How does this skill use gzip for scaling?

It utilizes the principle that data compressibility (measured via gzip) predicts optimal scaling laws, allowing the system to adjust parameter vs. data ratios dynamically.

What is the primary purpose of the Supersparsity-Unison skill?

It acts as a bridge between neural network research (like sparsity and mixture of experts) and the Unison programming language, providing patterns for efficient, content-addressed computation.

Can this skill be used for pruning models?

Yes, it implements the Lottery Ticket Hypothesis through 'Regret Pruning,' identifying the most efficient sub-operations (winning tickets) within a computational graph.

What is the TiDAR to Unison mapping?

It maps 'Diffusion Drafting' to Unison's parallel SplitRng operations for generation, and 'Autoregressive Verification' to sequential RegretOp patterns for validation.

Supersparsity Unison

Name: Supersparsity Unison
Author: plurigrid

byplurigrid

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Ciencia de Datos y ML

Bridges neural network sparsity research with Unison’s content-addressed ability system to optimize sparse computation and scaling.

Supersparsity-Unison is a synthesis skill that maps advanced neural network architectures, such as Mixture of Experts (MoE) and TiDAR (Diffusion/Autoregression), into the Unison language's functional framework. It leverages data compression complexity—specifically gzip scaling laws—and the Lottery Ticket Hypothesis to guide efficient compute allocation and ability routing. By translating sparse activation patterns into Unison's content-addressed abilities and splittable RNGs, this skill provides a principled approach for building high-performance, sparse computational models that balance parallel drafting with sequential verification.

Características Principales

01Maps TiDAR architectures to Unison's parallel and sequential computational patterns

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03Implements gzip-based scaling law predictions for optimal compute allocation

04Integrates streaming ZIP implementations for moment-by-moment valid data emission

05Translates Mixture of Experts (MoE) top-k routing into Unison ability handlers

06Identifies computational 'winning tickets' using regret-based pruning and supermasks

Casos de Uso

01Developing sparse neural network architectures within functional programming environments

02Optimizing LLM scaling and resource allocation based on input data complexity

03Implementing efficient draft-and-verify patterns for high-throughput data processing

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi supersparsity-unison

For use in Claude.ai and ChatGPT

Download Skill