---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 147a100914b9acb0b35b0071528f39c2d5b8c0e7817302e4a66f7eae10481551
  pageDirectory: concepts
  sources:
    - fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md
  confidence: 0.95
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - trainingarguments-configuration
    - TrainingArguments
  citations:
    - file: fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md
title: TrainingArguments Configuration
description: Configuration of Hugging Face TrainingArguments for fine-tuning, including output directory, evaluation strategy, learning rate, and memory optimization settings.
tags:
  - huggingface
  - training-configuration
  - hyperparameters
timestamp: "2026-06-19T18:49:39.921Z"
---

# TrainingArguments Configuration

**TrainingArguments Configuration** refers to the setup of the Hugging Face `transformers` library's `TrainingArguments` class, which allows users to specify training parameters such as output directory, evaluation strategy, learning rate, and other hyperparameters when fine-tuning models. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Overview

The `TrainingArguments` class is a core component of the Hugging Face Trainer utility. It provides a structured way to define the configuration for model training, including where to save outputs, how often to evaluate, and various performance and memory optimization settings. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Basic Configuration

To create a training configuration, instantiate `TrainingArguments` with the desired parameters. At minimum, you must specify an output directory for saving model checkpoints and logs. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

```python
from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir=training_output_dir,
    evaluation_strategy="epoch"
)
```

The `evaluation_strategy` parameter controls when evaluation occurs during training. Common values include `"epoch"` (evaluate at the end of each epoch) and `"steps"` (evaluate every `eval_steps` steps). ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Memory Optimization Parameters

When training large models on a single GPU, you may encounter CUDA Out of Memory Errors. The `TrainingArguments` class provides several parameters to help manage GPU memory usage: ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

- **`per_device_train_batch_size`**: Reduce this value to lower memory consumption per GPU. Smaller batch sizes require less GPU memory but may increase training time. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]
- **`fp16`**: Set `fp16=True` to use mixed precision training (16-bit floating point), which reduces memory usage and can speed up training on compatible GPUs. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]
- **`gradient_accumulation_steps`**: Use this parameter to effectively increase the overall batch size without increasing per-device memory. Gradients are accumulated over the specified number of steps before performing an optimizer update. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

## Integration with Trainer

The `TrainingArguments` object is passed directly to the Trainer class constructor. The Trainer uses these arguments to control the training loop, including when to save checkpoints, log metrics, and run evaluations. ^[fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md]

```python
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_test_dataset["train"],
    eval_dataset=train_test_dataset["test"],
    compute_metrics=compute_metrics,
    data_collator=data_collator,
)
```

## Related Concepts

- Trainer — The Hugging Face utility that uses TrainingArguments to orchestrate model training
- [Fine-tuning Hugging Face Models](/concepts/single-gpu-fine-tuning-with-hugging-face-on-databricks.md) — The broader process of adapting pre-trained models to specific tasks
- CUDA Out of Memory Errors — Common memory issues addressed by TrainingArguments parameters
- Data Collator — Used alongside TrainingArguments to batch input data during training
- MLflow Integration — TrainingArguments works with MLflow for experiment tracking and model logging
- Mixed Precision Training — The technique enabled by the `fp16` parameter

## Sources

- fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md

# Citations

1. [fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws.md](/references/fine-tune-hugging-face-models-for-a-single-gpu-databricks-on-aws-e2c49846.md)
