Quick start
For Fine-tuning LLMs

Quick start for fine-tuning LLMs

MOSTLY AI integrates with well-known LLMs and gives the flexibility to use any LLM to generate privacy-safe synthetic text. When included in a tabular dataset, synthetic text data is intelligently aligned with the rest of the data, ensuring high correlation while safeguarding the privacy of real-world individuals.

MOSTLY AI for fine-tuning LLMs

Step 1: Train a generator to fine-tune an LLM

Prerequisites

  • Dataset with unstructured text in a supported format. The dataset you will use for fine-tuning must be CSV, Parquet, TSV, where one or more of the columns contains unstructured text.
  • Size of the training dataset. Depending on the size of the dataset and the compute resources you use for training, it is possible that the generator training can fail with out-of-memory errors. For details, see Troubleshooting.

Steps

  1. Create a new generator and upload a table file or add a table from a data source that contains unstructured text.
  2. On Data configuration page, for the column containing unstructured text, select Language/Text from Encoding type. MOSTLY AI Unstructured text - Select Language/Text as Encoding type
  3. Click Configure models in the upper right.
  4. On the Model configuration page, expand the :language model and configure it.

    1. For Model, select one of the available language models for training.

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      The list of available models includes HuggingFace text generation models we have tested as well as the not-pre-trained MOSTLY AI LSTM model.

      If you need to use another LLM, contact MOSTLY AI Support.

      MOSTLY AI Unstructured text - Select language model to use

    2. For Compute, select an available compute. We recommend the use of a GPU-enabled compute for language models.

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      From Compute, you select a compute from a list of compute resources configured for the MOSTLY AI application. The compute is based on the resources available in the compute cluster where MOSTLY AI is running.

      • CPU-based computes offer a specific number of CPU cores and memory. Typically, they perform better when assigned to tabular data computations.
      • GPU-based computes include a specific number of GPU cores and GPU memory. For language model fine-tuning and language generation, it is typically faster to use GPU computes.
      MOSTLY AI Unstructured text - Select compute
  5. Click Start training in the upper right. MOSTLY AI Unstructured text - Start training

Step 2: Generate text

  1. Open the trained generator and click Generate data in the upper right. MOSTLY AI Unstructured text - Click Generate data
  2. Configure the generation.
    1. For Tabular compute, select the compute you want to use for tabular data. For tabular data, it is usually best to use a CPU-based compute.
    2. For Language compute, select the compute you want to use for tabular data. For language (unstructured text), GPU-based computes tend to be faster.
    3. (Optional) If needed, adjust the rest of the generation options.
  3. Click Start generation in the upper right. MOSTLY AI Unstructured text - Click Start generation

Troubleshooting

Depending on your original text dataset, it is possible that the model samples of your trained generator include _INVALID_ text values, or that for particularly long texts, the generator training can fail. Learn how you can troubleshoot such issues.

_INVALID_ values

If you encounter _INVALID_ values in your model samples or generated synthetic text data, this is likely due to the use of the less efficient CPU compute for fine-tuning, insufficient data, or insufficient training time.

Use GPUs for language model fine-tuning

GPUs are in a league of their own when it comes to LLM fine-tuning. To avoid _INVALID_ values, you can use the best practices listed below.

Increase Max training time

If you already use GPUs and still see _INVALID_ values, the next step is to train a new generator with an increased Max training time.

  • Start by training a new generator with Max training time increased to 20 min. The default is 10 min.
  • If you still see _INVALID_ values, increase Max training time to 30 min.

For details, see Increase max training time.

Use a training dataset with shorter texts

Using a dataset with shorter texts or trimming its original lengths can also help to avoid _INVALID_ values.

Generator training failures

Depending on the LLM you use, the size of your text data and its dataset, and the compute resources available for fine-tuning, generator training might fail with out-of-memory errors. To troubleshoot, try the suggestions below in the order listed.

  1. Set Batch size to 2 or 4. For details on how to set batch size, see Increase batch size.
  2. Use a training dataset with shorter texts.