# Parameters Guide ## Fine-Tuning Parameters Guide Adjusting the fine-tuning configuration parameters allows you to customize the training process to better suit your data and use case. Here's a list of the supported parameters and their impact on the fine-tuning process: ### **Learning Rate** * Determines how quickly the model learns. * Lower rates are ideal for minor adjustments, while higher rates speed up learning but risk overshooting. * Default value: **0.0001** ### **Batch Size** * Specifies how much data is processed simultaneously. * Smaller batch sizes can improve accuracy but take longer to train. * Default value: **16** ### **Epochs** * Indicates how many times the model will iterate through the entire dataset. * More epochs can improve accuracy but increase computation time. * Default value: **10** epochs ### **LoRA Rank** * The rank of the low-rank adaptation matrix. * Higher ranks can capture more information but require more memory and computation. * Default value: **16** --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.getdynamiq.ai/llm-fine-tuning/parameters-guide.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.