Open-Weight Models

Model · Sentence similarity

lateon-ours-hardneg-n10573-e1-ckpt82-qd1024

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…

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…

Model · Text classification

laya

Convai Innovations

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…

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…

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…

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…

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…

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…

Model · Text classification

laya-kvp10k-noul

Sothiara Em

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…

Model

laya-moneytrail-injection

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…

Model · Text classification

laya-multilingual

Convai Innovations

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…

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…

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…

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…

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…

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.…

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…

Model · Text classification

laya-nextflow-nfcore

PrimeOmicX LLC

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

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…

Model · Text classification

laya-onnx

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…

Model · Text classification

laya-pdpa-onnx-v4-32k

MY

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…