# Models, Page 353 of 477 Source: https://savrn.com/models/page/353 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) - 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Model · Sentence similarity ### [lateon-ours-hardneg-n10573-e1-ckpt82-qd1024](https://savrn.com/models/lateon-ours-hardneg-n10573-e1-ckpt82-qd1024) [Dangerousmanleebyeonggeon](https://savrn.com/model-publishers/dangerousmanleebyeonggeon) This is a PyLate model finetuned from lightonai/LateOn. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator. Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search. Load the ColBERT model and initialize the PLAID index, then encode and index your documents: Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it: Once the documents are indexed, you can retrieve the top-k most relevant documents for a… Open weights 149M parameters 8,192 tokens PyLate [View model](https://savrn.com/models/lateon-ours-hardneg-n10573-e1-ckpt82-qd1024) Model · Robotics ### [latest_fold_diffusion_towel_fold2_20261002](https://savrn.com/models/latest-fold-diffusion-towel-fold2-20261002) [Yuval Gotlib](https://savrn.com/model-publishers/yuvalgot) Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. This policy has been trained and pushed to the Hub using LeRobot. See the full LeRobot documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI cheat-sheet — quick reference for the lerobot- commands. The short… Open weights apache-2.0 263M parameters lerobot [View model](https://savrn.com/models/latest-fold-diffusion-towel-fold2-20261002) Model · Text classification ### [laya](https://savrn.com/models/laya-2) [Convai Innovations](https://savrn.com/model-publishers/convaiinnovations) Multilingual, non-autoregressive System 1 decision model. Give it a state (text, email, ticket, or JSON) and typed questions; it returns typed answers with mathematically calibrated probabilities in a single forward pass (~33 ms) across 100+ languages. Trained with reinforcement learning against strictly proper scoring rules (RLCD), so reporting honest probabilities is the only way to maximise reward. It never generates text, so there is nothing to parse and nothing to hallucinate. This repo holds all three checkpoints and is the hub for the family. The English checkpoint is at the repo root; the other two are bundled subfolders, and only the one you request is downloaded: pip install -U… Open weights apache-2.0 421M parameters transformers [View model](https://savrn.com/models/laya-2) Model · Text classification ### [laya](https://savrn.com/models/laya-3) [Verdict](https://savrn.com/model-publishers/verdict-models) The Laya System One decision model, exported to ONNX fp32 and packaged for Verdict -- a high-performance serving engine for System One decision models. Serve it with one command: Pick the image that matches the host. The -v mounts cache the model, so only the first run downloads it. Add -e VERDICTUSEMODELSCOPE=1 and use sgyaqing/laya to fetch from ModelScope instead of the hub; add -e HFENDPOINT=https://hf-mirror.com if the hub is slow or unreachable. Endpoints: the standard decision protocols POST /v1/systemone and POST /v1/decisions, native POST /v1/choice | /v1/score | /v1/noul, plus an OpenAI-compatible layer (/v1/chat/completions, /v1/models). Measured on one machine (RTX 4090, AMD… Open weights apache-2.0 laya [View model](https://savrn.com/models/laya-3) Model ### [laya-browser-mind2web](https://savrn.com/models/laya-browser-mind2web) [Hitesh S](https://savrn.com/model-publishers/quantum08) A Laya (421M, ModernBERT-large encoder + typed decision heads) checkpoint fine-tuned to pick the next browser action — an operation (CLICK / TYPETEXT / SELECT) and a target element — from a page's DOM and a natural-language goal. Trained on Mind2Web (Deng et al., NeurIPS 2023 Datasets and Benchmarks Track, CC-BY-4.0). Built as the policy for laya-system-use, a fork of browser-use/jev-ultrafast that replaces the hosted TypeSafe Jev API with this open-weight