com/models/r16-h-chrono4-skd-final) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2B parameters [View model](https://savrn.com/models/r16-h-chrono4-skd-final) Model ### [r16-H-chrono4-skd-s1000](https://savrn.com/models/r16-h-chrono4-skd-s1000) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2B parameters [View model](https://savrn.com/models/r16-h-chrono4-skd-s1000) Model ### [r16-H-chrono4-skd-s500](https://savrn.com/models/r16-h-chrono4-skd-s500) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2B parameters [View model](https://savrn.com/models/r16-h-chrono4-skd-s500) Model ### [r16-H-soup3-fkd-final](https://savrn.com/models/r16-h-soup3-fkd-final) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2.1B parameters [View model](https://savrn.com/models/r16-h-soup3-fkd-final) Model ### [r16-H-soup3-fkd-s400](https://savrn.com/models/r16-h-soup3-fkd-s400) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2.1B parameters [View model](https://savrn.com/models/r16-h-soup3-fkd-s400) Model ### [r16-H-soup3-fkd-s600](https://savrn.com/models/r16-h-soup3-fkd-s600) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2.1B parameters [View model](https://savrn.com/models/r16-h-soup3-fkd-s600) Model ### [r16-H-soup3-fkd-s800](https://savrn.com/models/r16-h-soup3-fkd-s800) [Alfred Smith](https://savrn.com/model-publishers/alfred8995) Access requested at publisher 2.1B parameters [View model](https://savrn.com/models/r16-h-soup3-fkd-s800) Model · Text to image ### [r3iko2](https://savrn.com/models/r3iko2) [Mike](https://savrn.com/model-publishers/mej023) A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps). Use the token r3iko2 to invoke the concept. Open weights apache-2.0 diffusers [View model](https://savrn.com/models/r3iko2) Model ### [r5-student-v23](https://savrn.com/models/r5-student-v23) [Majid](https://savrn.com/model-publishers/majid2230) Open weights [View model](https://savrn.com/models/r5-student-v23) Model · Image classification ### [raah-9b](https://savrn.com/models/raah-9b) [Yashaswi](https://savrn.com/model-publishers/yalgog) Open weights cc-by-sa-4.0 peft [View model](https://savrn.com/models/raah-9b) Model ### [rab-4b-Lise-tc-v13-seed4-ep5](https://savrn.com/models/rab-4b-lise-tc-v13-seed4-ep5) [Gdvstd](https://savrn.com/model-publishers/gdvstd) Open weights 4.4B parameters 262,144 tokens [View model](https://savrn.com/models/rab-4b-lise-tc-v13-seed4-ep5) Model ### [rab-4b-Lpure-tc-rfix-v13-seed2-ep5](https://savrn.com/models/rab-4b-lpure-tc-rfix-v13-seed2-ep5) [Gdvstd](https://savrn.com/model-publishers/gdvstd) Open weights 4.4B parameters 262,144 tokens [View model](https://savrn.com/models/rab-4b-lpure-tc-rfix-v13-seed2-ep5) Model ### [rab-4b-MptrSS-v13-ep5](https://savrn.com/models/rab-4b-mptrss-v13-ep5) [Gdvstd](https://savrn.com/model-publishers/gdvstd) Open weights 4.4B parameters 262,144 tokens [View model](https://savrn.com/models/rab-4b-mptrss-v13-ep5) Model ### [rab-cogito8b-Lhint-tc-v13-ep7](https://savrn.com/models/rab-cogito8b-lhint-tc-v13-ep7) [Gdvstd](https://savrn.com/model-publishers/gdvstd) Open weights [View model](https://savrn.com/models/rab-cogito8b-lhint-tc-v13-ep7) Model ### [radamn-ai-v1](https://savrn.com/models/radamn-ai-v1) [Wellerson Blay](https://savrn.com/model-publishers/luffynox) This llama model was trained 2x faster with Unsloth Open weights apache-2.0 transformers [View model](https://savrn.com/models/radamn-ai-v1) Model ### [radiochat-laya-multilingual-onnx](https://savrn.com/models/radiochat-laya-multilingual-onnx) [Shiva Prakash](https://savrn.com/model-publishers/foreverstorm) A browser-ready export of convaiinnovations/laya-multilingual (revision e4e9ddf21a7b1903b7acffd8814ad4307bf63a67), the multilingual checkpoint of Laya by Convai Innovations — a non-autoregressive decision model that answers typed questions (choice, score, yes/no) in one forward pass. All credit for the model goes to its authors; see the Laya repository. Licensed Apache-2.0, like the original. Used by RadioChat to moderate chat messages on the user's own device. The split layout that laya-ts loads: 1. laya-ts/scripts/exportonnx.py --repo convaiinnovations/laya-multilingual (laya commit 9d95567), which checks the ONNX outputs against PyTorch (max logit difference ~2e-6). 