![]() This tool accepts files in the following data formats, if they contain a prompt and a completion column/key: To analyze your training data with the data preparation tool, run the following Python command, replacing with the full path and file name of the training data file to be analyzed: openai tools fine_tunes.prepare_data -f To install the CLI, run the following Python command: pip install -upgrade openai OpenAI has developed a tool that validates, gives suggestions, and reformats your data into a JSONL file ready for fine-tuning. We recommend using OpenAI's command-line interface (CLI) to assist with many of the data preparation steps. In general, we've found that each doubling of the dataset size leads to a linear increase in model quality.įor more information about preparing training data for various tasks, see Learn how to prepare your dataset for fine-tuning. We recommend having at least 200 training examples. The more training examples you have, the better. You don't need to give detailed instructions or multiple completion examples for the same prompt. For fine-tuning, we recommend that each training example consists of a single input prompt and its desired completion output. Prompts for completion calls often use either detailed instructions or few-shot learning techniques, and consist of multiple examples. Creating your training and validation datasetsĭesigning your prompts and completions for fine-tuning is different from designing your prompts for use with any of our GPT-3 base models. For more information about formatting your training data, see Learn how to prepare your dataset for fine-tuning. In addition to the JSONL format, training and validation data files must be encoded in UTF-8 and include a byte-order mark (BOM), and the file must be less than 200 MB in size. ![]() Here's an example of the training data format: The OpenAI command-line interface (CLI) includes a data preparation tool that validates, gives suggestions, and reformats your training data into a JSONL file ready for fine-tuning. The training and validation data you use must be formatted as a JSON Lines (JSONL) document in which each line represents a single prompt-completion pair. Your training data and validation data sets consist of input & output examples for how you would like the model to perform. Prepare your training and validation data ![]() Optionally, analyze your customized model for performance and fit.Check the status of your customized model.Review your choices and train your new customized model.Optionally, choose advanced options for your fine-tune job. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |