Can I use this for LLM fine-tuning?

Yes, it specifically includes patterns for Parameter-Efficient Fine-Tuning (PEFT) using techniques like LoRA, QLoRA, and DoRA to adapt large models efficiently.

What is gradient isolation?

Gradient isolation involves techniques like freezing weights or using stop-gradient operations to prevent the training of new modules from destabilizing the pre-existing parts of a network.

What are dynamic architectures in machine learning?

Dynamic architectures are neural networks that can grow, prune, or adapt their structure (topology) during the training phase based on performance metrics, rather than having a fixed size from the start.

How does this skill help with catastrophic forgetting?

The skill provides access to continual learning foundations like EWC (Elastic Weight Consolidation), rehearsal strategies, and architectural isolation to ensure new learning doesn't destroy old knowledge.

Dynamic AI Architectures

Name: Dynamic AI Architectures
Author: tachyon-beep

bytachyon-beep

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Data Science & ML

Implements and manages neural networks that adapt, grow, or prune their topology during training to optimize capacity and prevent catastrophic forgetting.

About

This skill serves as a comprehensive orchestration framework for building adaptive neural networks that evolve their structure dynamically during the training process. It provides expert guidance on implementing growing networks, pruning underperforming modules, and managing continual learning to ensure models can scale without losing existing knowledge. By routing to specialized strategies for gradient isolation, PEFT adapters, and modular composition, it enables developers to build sophisticated morphogenetic systems that balance stability and plasticity, ensuring models can efficiently integrate new capabilities without destabilizing existing weights.

Key Features

Adaptive topology management including growing and pruning patterns
7 GitHub stars
PEFT and adapter-based fine-tuning optimization (LoRA, QLoRA)
Continual learning strategies to mitigate catastrophic forgetting
Gradient isolation techniques for stable modular integration
ML lifecycle orchestration via state-machine transition gates

Use Cases

Building resource-efficient networks that prune unnecessary parameters during training
Developing models that learn new tasks sequentially without overwriting previous capabilities
Implementing graftable modular architectures for specialized domain-specific adaptation

About

Key Features

Adaptive topology management including growing and pruning patterns
7 GitHub stars
PEFT and adapter-based fine-tuning optimization (LoRA, QLoRA)
Continual learning strategies to mitigate catastrophic forgetting
Gradient isolation techniques for stable modular integration
ML lifecycle orchestration via state-machine transition gates

Use Cases

Building resource-efficient networks that prune unnecessary parameters during training
Developing models that learn new tasks sequentially without overwriting previous capabilities
Implementing graftable modular architectures for specialized domain-specific adaptation