2. ONNX Runtime 1.30… Open weights apache-2.0 onnx [View model](https://savrn.com/models/radiochat-laya-multilingual-onnx) Model · Reinforcement learning ### [ragen-frozenlake_05b_instruct_ppo100_20261018_020449](https://savrn.com/models/ragen-frozenlake-05b-instruct-ppo100-20261018-020449) [Xiaoyou77527](https://savrn.com/model-publishers/xiaoyou77527) Base model: Qwen/Qwen2.5-0.5B-Instruct. Environment: 4x4 FrozenLake, p=0.8 safe-tile probability, no slipping, grid and coordinates. PPO/GAE, no rollout filtering. at most 2 actions per turn, response limit 128 tokens. Actor LR 1e-6, critic LR 1e-5, entropy coefficient 0.001. See results/config.yaml for all settings. Validation: fixed 64 seeds (123 through 186), greedy decoding, before training and every 10 steps. Training maps start at seed 10000. One training run only. W&B: https://wandb.ai/xiaoyiwang2027-northwestern-university/ragenpilotfrozenlake/runs/8baf50af33eb Best saved TRAINED checkpoint by validation success, then reward: step0030 Each completed HF checkpoint is under… Open weights apache-2.0 [View model](https://savrn.com/models/ragen-frozenlake-05b-instruct-ppo100-20261018-020449) Model ### [ragen-searchqa-checkpoints](https://savrn.com/models/ragen-searchqa-checkpoints) [TBD's TBD Lab](https://savrn.com/model-publishers/tbd-lab) Single repository for RAGEN-2 and VAPO SearchQA experiments, organized by run. Native distributed training state is preserved for resumption. Open weights [View model](https://savrn.com/models/ragen-searchqa-checkpoints) Model ### [raiz-transcribe-app](https://savrn.com/models/raiz-transcribe-app) [MATIAS GODOY](https://savrn.com/model-publishers/impulsargt2026) Servidor de transcripción de ImpulsARG-T. Sin secretos ni datos: se configuran como variables en el host. Open weights [View model](https://savrn.com/models/raiz-transcribe-app) Model ### [ramanv-whisper-stt-checkpoints](https://savrn.com/models/ramanv-whisper-stt-checkpoints) [Some](https://savrn.com/model-publishers/guruawe) Open weights [View model](https://savrn.com/models/ramanv-whisper-stt-checkpoints) Model ### [randomized-runs-results](https://savrn.com/models/randomized-runs-results) [Rier](https://savrn.com/model-publishers/rier) Access requested at publisher [View model](https://savrn.com/models/randomized-runs-results) Model · Speech recognition ### [raon-speech-9b-GGUF](https://savrn.com/models/raon-speech-9b-gguf) [Cstr](https://savrn.com/model-publishers/cstr) GGUF conversion of the speech-to-text subset of KRAFTON/Raon-Speech-9B for CrispASR (--backend raon-speech, or -m raon-speech to auto-download Q4K). The talker, code predictor, Mimi codec, output adaptor and speaker encoder serve speech output only, so they are not converted. CrispASR reproduces RaonPipeline.stt's front end: - the input is resampled 16k → 24k, as the processor does; - the audio is cut into 8 s chunks, each resampled 24k → 16k (a port of torchaudio's sinc resampler); - each chunk gets its own log-mel (per-chunk max) and is encoded on its own; - the 13 Hz encoder output is truncated to the 12.5 Hz frame grid. The prompt is "Transcribe the audio into text"; --ask "..."… Open weights cc-by-nc-4.0 [View model](https://savrn.com/models/raon-speech-9b-gguf) ## 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.