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consult-architecture

Analyze Mamba2 architecture constraints for VRAM and compute feasibility.

Updated Feb 24, 2026
Search Tags:#architecture#compatibility#experiment-design#training-costs#vram#ssm#mamba2
One-click install
npx skills add https://github.com/ScavieFae/autonomous-world-model --skill consult-architecture
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: consult-architecture
Source: https://github.com/ScavieFae/autonomous-world-model/tree/main/.claude/skills/consult-architecture
Command: npx skills add https://github.com/ScavieFae/autonomous-world-model --skill consult-architecture

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guide architecture-aware experiment design for Mamba2 state-space models, translating theoretical ideas into feasible training configurations and resource estimates.

Core Features & Use Cases

  • Analyze compatibility of techniques with Mamba2 without relying on transformer-specific assumptions.
  • Estimate VRAM and compute implications of architectural changes (e.g., d_state, context length, number of layers) within given hardware constraints.
  • Connect architectural questions to concrete code references (models/mamba2.py, models/encoding.py, training/trainer.py) to ground recommendations.
  • Use cases include evaluating new loss designs, input encodings, and experiment scope decisions to inform design and risk assessment.

Quick Start

Ask it to evaluate whether a proposed architectural change is feasible within available VRAM and how it would impact training stability.

Frequently Asked Questions about consult-architecture

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate VRAM requirements for training a Mamba2 architecture model?▼

Estimating Mamba2 VRAM requirements involves analyzing architectural parameters like d_state, context length, and layer count against hardware constraints. This skill evaluates compute implications to determine if proposed training configurations are feasible.

Can I apply transformer-specific techniques and loss designs to Mamba2 state-space models?▼

Techniques designed for transformers often require adaptation for Mamba2 compatibility. This skill analyzes whether proposed loss designs and input encodings align with state-space model constraints instead of relying on transformer-specific assumptions.

What is the best way to evaluate architecture changes for Mamba2 experiments?▼

Evaluating Mamba2 architecture changes involves grounding recommendations in concrete code references like models/mamba2.py and training/trainer.py. This skill connects architectural questions to code references to assess experiment scope and training stability.

Does increasing context length in Mamba2 impact training stability and compute tradeoffs?▼

Increasing context length in Mamba2 directly impacts VRAM usage and compute tradeoffs. This skill analyzes how adjustments to context length and d_state affect training stability and resource costs within your specific hardware constraints.

How do I adjust training regimes for Mamba2 state-space models?▼

Adjusting training regimes for Mamba2 involves analyzing compatibility with model encoding and trainer references. This skill assesses how regime adjustments interact with the architecture to inform experiment design and risk evaluation.

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