model. This is a research/hobby-project checkpoint, not a production model. Read the Limitations section before using it. Given a page's interactive elements (label, role, current value) and a goal, it answers two typed questions in one… Open weights apache-2.0 421M parameters [View model](https://savrn.com/models/laya-browser-mind2web) Model · Text classification ### [laya-coreai](https://savrn.com/models/laya-coreai) [Andrey Babikov](https://savrn.com/model-publishers/andyinqtr) Laya typed decisions on Apple Silicon, running on the Core AI runtime — the successor to Core ML. This is a.aimodel asset exported from via Apple's coreai-torch bridge. It outputs choice / score / noul probabilities (and RL action logits) with zero generated tokens and no PyTorch, Core ML, Transformers, or cloud API at inference time. macOS 27+ (Core AI runtime), Python 3.10+. Validated on M3 Max / macOS 27.2. Validate the download end-to-end (all three specializations, timing, contract checks): Snake demo with the model (terminal game, reuses the laya-coreml UI + safety shield; automatically uses the B3 asset when present for ~2x game throughput): ~3× faster per pass than the fastest Core… Open weights apache-2.0 322M parameters coreai [View model](https://savrn.com/models/laya-coreai) Model · Text classification ### [laya-coreml](https://savrn.com/models/laya-coreml) [Aac6fef](https://savrn.com/model-publishers/aac6fef) Laya typed decisions on Apple Silicon, using CPU + GPU. This is a portable Core ML bundle for laya-coreml, converted from convaiinnovations/laya. It outputs choice, score, and noul probabilities with zero generated tokens. Inference needs no PyTorch, Transformers, MLX, remote code, or cloud API. Apple Silicon, macOS 15+, Python 3.11–3.13. Tested on M3 Max / macOS 27.2. To download explicitly and then run entirely offline: Use laya.load("aac6fef/laya-coreml", localfilesonly=True) for a cached snapshot or pass a local directory. Use revision=" " to pin a remote revision. This FP16 export retains the original model architecture and decision schema. The enumerated-length GPU export is the… Open weights apache-2.0 coreml [View model](https://savrn.com/models/laya-coreml) Model · Text classification ### [laya-heart-laya-onnx](https://savrn.com/models/laya-heart-laya-onnx) [Yunkun Song](https://savrn.com/model-publishers/yunkunsong) Two ONNX exports of Laya-multilingual (322M parameters, built on mmBERT) used by Laya-Heart, a texting copilot for couples that runs on your own computer. Laya reads the partner's message and answers short card questions with probabilities ("Does 没事 really mean it's fine?", a danger level from 1 to 10, a hidden safety question). Both run with onnxruntime and Laya's laya-ts runtime, exactly as the app runs them. Practice exam written and labelled by teacher B (Qwen3-32B-AWQ, a different model family from the teacher that wrote the training chats). One command in the repository recomputes every number from the raw answers, or re-runs both models: npm run reproduce / npm run reproduce… Open weights apache-2.0 laya [View model](https://savrn.com/models/laya-heart-laya-onnx) Model ### [laya-intent-router-400m-onnx](https://savrn.com/models/laya-intent-router-400m-onnx) [Vraj Shah](https://savrn.com/model-publishers/vrajnotviraj) Open weights [View model](https://savrn.com/models/laya-intent-router-400m-onnx) Model · Text classification ### [laya-kvp10k-noul](https://savrn.com/models/laya-kvp10k-noul) [Sothiara Em](https://savrn.com/model-publishers/sothiem) A fine-tuned Laya model (Convai Innovations, 421M, ModernBERT-large backbone) that answers one typed noul question: does a value correctly match its key label? (e.g. firstname = John → true, firstname = 1992 → false). Fine-tuned on the IBM KVP-10K dataset via the pre-parsed community mirror (OCR pre-extracted; no OCR step needed). This is the v2 (remediated) release: document-level 80/10/10 data partitioning, mixed-class frozen test set, calib-split temperature fitting, and