Welcome to the world of AI model merging! In this blog, we will guide you through the steps of creating a powerful AI model known as OrcaHermes-Mistral-70B by merging two existing Miqu models. So, let’s dive in!

Understanding OrcaHermes-Mistral-70B

The OrcaHermes-Mistral-70B model is an interesting experiment that combines two high-performing models trained on distinct datasets. Just like creating a gourmet dish by carefully selecting and merging specific ingredients, this model is a blend of the best attributes of two separate models: alicecomfymiqu-openhermes-full and ShinojiResearchSenku-70B-Full.

How to Merge Models

The merging process requires a configuration setup detailed in a YAML file. Here’s a breakdown of creating the configuration:

yamlslices:
  - sources:
      - model: localpathtoSenku-70B-Full
        layer_range: [0, 80]
      - model: localpathtomiqu-openhermes-full
        layer_range: [0, 80]
merge_method: slerp
base_model: localpathtoSenku-70B-Full
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 # fallback for rest of tensors
dtype: float16

Breaking Down the Configuration

This YAML configuration is like a recipe that tells the model how to combine the ingredients:

• Sources: Specifies the two model paths and the layers being merged (0 to 80).
• Merge Method: The slerp (spherical linear interpolation) technique is used here, which smoothly blends features from the two models.
• Base Model: Identifies the primary model on which the merging is based.
• Parameters: This detail indicates how much influence each model’s filters should have on the final output, similar to adjusting spice levels in a dish.
• Data Type: Specifies that the calculations use a 16-bit float for efficiency.

Troubleshooting Tips

While merging models can be seemingly straightforward, challenges may arise. Here are some common issues and how to resolve them:

• Incorrect Model Paths: Ensure that the model paths in your YAML configuration are correct and accessible.
• Layer Range Errors: Double-check that the specified layer ranges do not exceed the total number of layers in the models.
• Memory Issues: If you encounter memory errors, try using a model with less depth or reduce the dtype to float32.

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Conclusion

We hope this guide helps you seamlessly merge AI models with maximum efficiency! 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.