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In this tutorial, we implement a Gin Config –controlled PyTorch experiment pipeline in which the executable training code remains stable. At the same time, the experimental degrees of freedom are moved into declarative configuration files. We construct a nonlinear spiral binary classification task, define a configurable MLP with scoped architectural variants, and expose parameters for the optimizer, scheduler, loss, batching, seeding, and training loop via @gin.configurable bindings. We use Gin’s scoped references to instantiate separate model configurations, runtime bindings to override selected parameters without editing source code, and operative config export to capture the exact resolved configuration that produces each training run. Installing Gin Config and Building the Spiral Dataset Copy Code Copied Use a different Browser !pip -q install gin-config import os import json import math import random import textwrap from pathlib import Path import gin import numpy as np ...

Google released LiteRT.js, a JavaScript binding of LiteRT. LiteRT is Google’s on-device inference library, previously called TensorFlow Lite. LiteRT.js runs .tflite models directly inside the browser. Because inference stays local, Google cites enhanced user privacy, zero server costs, and ultra-low latency. What is LiteRT.js? It is not a new model format. Rather, Google compiled its existing native runtime to WebAssembly and exposed it to JavaScript. Earlier web AI solutions, including TensorFlow.js, relied on JavaScript-based kernels. Google describes those as less performant. LiteRT.js instead ships the native cross-platform runtime with its optimizations intact. Consequently, web apps inherit work done elsewhere. Performance upgrades, quantization improvements, and hardware optimizations built for Android, iOS, and desktop arrive on the web too. How It Works: One Runtime, Three Backends Under that runtime, LiteRT.js targets three backends: C...

PrismML just released Bonsai 27B . It is a low-bit representation of Qwen3.6-27B, not a new pretrain. The architecture is unchanged. Two variants ship under Apache 2.0. Ternary Bonsai 27B uses {−1, 0, +1} weights at a true 1.71 bits per weight. Its ideal size is 5.9GB. 1-bit Bonsai 27B uses binary {−1, +1} weights at 1.125 bits per weight, for 3.9GB. Both are multimodal. The split is ~24.8B language weights, a 0.46B vision tower, and 2.5B in embeddings and the LM head. The vision tower is held separately at 4-bit (HQQ). Context is 262K tokens, kept practical because ~75% of Qwen3.6-27B attention is linear. That architecture shapes the compression method below. How the Compression Works Each weight is a code, with one shared FP16 scale per group of 128. The effective weight is w_i = s_g · t_i . A ternary value carries log2(3) ≈ 1.585 bits. One FP16 scale per 128 weights adds 16/128 , giving ≈1.71 bits per weight. That is a ~9.4× reduction against F...

Hayden Bleasel , an expert developer from OpenAI, released Blume , an open-source documentation framework. Blume shipped to npm as version 1.0.3 the same day. It is as simple as Drop Markdown into a folder and ship a docs site. No app boilerplate is written or maintained afterward. The project is MIT-licensed and open sourced. What is Blume? Blume is a command-line tool paired with a component library for docs. It reads a folder of Markdown or MDX files. From that folder, it produces a production-grade documentation site. That output ships navigation, search, theming, and Open Graph images. Configuration stays optional and is added one file at a time. The code is a TypeScript monorepo; the published package sits at packages/blume . Blume’s own documentation, under apps/docs , is built with Blume itself. It requires Node.js 22.12 or newer. It runs with Bun, pnpm, npm, or yarn. How Blume Works ? Under the surface, Blume generates and drives a hidden Astro project. ...