# Models, Page 399 of 477 Source: https://savrn.com/models/page/399 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 · Image and text to text ### [povl-rl-gsapo-200](https://savrn.com/models/povl-rl-gsapo-200) [Cheney Wong](https://savrn.com/model-publishers/cheneywong) Reinforcement learning round 2, GSAPO, 200 steps. The delivered model of that round. Equal-weight TEDS 0.8473 / TEDS-Struct 0.9078 over 33,545 samples. Fine-tuned from CheneyWong/povl-rl-dapo-400, which is RL round 1 -- the stages form a single linear chain. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction that correctly reads the visible text scores 0.33 TEDS / 1.00 TEDS-S. Only the TEDS-S column is meaningful for that dataset. Rejected at the gate. The one substantive gap: every… Open weights apache-2.0 906M parameters 131,072 tokens transformers [View model](https://savrn.com/models/povl-rl-gsapo-200) Model · Image and text to text ### [povl-sft-2d-merge](https://savrn.com/models/povl-sft-2d-merge) [Cheney Wong](https://savrn.com/model-publishers/cheneywong) Corrective supervised fine-tuning. The core fix is the two-dimensional merge token (a cell spanning both rows and columns): it accounted for only 0.89% of cell tokens in the previous training mixture, and one source had 0.00%. 22,110 samples with two or more 2D merges were added, lifting the rate to 3.92% (1.06 -> 4.96 per table). This also eliminated the runaway-truncation failure mode entirely. Fine-tuned from CheneyWong/povl-sft-full-3ep, which is its own immediate predecessor in the same run. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a… Open weights apache-2.0 906M parameters 131,072 tokens transformers [View model](https://savrn.com/models/povl-sft-2d-merge) Model · Image and text to text ### [povl-sft-full-3ep](https://savrn.com/models/povl-sft-full-3ep) [Cheney Wong](https://savrn.com/model-publishers/cheneywong) First-stage supervised fine-tuning: full-parameter, 100,122 samples, 3 epochs. Its job was to stop the model from generating unbounded output on unseen layouts -- average completion length on real photographed pages had grown to 6,610 characters with a 37.3% truncation rate. Fine-tuned from CheneyWong/tablerecognitionpaddleocrvl16 (PaddleOCR-VL-1.6), which is the released base model, unmodified except for a cleaning pass. Base = the unmodified base model. Public datasets only; the project's self-built benchmarks are excluded. (not evaluated) (~0.22 here) measures how well the model reproduces empty cells and should be ignored -- a perturbation test shows a structurally perfect prediction… Open weights apache-2.0 906M parameters 131,072 tokens transformers [View model](https://savrn.com/models/povl-sft-full-3ep) Model ### [pp-relay](https://savrn.com/models/pp-relay) [Ricardo](https://savrn.com/model-publishers/ricardo0418) Open weights [View model](https://savrn.com/models/pp-relay) Model ### [ppe_02](https://savrn.com/models/ppe-02) [Targaryen](https://savrn.com/model-publishers/targaryen101) Open weights [View model](https://savrn.com/models/ppe-02) Model · Text classification ### [pplx-decider-v1-27b-EXL3](https://savrn.com/models/pplx-decider-v1-27b-exl3) [ramGPT](https://savrn.com/model-publishers/ramgpt) EXL3 4.00 bpw conversion of perplexity-ai/pplx-decider-v1-27b. pplx-decider-v1-27b does not use the normal LM-head token-generation path for its final answer. It produces a hidden state and applies the model's custom readout.safetensors decision head to obtain option probabilities. The repository therefore includes pplxdeciderexl3.py, which registers the model architecture and performs the custom decision readout. This model is not recommended for standard TabbyAPI usage. A local smoke test with TabbyAPI and ExLlamaV3 1.5.2 failed during startup with: More importantly, simply registering the architecture is not sufficient for normal /v1/chat/completions behavior: TabbyAPI's standard… Open weights apache-2.0 7.7B parameters 262,144 tokens [View model](https://savrn.com/models/pplx-decider-v1-27b-exl3) Model · Feature extraction ### [pplx-embed-v2-late-0.6b-ONNX](https://savrn.com/models/pplx-embed-v2-late-0-6b-onnx) [Nicolas Embleton](https://savrn.com/model-publishers/nicolasembleton) Exported to ONNX from