a frozen category-stratified release gate. It supersedes the v1 release pre-remediation, historical artifact). Laya is a non-autoregressive decision model: you give it a state (text/dict) plus typed questions (choice… Open weights apache-2.0 421M parameters laya [View model](https://savrn.com/models/laya-kvp10k-noul) Model ### [laya-moneytrail-injection](https://savrn.com/models/laya-moneytrail-injection) [Chetan Choudhary](https://savrn.com/model-publishers/chetan-choudhary) A fine-tune of convaiinnovations/laya that flags an evidence document carrying an instruction aimed at its reader. Built for MoneyTrail, a cross-organization payment investigation prototype. This is not the best available detector for the job, and it is not used in MoneyTrail's decision path. It is published so the experiment can be reproduced. - On attacks no detector could have trained on, it scores 0.688 AUC. deepset/deberta-v3-base-injection scores 0.894 and sheltron-ai/prompt-guard-68m 0.785 on the same data. If you want a screen, use one of those. - It reaches a perfect 1.000 on its own held-out synthetic test split. That number is an artefact of the generator that made the data: the… Open weights apache-2.0 421M parameters laya [View model](https://savrn.com/models/laya-moneytrail-injection) Model · Text classification ### [laya-multilingual](https://savrn.com/models/laya-multilingual) [Convai Innovations](https://savrn.com/model-publishers/convaiinnovations) Non-autoregressive System 1 decision model covering 100+ languages. Give it a state (text, email, ticket, or JSON) and typed questions; it returns typed answers with probabilities in a single forward pass. No text generation, so nothing to parse and nothing to hallucinate. Part of the Laya family — use this checkpoint for anything that is not English. Since laya 0.3.13 the default Router() keeps both english and this checkpoint resident, so a mixed workload no longer swaps checkpoints on every language change. For a server, load them up front so even the first request of each language is just a forward pass: router.attach("multilingual", agent) registers an Agent you already built, so a… Open weights apache-2.0 322M parameters transformers [View model](https://savrn.com/models/laya-multilingual) Model · Text classification ### [laya-multilingual](https://savrn.com/models/laya-multilingual-2) [Scott Lamkin](https://savrn.com/model-publishers/scottrlamkin) Non-autoregressive System 1 decision model covering 100+ languages. Give it a state (text, email, ticket, or JSON) and typed questions; it returns typed answers with probabilities in a single forward pass. No text generation, so nothing to parse and nothing to hallucinate. Part of the Laya family — use this checkpoint for anything that is not English. The default Router() keeps both english and this checkpoint resident, so a mixed workload no longer swaps checkpoints on every language change. For a server, load them up front so even the first request of each language is just a forward pass: router.attach("multilingual", agent) registers an Agent you already built, so a process that loaded… Open weights apache-2.0 322M parameters transformers [View model](https://savrn.com/models/laya-multilingual-2) Model · Text classification ### [laya-multilingual](https://savrn.com/models/laya-multilingual-3) [Verdict](https://savrn.com/model-publishers/verdict-models) The Laya Multilingual System One decision model (mmBERT-base, 100+ languages), exported to ONNX fp32 and packaged for Verdict -- a high-performance serving engine for System One decision models. Serve it with one command: Pick the image that matches the host. The -v mounts cache the model, so only the first run downloads it. Add -e VERDICTUSEMODELSCOPE=1 and use sgyaqing/laya-multilingual to fetch from ModelScope instead of the hub; add -e HFENDPOINT=https://hf-mirror.com if the hub is slow or unreachable. For English-only workloads, use verdict-models/laya. Endpoints: the standard decision protocols