onnx

作者在博客中演示了如何完全在浏览器端运行 RAG：用静态嵌入检索段落，再通过 transformers.js 在 WebGPU/WASM 上运行小型模型（如 Qwen3-0.6B、SmolLM2-360M）生成两句话摘要，全程数据不出本地。文章还评估了这些小模型的忠实度局限。

Datalab 发布 Indra OCR——一个仅约 1M 参数的轻量 OCR 模型，在 CPU 上每秒可处理多达 49 页，速度比 Tesseract 和 PaddleOCR 快 11-24 倍，可将 PDF 和图片转换为 Markdown、纯文本或带框的 JSON。

A 1.4M-parameter U-Net student model, distilled from FLUX.2 klein, achieves real-time 30-40 FPS neural weather restyling (snow, wet, night) inside a Minecraft Fabric mod on a budget GTX 1650, using a PatchGAN fine-tune to fix washed-out outputs from pure pixel losses.

Talus is a 23M-parameter pixel-space diffusion model that generates 64x64 game terrain heightmaps conditioned on terrain type and user-selected measurable properties, trained from scratch on a single RTX 5060 and evaluated against a real-vs-real noise floor. It runs in the browser via ONNX Runtime Web on WebGPU (~3s per map) with Apache-2.0 open-sourced code and weights.

Neuphonic open-sources NeuDecide under Apache 2.0, a 43 MB audio-to-tool-call model that skips transcription, achieving 72.4% tool accuracy on SLURP while running on a single CPU thread with a browser WASM demo.

A Chrome extension that filters YouTube feeds using a small on-device ONNX decision model (opendecider-nano) running via WebGPU/WASM with no server or API calls. On 400 videos it achieves 0.934 balanced accuracy on rules like "hide music" or "only news", and can also flag prompt injection in selected text.

Sopro V2 Turbo is a 120M-parameter open-source TTS model with zero-shot voice cloning that streams with ~300ms time-to-first-audio on laptop CPU and runs in-browser via ONNX, reaching SOTA-level intelligibility despite its small size.

BeeNara is a 332MB ONNX cross-encoder model for document sorting that uses split-conformal prediction to provide calibrated 'none fits' responses for uncertain classifications.

A lightweight TTS implementation, replicating Audio8's training with 2000 hours of data, achieving a SIM metric of 0.72 in under 10 hours of training on H200.

Netron is an open-source visualizer for neural network, deep learning, and machine learning model files, supporting formats like ONNX, PyTorch, TensorFlow, and more via browser, desktop, or Python.

A tutorial on building a real-time object detection and tracking pipeline for robotics using ROS 2 and YOLOv11, covering threaded inference, ByteTrack integration, confidence validation, and ONNX export for edge deployment.

Ultralight Digital Human is an open-source Python project that lets you train a person-specific, audio-driven talking head from a 3–5 minute video, with support for HuBERT/WeNet audio features, ONNX export, and streaming inference.

They released a unified multi-head security classifier (mmBERT-small encoder with seven heads) using masked losses for absent task labels, achieving high F1 scores across seven tasks, and also released quantized ONNX INT8 variants. Weights and metrics are public.

Introduces qZACH-ViT, a quantization-aware extension of ZACH-ViT with recursive intrinsic explanations, and Recursive Attribution-Stabilized Optimization (RASO) for stable attribution gradients. Achieves high prediction agreement and speedups on MedMNIST datasets after INT8 conversion.

Explores the performance of running a voice assistant with Qwen3-ASR and Kokoro-TTS ONNX models on CPU, measuring response times without a GPU.

Manticore Search 27.1.5 introduces a new ONNX Runtime backend for embeddings that achieves ~14x faster performance than the previous SentenceTransformers/Candle path, with throughput ranging from 70-230 docs/sec compared to 5-11 docs/sec, and no API changes required.

Simon Willison ported the Moebius 0.2B image inpainting model to run in the browser using WebGPU and ONNX Runtime, assisted by Claude Code. The resulting demo allows users to upload images and remove objects via inpainting.

archex is a local-first, deterministic tool that builds token-budgeted code context bundles for AI agents, using a full retrieval pipeline (BM25F, local embeddings, cross-encoder reranker, dependency-graph expansion) on your hardware with no API keys or telemetry, outperforming alternatives in recall and efficiency.

OpenCV 5 is a major release of the computer vision library, featuring a new DNN engine, improved ONNX support, hardware acceleration, and a cleaner architecture. It represents a significant modernization for developers working with classical vision, deep learning, and edge deployment.

NVIDIA released an optimized ONNX version of the Kokoro TTS model on Hugging Face. The 82M parameter model is lightweight, fast, and ready for commercial use.