perplexity-ai/pplx-embed-v2-late-0.6b (by Perplexity, MIT license). This is an unofficial conversion; all credit for the model goes to the original authors. The model is a late-interaction (multi-vector) retriever: it outputs one L2-normalized 128-d vector per token or merged image patch, and documents are scored against queries with MaxSim. - embedtokens: inputids -> inputsembeds - visionencoder: pixelvalues, gridthw (one image per call) -> image embeddings that replace the image tokens - textmodel: inputsembeds, attentionmask, positionids [3,B,T] -> tokenembeddings [B,T,128] Browser runtime: the fp32, fp16, q4 files loaded and ran with onnxruntime-web 1.30.0 (wasm… Open weights mit 262,144 tokens onnx [View model](https://savrn.com/models/pplx-embed-v2-late-0-6b-onnx) Model · Reinforcement learning ### [ppo-LunarLander-v2](https://savrn.com/models/ppo-lunarlander-v2) [Harishankar MV](https://savrn.com/model-publishers/hari72) This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library. Open weights stable-baselines3 [View model](https://savrn.com/models/ppo-lunarlander-v2) Model · Reinforcement learning ### [ppo-LunarLander-v2](https://savrn.com/models/ppo-lunarlander-v2-2) [Jarad Peckenpaugh](https://savrn.com/model-publishers/seekdepth) This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. Try this policy in the Lunar Lander Sandbox. Choose Human Pilot for direct keyboard control, AI Pilot to let the policy choose every action, or Autopilot with Manual Override to let the policy fly until you intervene. 4 AI Simulations runs four independent policy-controlled flights together, with separate rewards and outcomes. The browser runs an ONNX export of this checkpoint with ONNX Runtime Web. Its Sensor Fault setting (OFF by default) can substitute the previous physics-step observation for an AI decision's current input. Thruster Fault (also OFF by default) can drop a requested thruster… Open weights stable-baselines3 [View model](https://savrn.com/models/ppo-lunarlander-v2-2) Model · Reinforcement learning ### [ppo-LunarLander-v2-from-scratch](https://savrn.com/models/ppo-lunarlander-v2-from-scratch) [Aiman](https://savrn.com/model-publishers/muaibinrah) This is a from-scratch PyTorch implementation of PPO (HF Deep RL Course Unit8 Part1), trained on LunarLander-v3 (Kaggle gymnasium removed v2; checker tags kept as LunarLander-v2 to match the exact env-id string the course checker expects). Architecture: CleanRL-style PPO, 2-layer 64-unit actor/critic MLPs, GAE, clipped surrogate objective, orthogonal init. Training: 16 parallel envs, 3,000,000 total steps, ~639s on a Kaggle T4 GPU. Evaluation: 20 deterministic (argmax) episodes, meanreward=219.32, stdreward=23.46. Load pposcratchmodel.pt into the Agent class defined in pposcratch.py (also included in this repo) and run deterministic argmax actions on the actor logits. Open weights deep-rl-course [View model](https://savrn.com/models/ppo-lunarlander-v2-from-scratch) Model ### [ppo-LunarLander-v3](https://savrn.com/models/ppo-lunarlander-v3) [Kevin Xu](https://savrn.com/model-publishers/kevinlxu) video demo: https://huggingface.co/KevinLXu/ppo-LunarLander-v3/resolve/main/trained-lunarlander-step-0-to-step-1000.mp4 This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library. https://huggingface.co/learn/deep-rl-course/unit1/hands-on Open weights stable-baselines3 [View model](https://savrn.com/models/ppo-lunarlander-v3) Model · Reinforcement learning ### [ppo-LunarLander-v3](https://savrn.com/models/ppo-lunarlander-v3-2) [Bananabaurus](https://savrn.com/model-publishers/bananabaurus-rex) This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library. Open weights stable-baselines3 [View model](https://savrn.com/models/ppo-lunarlander-v3-2) Model · Reinforcement learning ### [ppo-LunarLander-v3](https://savrn.com/models/ppo-lunarlander-v3-3) [Aleesha S Boby](https://savrn.com/model-publishers/aleeshasboby) This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library. Open weights stable-baselines3 [View model](https://savrn.com/models/ppo-lunarlander-v3-3) Model · Reinforcement learning ### [ppo-LunarLander-v3](https://savrn.com/models/ppo-lunarlander-v3-4) [Marlowe Zhong](https://savrn.com/model-publishers/marlowee) This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library. Open