This repo contains OPAL Quants of FINAL-Bench/Darwin-36B-Opus

👉 APEX Quantization Method
Ettore Di Giacinto & Richard Palethorpe (LocalAI Team). OPAL adapts the layer-wise precision gradients and MoE-aware tensor classification outlined in the APEX technical paper.

👉 Bartowski and Lamim
For the excellent semantic imatrix calibration dataset that powers OPAL's activation scaling.

👉 llama.cpp
Georgi Gerganov and contributors for the foundational inference and quantization engine.

👉 HuggingFace Accelerate
For the init_empty_weights() context manager that makes the 0-RAM "Ghost Model" possible.

👉 Jackrong
For the sexy markdown readme inspo.

A coffee in Ethereum would be cool! Although I don't drink coffee—I think it tastes like burnt water—but a pink lemonade would be fire! 🔥