Can I convert existing models to BitNet using this skill?

Yes, the skill provides Quantization-Aware Training (QAT) scripts and Straight-Through Estimators (STE) to help convert and train models in a ternary weight format.

Does this skill require specific hardware?

Yes, this skill is optimized for Apple Silicon (M-series chips) as it utilizes the MLX framework developed by Apple for efficient array processing.

What is a 1.58-bit LLM?

A 1.58-bit LLM is a Large Language Model where weights are constrained to ternary values (-1, 0, +1). This requires log2(3) bits per parameter, significantly reducing memory and compute requirements while maintaining performance.

What is the role of GF(3) in this skill?

GF(3) is a finite field with three elements. This skill uses it as a mathematical bridge to map ternary weights to deterministic colors and verify model conservation laws through residue checks.

MLX BitNet GF(3)

Name: MLX BitNet GF(3)
Author: plurigrid

byplurigrid

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Ciencia de Datos y ML

Implements and visualizes 1.58-bit ternary Large Language Models on Apple Silicon using GF(3) logic and color-coded weight analysis.

This skill integrates MLX-based BitNet 1.58-bit inference and training with GF(3) mathematical frameworks to optimize LLM performance on Apple Silicon. By mapping ternary weights to specific color values and conservation laws, it allows developers to perform Quantization-Aware Training (QAT), visualize weight distributions, and trace model activations deterministically. It is an essential tool for researchers and developers building highly efficient, low-memory AI models that leverage the mathematical properties of finite fields for structural integrity and hardware-specific performance.

Características Principales

011.58-bit ternary weight quantization ({-1, 0, +1}) for ultra-low memory footprints.

02Quantization-Aware Training (QAT) with Straight-Through Estimators (STE).

03Conservation law verification to ensure mathematical consistency across model states.

048 GitHub stars

05Deterministic GF(3) color visualization for weight distribution and layer analysis.

06Optimized MLX inference engines specifically tuned for Apple Silicon hardware.

Casos de Uso

01Running high-performance LLMs on memory-constrained Apple Silicon devices.

02Visualizing model internal states and weight distributions during training or inference.

03Researching ternary neural network architectures and GF(3) mathematical properties.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi mlx-bitnet-gf3

For use in Claude.ai and ChatGPT

Download Skill