mizorewww/laya-coreml

选用指南

AI 根据 README 整理 · 2026/10/16 21:11:32

使用方式

通过 Python API 或命令行使用，例如 `laya-coreml-snake` 运行演示，或使用 `laya.load` 加载模型进行预测。

查看 README 依据

compiled MLX FP16. A separately validated W8 palette variant reached 4.88 ms and 3.19× energy improvement. These are single-question results, not full Snake frame times; the requested 10× improvement was not achieved. ## Run the demo Apple Silicon · macOS 15+ · Python 3.11–3.13. ```bash pip install 'laya-coreml[demo]' hf download aac6fef/laya-multilingual-coreml-ane --local-dir models/snake laya-coreml-snake --model ./models/snake ``` Download once, then play offline. No PyTorch, Transformers or MLX is needed for

inference. The terminal needs 104 columns × 35 rows. Space pauses; ↑/↓ changes speed; R resets; Q quits. First-time Core ML initialization can take tens of seconds. [Controls, recording and video export](https://github.com/mizorewww/laya-coreml/blob/main/docs/SNAKE_DEMO.md) · [Measured stable decision rates](https://github.com/mizorewww/laya-coreml/blob/main/docs/SNAKE_BENCHMARKS.md) · [Shareable video and recording provenance](https://github.com/mizorewww/laya-coreml/blob/main/docs/LAUNCH.md) ## Ask for a decision ```bash pip install laya-coreml ``` ```python import laya_coreml as laya

部署要求

需要 Apple Silicon、macOS 15+、Python 3.11–3.13。

查看 README 依据

compiled MLX FP16. A separately validated W8 palette variant reached 4.88 ms and 3.19× energy improvement. These are single-question results, not full Snake frame times; the requested 10× improvement was not achieved. ## Run the demo Apple Silicon · macOS 15+ · Python 3.11–3.13. ```bash pip install 'laya-coreml[demo]' hf download aac6fef/laya-multilingual-coreml-ane --local-dir models/snake laya-coreml-snake --model ./models/snake ``` Download once, then play offline. No PyTorch, Transformers or MLX is needed for

已知限制

ANE 模型有 96-token 总限制，长文本需使用 1024-token 模型；W8 变体是近似压缩，可能影响精度。

查看 README 依据

`local_files_only=True` to require an existing cache, or load a local directory. Following upstream v0.3.5, fitted calibration temperatures are clamped to `[0.5, 5.0]` before use: the shipped `choice:11+` bucket is 0.1006, which would sharpen logits ~10x and report a coin flip as near-certainty. The checkpoint's raw values remain available as `agent.temperature_raw` and `agent.temperature_by_options_raw`, and a `RuntimeWarning` names every clamped bucket at load. The ANE bundle has a **96-token total limit**, including question, options and

| Speed gain | 1× | **1.39×** | **1.42×** | | System energy gain | 1× | **2.78×** | **3.19×** | Energy uses direct SMC PSTR sensor readings, with raw samples and explicit anomaly rejection. This is an estimate with sensor and background-load uncertainty. **Speed gain × average power ratio = energy gain**; multiplying energy by speed again would double-count time. The W8 variant compresses weights while retaining FP16 compute. It is approximate, and its package-size reduction is not a speed ratio.