weights stable-baselines3 [View model](https://savrn.com/models/ppo-lunarlander-v3-4) Model · Reinforcement learning ### [ppo-Pyramids](https://savrn.com/models/ppo-pyramids) [Aiman](https://savrn.com/model-publishers/muaibinrah) This is a trained model of a ppo agent playing Pyramids using the Unity ML-Agents Library (HF Deep RL Course Unit5). Final training (run-id Pyramids1): Step 990000, Mean Reward 1.662, Std of Reward 0.400 (checkpoint exported at Step 1000160). Download the.onnx file and use with the ML-Agents Pyramids demo, or watch it play at https://huggingface.co/spaces/unity/ML-Agents-Pyramids Open weights ml-agents [View model](https://savrn.com/models/ppo-pyramids) Model · Reinforcement learning ### [ppo-SnowballTarget](https://savrn.com/models/ppo-snowballtarget) [Aiman](https://savrn.com/model-publishers/muaibinrah) This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library (HF Deep RL Course Unit5). Download the.onnx file and use with the ML-Agents SnowballTarget demo, or watch it play at https://huggingface.co/spaces/ThomasSimonini/ML-Agents-SnowballTarget Open weights ml-agents [View model](https://savrn.com/models/ppo-snowballtarget) Model · Reinforcement learning ### [ppo-lunarlanderv3](https://savrn.com/models/ppo-lunarlanderv3) [Mathis Limpkin](https://savrn.com/model-publishers/fozzikle) This is a trained model of a PPO agent playing LunarLander-v3 using the stable-baselines3 library. Open weights stable-baselines3 [View model](https://savrn.com/models/ppo-lunarlanderv3) Model · Text generation ### [ppt-pythia-160m-uniform250-previous_mse-seed1024-stage1](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed1024-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1 Open weights apache-2.0 85M parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed1024-stage1) Model ### [ppt-pythia-160m-uniform250-previous_mse-seed1024-stage2](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed1024-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) Open weights 162M parameters 2,048 tokens [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed1024-stage2) Model · Text generation ### [ppt-pythia-160m-uniform250-previous_mse-seed208-stage1](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed208-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1 Open weights apache-2.0 85M parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed208-stage1) Model ### [ppt-pythia-160m-uniform250-previous_mse-seed208-stage2](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed208-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) Open weights 162M parameters 2,048 tokens [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed208-stage2) Model · Text generation ### [ppt-pythia-160m-uniform250-previous_mse-seed324-stage1](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed324-stage1) [Qing Yao](https://savrn.com/model-publishers/qing-yao) This model is a fine-tuned version of EleutherAI/pythia-160m on an unknown dataset. The following hyperparameters were used during training: - learningrate: 0.001 - trainbatchsize: 16 - evalbatchsize: 16 - gradientaccumulationsteps: 2 - totaltrainbatchsize: 32 - lrschedulertype: cosinewithminlr - lrschedulerwarmupsteps: 13 - trainingsteps: 250 - Transformers 5.4.0 - Pytorch 2.8.0+cu128 - Datasets 3.2.0 - Tokenizers 0.22.1 Open weights apache-2.0 85M parameters 2,048 tokens transformers [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed324-stage1) Model ### [ppt-pythia-160m-uniform250-previous_mse-seed324-stage2](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed324-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) Open weights 162M parameters 2,048 tokens [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-seed324-stage2) Model ### [ppt-pythia-160m-uniform250-previous_mse_delta_shuffle1-seed1024-stage2](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-delta-shuffle1-seed1024-stage2) [Qing Yao](https://savrn.com/model-publishers/qing-yao) Open weights 162M parameters 2,048 tokens [View model](https://savrn.com/models/ppt-pythia-160m-uniform250-previous-mse-delta-shuffle1-seed1024-stage2) ## 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.