Ever wondered how to combine the capabilities of different pre-trained language models to create a supercharged version? In this guide, we’ll walk you through the process of merging models using the MergeKit tool. By the end of this article, you’ll be all set to generate amazing text outputs with your newly merged model!

What is MergeKit?

MergeKit is a powerful tool designed to help you merge various pre-trained language models effortlessly. Think of it as the culinary blending of various ingredients to create a gourmet dish. Each model contributes its unique flavor, resulting in a more robust and versatile output.

Step-by-Step Guide to Merging Models

Let’s break down the merging process into digestible steps:

• Install MergeKit: Get the MergeKit package from GitHub here.
• Choose Your Models: Select the models you want to merge. In our case, we are merging several models, which include:
• refuelai/Llama-3-Refueled
• cognitivecomputations/dolphin-2.9-llama3-8b
• NousResearch/Hermes-2-Theta-Llama-3-8B
• NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
• cgato/L3-TheSpice-8b-v0.8.3
• Choose a Base Model: Use the base model, which in our case is Sao10KL3-8B-Stheno-v3.2.
• Configure YAML: Create a YAML configuration file. This file defines how your models will be merged. An example configuration looks like this:

yaml
models:
  - model: refuelaiLlama-3-Refueled
  - model: cognitivecomputationsdolphin-2.9-llama3-8b
  - model: cgatoL3-TheSpice-8b-v0.8.3
  - model: NeverSleepLlama-3-Lumimaid-8B-v0.1-OAS
  - model: NousResearchHermes-2-Theta-Llama-3-8B
merge_method: model_stock
base_model: Sao10KL3-8B-Stheno-v3.2
normalize: false
int8_mask: true
dtype: bfloat16

• Run the Merge: Execute the MergeKit tool with your configuration file. This will produce a merged model ready for use!

Understanding the Code: An Analogy

Imagine you are a chef preparing a fantastic meal. Each model you are using in this recipe offers a unique ingredient – a pinch of knowledge from one, a hint of creativity from another, and so on. The process of merging them is akin to mixing these ingredients harmoniously, to create a dish that is greater than the sum of its parts. The YAML file acts as your recipe that guides you through the exact amounts and methods needed to get the best results in creating your final dish (the merged model).

Evaluation of Merged Models

After merging the models, it’s vital to evaluate their performance on different tasks. The merged model has been evaluated on various datasets, producing results summarized below:

• IFEval (0-Shot): 67.86 strict accuracy
• BBH (3-Shot): 36.41 normalized accuracy
• MATH Lvl 5 (4-Shot): 9.21 exact match
• GPQA (0-shot): 7.38 normalized accuracy
• MuSR (0-shot): 10.97 normalized accuracy
• MMLU-PRO (5-shot): 28.87 accuracy

Troubleshooting Tips

If you encounter issues during the merging process, consider the following:

• Ensure that all dependencies are properly installed and up-to-date.
• Double-check your YAML configuration for any errors in syntax or formatting.
• If the merging process doesn’t yield expected results, experiment with different models or merging methods.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

We hope this step-by-step guide helps you create a powerful merged language model that combines the strengths of various pre-trained models. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.