base-model-selector

What problem does it solve?

This Skill prevents wasted resources by ensuring you select the most appropriate base Large Language Model (LLM) for your fine-tuning project before you begin the costly process of data generation and training.

Core Features & Use Cases

• Systematic Model Research: Guides an exhaustive search for candidate LLMs based on size, architecture, and domain fit.
• Baseline Evaluation: Establishes a performance benchmark for potential base models against your specific domain rubric.
• Informed Decision Making: Provides clear criteria for selecting a primary and backup model, or deciding if fine-tuning is even necessary.
• Use Case: Before fine-tuning a model for therapeutic coaching, use this Skill to research and evaluate models like Qwen, Llama, and Mistral, determining which one offers the best starting point for conversational quality and empathy.

Quick Start

Use the base-model-selector skill to research and evaluate LLM candidates for a new fine-tuning project.