model-merging

What problem does it solve?

This Skill solves the problem of creating specialized LLMs by combining capabilities from multiple fine-tuned models. It bypasses the need for expensive and time-consuming retraining, which often leads to catastrophic forgetting of previously learned skills.

Core Features & Use Cases

• Capability Blending: Seamlessly combine expertise from multiple fine-tuned models (e.g., mathematical reasoning, code generation, conversational ability) into a single, powerful model.
• Performance Improvement: Often achieve +5-10% performance gains on benchmarks compared to individual parent models, creating synergistic effects.
• Rapid Experimentation: Create and test new model variants in minutes on a CPU, drastically accelerating the development and iteration cycle.
• Cost-Effective: Perform complex model combinations without requiring expensive GPUs for the merging process itself.
• Use Case: Blend a math-specialized LLM, a coding LLM, and a general chat LLM to create a single AI assistant that excels across all three domains, ready for deployment in minutes.

Quick Start

Perform a simple linear merge of Mistral-7B-v0.1 and OpenHermes-2.5-Mistral-7B with equal weights (0.5 each) to combine their capabilities into a new model.