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kali20gakki
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model-adapt

Create and validate Transformer model adapters with a four-step verification workflow.

7|3|Updated Mar 6, 2026
Search Tags:#interfaces#verification#mlops#adapter#transformers#model-adapter
One-click install
npx skills add https://github.com/kali20gakki/mindstudio-skills --skill model-adapt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-adapt
Source: https://github.com/kali20gakki/mindstudio-skills/tree/main/msmodelslim-model-adapt
Command: npx skills add https://github.com/kali20gakki/mindstudio-skills --skill model-adapt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, transformers, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a structured blueprint to create and validate Transformer model adapters, including required interfaces and a four-step verification workflow, reducing integration risk and speeding up deployment.

Core Features & Use Cases

  • Standardized adapter scaffold: Implements handle_dataset, init_model, generate_model_visit, generate_model_forward, and enable_kv_cache with templates.
  • Verification & registration: Includes four-step validation, config.ini registration guidance, and example scripts/references for reproducibility.
  • Use Case: For teams building decoder-only LLMs or text-focused VLM adapters, enabling consistent adaptation and testing across models.

Quick Start

Provide a model specification and run the 4-step verification to bootstrap an adapter.

Frequently Asked Questions about model-adapt

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

FAQPage Schema
How do I create a Transformer model adapter for a decoder-only LLM?▼

To create a Transformer model adapter, implement the five required interfaces: handle_dataset, init_model, generate_model_visit, generate_model_forward, and enable_kv_cache. A template-driven workflow provides the necessary scaffold to bootstrap adapter implementation.

What is the four-step verification workflow for model adapters?▼

The four-step verification workflow validates adapter implementability and stability. It guides you through required interface enforcement, config.ini registration, and template-driven testing using provided scripts and references to ensure consistent adaptation across models.

Can I use this adapter scaffold for text-focused Vision Language Models?▼

Yes, the adapter scaffold applies to both decoder-only LLMs and text-focused VLMs. It enforces the five required interfaces and config registration to ensure consistent adaptation and testing across different Transformer model architectures.

What dependencies do I need to validate Transformer adapters?▼

Validating Transformer adapters requires the torch and transformers dependencies. These frameworks support the implementation of the five required interfaces and the execution of the template-driven verification scripts.

How do I register a model adapter configuration?▼

Register a model adapter by adding its configuration to the config.ini file. The Skill provides registration guidance and example references to ensure the adapter is properly documented and reproducible within the MLOps workflow.

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