POST /v1/systemone and POST /v1/decisions, native POST /v1/choice | /v1/score | /v1/noul… Open weights apache-2.0 laya [View model](https://savrn.com/models/laya-multilingual-3) Model ### [laya-multilingual-UNSW-NB15](https://savrn.com/models/laya-multilingual-unsw-nb15) [Anguswwww](https://savrn.com/model-publishers/anguswwww) Open weights apache-2.0 322M parameters laya [View model](https://savrn.com/models/laya-multilingual-unsw-nb15) Model · Text classification ### [laya-multilingual-coreml](https://savrn.com/models/laya-multilingual-coreml) [Aac6fef](https://savrn.com/model-publishers/aac6fef) Laya typed decisions on Apple Silicon, using CPU + GPU. This is a portable Core ML bundle for laya-coreml, converted from convaiinnovations/laya-multilingual. It outputs choice, score, and noul probabilities with zero generated tokens. Inference needs no PyTorch, Transformers, MLX, remote code, or cloud API. Apple Silicon, macOS 15+, Python 3.11–3.13. Tested on M3 Max / macOS 27.2. To download explicitly and then run entirely offline: Use laya.load("aac6fef/laya-multilingual-coreml", localfilesonly=True) for a cached snapshot or pass a local directory. Use revision=" " to pin a remote revision. This FP16 export retains the original model architecture and decision schema. The… Open weights apache-2.0 coreml [View model](https://savrn.com/models/laya-multilingual-coreml) Model · Text classification ### [laya-multilingual-coreml-ane](https://savrn.com/models/laya-multilingual-coreml-ane) [Aac6fef](https://savrn.com/model-publishers/aac6fef) Laya typed decisions on Apple Silicon, using CPU + Neural Engine. This is a portable Core ML bundle for laya-coreml, converted from convaiinnovations/laya-multilingual. It outputs choice, score, and noul probabilities with zero generated tokens. Inference needs no PyTorch, Transformers, MLX, remote code, or cloud API. Apple Silicon, macOS 15+, Python 3.11–3.13. Tested on M3 Max / macOS 27.2. To download explicitly and then run entirely offline: Use laya.load("aac6fef/laya-multilingual-coreml-ane", localfilesonly=True) for a cached snapshot or pass a local directory. Use revision=" " to pin a remote revision. This FP16 conversion retains the original trained parameters. The ANE graph's host… Open weights apache-2.0 coreml [View model](https://savrn.com/models/laya-multilingual-coreml-ane) Model · Text classification ### [laya-multilingual-coreml-ane-w8](https://savrn.com/models/laya-multilingual-coreml-ane-w8) [Aac6fef](https://savrn.com/model-publishers/aac6fef) Laya typed decisions on Apple Silicon, using CPU + Neural Engine. This is a portable Core ML bundle for laya-coreml, converted from convaiinnovations/laya-multilingual. It outputs choice, score, and noul probabilities with zero generated tokens. Inference needs no PyTorch, Transformers, MLX, remote code, or cloud API. Apple Silicon, macOS 15+, Python 3.11–3.13. Tested on M3 Max / macOS 27.2. To download explicitly and then run entirely offline: Use laya.load("aac6fef/laya-multilingual-coreml-ane-w8", localfilesonly=True) for a cached snapshot or pass a local directory. Use revision=" " to pin a remote revision. This is a separately identified 8-bit grouped K-means weight-palette variant.… Open weights apache-2.0 coreml [View model](https://savrn.com/models/laya-multilingual-coreml-ane-w8) Model · Text classification ### [laya-multilingual-coreml-snake](https://savrn.com/models/laya-multilingual-coreml-snake) [Aac6fef](https://savrn.com/model-publishers/aac6fef) Laya typed decisions on Apple Silicon, using CPU + GPU. This is a portable Core ML bundle for laya-coreml, converted from convaiinnovations/laya-multilingual. It outputs choice, score, and noul probabilities with zero generated tokens. Inference needs no PyTorch, Transformers, MLX, remote code, or cloud API. Apple Silicon, macOS 15+, Python 3.11–3.13. Tested on M3 Max / macOS 27.2. To download explicitly and then run entirely offline: Use laya.load("aac6fef/laya-multilingual-coreml-snake", localfilesonly=True) for