Creating AI fine-tuned models

The Fine-Tuned Models section allows you to create fine-tuned models from downloaded models that can then be loaded and used for text generation. Multiple models may be created, but only one text generation may be loaded into memory for use at any given time. This feature is only available on Mac computers with Apple silicon.

Important  You must first enable the model server before the Fine-Tuned Models section will be available. Some models require you to include a Hugging Face token in the Model Server section to download the model. See Enabling the AI model server.

To create a fine-tuned model:

  1. Click the AI Services > Fine-Tuned Models tab.

  2. Click the Create Fine-Tuned Model button.

  3. In the Create a Fine-Tuned Model dialog, click the Browse... button, then browse for the JSON file containing the training data.

    Each line in the training data JSON file should contain an example of a user input and the valid response to the input. For example:

    Copy 
    {"messages":[{"content":"System prompt","role":"system"},{"content":"Example user data","role":"user"},{"content":"Example system output after processing user data"}]}
    {"messages":[{"content":"sys.123","role":"system"},{"content":"usr.234","role":"user"},{"content":"ast.345","role":"assistant"}]}
    {"messages":[{"content":"sys.456","role":"system"},{"content":"usr.567","role":"user"},{"content":"ast.678","role":"assistant"}]}

  4. In the Create a Fine-Tuned Model dialog, enter the following information:

    • Fine-Tuned Model Name: The name of the model you want to use for client connections. FileMaker Server will prefix the specified name with "fm-mlx-" so the actual name of the fine tuned model will be "fm-mlx-<specified_name>".

    • Base Model: Select the model you want to fine-tune.

      Note  Base models must be a text-generation model that is already downlaoded and acknowledged.

    • Number of Iterations: The number of training iterations. More iterations takes more memory and more time to process.

    • LoRA Layers: The number of low-rank adaptation layers (LoRA). More layers takes more memory and time.

    • Batch Size: The number of training examples to train the model with at one time. The larger the batch size, the more memory required, though the training may complete more quickly.

  5. Click the Create Model button.