---
type: concept
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  contentHash: 4868607238af9fbaa8e763f265e1bf0652042b61e614cf93f887aa396f970a7f
  pageDirectory: concepts
  sources:
    - load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md
  confidence: 0.85
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - data-preparation-for-llm-fine-tuning-with-hugging-face
    - DPFLFWHF
    - Prepare Data for Fine-Tuning Hugging Face Models
    - Prepare data for fine tuning Hugging Face models
    - dataset preparation for fine-tuning
  citations:
    - file: load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md
title: Data Preparation for LLM Fine-Tuning with Hugging Face
description: Preparing data for fine-tuning open-source large language models using Hugging Face Transformers and Datasets libraries on Databricks.
tags:
  - hugging-face
  - llm
  - fine-tuning
  - databricks
timestamp: "2026-06-19T19:13:15.593Z"
---

# Data Preparation for LLM Fine‑Tuning with Hugging Face

**Data Preparation for LLM Fine‑Tuning with Hugging Face** refers to the process of loading, formatting, and transforming datasets so that they can be used to fine‑tune open‑source large language models (LLMs) using the Hugging Face Transformers library and the Hugging Face Datasets library. This workflow is commonly executed on platforms like Databricks, which provides integrated storage and compute for machine learning and deep learning workloads. ^[load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md]

## Overview

The data preparation step is a critical part of any LLM fine‑tuning pipeline. Databricks documentation explicitly notes that you can prepare your data for fine‑tuning open‑source large language models with Hugging Face Transformers and Hugging Face Datasets. ^[load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md]

The general workflow includes:

1. **Loading raw data** – from [Unity Catalog](/concepts/unity-catalog.md) tables, cloud storage files (e.g., CSV), or other sources.
2. **Converting to a Hugging Face Dataset** – using the `datasets` library to create a `Dataset` or `DatasetDict`.
3. **Tokenizing the text** – applying a tokenizer from a pretrained model checkpoint (e.g., `AutoTokenizer`).
4. **Formatting for training** – ensuring the dataset contains the required fields (e.g., `input_ids`, `attention_mask`, `labels`).
5. **Streaming (optional)** – for very large datasets that do not fit in memory.

Databricks provides a dedicated how‑to guide for this process: [Prepare data for fine tuning Hugging Face models](https://docs.databricks.com/aws/en/machine-learning/train-model/huggingface/load-data). ^[load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md]

## Using Hugging Face Datasets for Streaming

For extremely large datasets that cannot be loaded entirely into memory, the Hugging Face `datasets` library supports **streaming**. When streaming is enabled, data is loaded lazily in small batches, allowing training on datasets that are larger than available RAM. This approach is referenced in the context of distributed deep learning training on Databricks. ^[load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md]

Streaming can be used for datasets stored on the Hugging Face Hub or in cloud storage volumes. The `load_dataset` function with `streaming=True` returns an `IterableDataset` that can be consumed by the training loop. ^[load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md]

## Related Concepts

- Hugging Face Transformers – The core library for loading and fine‑tuning transformer‑based models.
- Hugging Face Datasets – The library for loading, processing, and streaming datasets.
- [Distributed deep learning training](/concepts/distributed-deep-learning-training-on-databricks.md) – Training across multiple GPUs or nodes, often requiring streaming data pipelines.
- [Unity Catalog](/concepts/unity-catalog.md) – Databricks governance solution for managing and accessing data assets.
- PyTorch IterableDataset – A PyTorch native approach for custom streaming logic.

## Sources

- load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md

# Citations

1. [load-data-for-machine-learning-and-deep-learning-databricks-on-aws.md](/references/load-data-for-machine-learning-and-deep-learning-databricks-on-aws-dfd2be96.md)