a cached snapshot or pass a local directory. Use revision=" " to pin a remote revision. This FP16 export retains the original model architecture and decision schema. The… Open weights apache-2.0 coreml [View model](https://savrn.com/models/laya-multilingual-coreml-snake) Model ### [laya-multilingual-onnx-fp16](https://savrn.com/models/laya-multilingual-onnx-fp16) [Duy Nguyen](https://savrn.com/model-publishers/firefox66) Open weights [View model](https://savrn.com/models/laya-multilingual-onnx-fp16) Model · Text classification ### [laya-nextflow-nfcore](https://savrn.com/models/laya-nextflow-nfcore) [PrimeOmicX LLC](https://savrn.com/model-publishers/primeomicx) Laya v3 is an ultra-fast, local System-1 Decision Engine fine-tuned on top of ModernBERT-Large for Nextflow DSL2 and the nf-core bioinformatics ecosystem. It is designed to make zero-latency, high-accuracy architectural decisions in autonomous pipeline compilers (such as Codaris), resolving complex setup questions without burning LLM tokens. Evaluated across 40 realistic Nextflow architecture tasks Open weights mit 421M parameters [View model](https://savrn.com/models/laya-nextflow-nfcore) Model · Any to any ### [laya-omni](https://savrn.com/models/laya-omni) [Zheqiushui](https://savrn.com/model-publishers/zheqiushui) Laya answers typed decisions (choose one option, give a score, answer yes/no) with calibrated probabilities. laya-omni adds optional image and audio inputs. Without an image or audio clip, the output is bit-identical to Laya. Qwen3-ASR-0.6B audio encoder extracted from the full ASR model (audioencoder/). base/16-256 (~93M), Qwen3-ASR audio encoder (186M). Several images: image=[a, b]. detail=True uses 256 image tokens instead of 64 (better for text, documents and spatial questions). One question with an image or a 10 s clip takes 30-120 ms on an RTX 4090, encoders included. Standard benchmarks, same questions, one RTX 4090, one question at a time from the image or audio file to the answer… Open weights other laya-omni [View model](https://savrn.com/models/laya-omni) Model · Text classification ### [laya-onnx](https://savrn.com/models/laya-onnx) [weXare](https://savrn.com/model-publishers/wexare) An unofficial ONNX export of convaiinnovations/laya, the open-source "System One" decision model by Convai Innovations, made to run client-side with ONNX Runtime Web on WebGPU or WebAssembly. Laya takes a state and typed questions and scores every option in one forward pass. It returns calibrated probabilities, not generated text. This is the default model of @wexare/laya-web (source), which loads it automatically: sha256 of layafp16.onnx: b2125c7a1044e78807ee718ff4e65c9892b6b55aeeeee64f31fffffd76871b0a Batch, sequence and option count are all dynamic. Masked option slots return -1e4. Turning logits into answers (per-option-count temperature, softmax, confidence) follows the reference laya… Open weights apache-2.0 onnx [View model](https://savrn.com/models/laya-onnx) Model · Text classification ### [laya-pdpa-onnx-v4-32k](https://savrn.com/models/laya-pdpa-onnx-v4-32k) [MY](https://savrn.com/model-publishers/suarify) ONNX export of Suarify/laya-pdpa-my-v4 — a Malaysian PDPA classifier for call transcripts. Ten typed questions per call: eight yes/no classes, one ordered severity, one choice for who disclosed. Exported with torch.onnx.export(dynamo=True), opset 17, external data. Accuracy is over all 960 held-out decisions (96 rows × 10 questions), by argmax, against a majority-class baseline of 72.8%. Parity of the fp32 export against the PyTorch checkpoint is 1.07e-05 max absolute logit difference across batches 1, 3, 8 and 16. fp32 and q4f16 both score 825/960. That identical total is a coincidence of to 10.15 absolute logit difference, and the flips happen to break 23 each way. The disagreements are… Open weights apache-2.0 [View model](https://savrn.com/models/laya-pdpa-onnx-v4-32k) ## 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.