Essential Libraries

You do not need every Hugging Face library on day one. You do need to know what each one is for.

transformers

This is the flagship library.

Use it for:

• Loading pretrained models
• Tokenization integration
• Inference pipelines
• Training helpers
• Text, vision, audio, and multimodal models

Example: Load a summarization model, tokenize documents, generate summaries, and save the resulting checkpoint.

datasets

This library loads and processes datasets efficiently.

Use it for:

• Public datasets from the Hub
• Train/validation/test splits
• Column transforms
• Mapping tokenization over large datasets
• Streaming large datasets

from datasets import load_dataset

dataset = load_dataset("imdb")
print(dataset["train"][0])

Why it matters:

• Cleaner preprocessing
• Reproducible splits
• Efficient memory handling

tokenizers

This library provides fast tokenization, often in Rust under the hood.

Tokenization converts raw text into tokens and ids that models can process.

Example: The same sentence may split very differently across tokenizers, which affects length, cost, and behavior.

evaluate

This library helps compute metrics.

import evaluate

accuracy = evaluate.load("accuracy")
print(accuracy.compute(predictions=[1, 0], references=[1, 1]))

Use it to avoid hand-rolled metric code when standard metrics already exist.

accelerate

accelerate helps with device handling and scaling.

Use it when:

• Moving from laptop to GPU machine
• Training across multiple GPUs
• Simplifying distributed training setup

It reduces infrastructure friction.

peft

PEFT stands for Parameter-Efficient Fine-Tuning.

It lets you adapt large models by training a small number of additional parameters instead of updating everything.

Popular methods include:

• LoRA
• Prefix tuning
• Prompt tuning

Why this matters:

• Lower cost
• Less memory use
• Faster iteration

huggingface_hub

This library handles:

• Authentication
• Repo metadata
• File download/upload
• Revision pinning
• Programmatic interaction with the Hub

How They Fit Together

A Standard Workflow

A very common pipeline is:

1. Use datasets to load data
2. Use transformers tokenizer to prepare inputs
3. Fine-tune or infer with transformers
4. Use evaluate for metrics
5. Use accelerate if hardware setup grows
6. Use peft if full fine-tuning is too expensive
7. Push artifacts with huggingface_hub

What Beginners Actually Need First

Start with just these:

• transformers
• datasets
• huggingface_hub

Add the others when your workflow demands them.