transformers

The Hugging Face Transformers library provides access to thousands of pre-trained models for tasks across NLP, computer vision, audio, and multimodal domains. Use this skill to load models, perform inference, and fine-tune on custom data.

Install transformers and core dependencies:

```bash

uv pip install torch transformers datasets evaluate accelerate

```

For vision tasks, add:

```bash

uv pip install timm pillow

```

For audio tasks, add:

```bash

uv pip install librosa soundfile

```

Many models on the Hugging Face Hub require authentication. Set up access:

```python

from huggingface_hub import login

login() # Follow prompts to enter token

```

Or set environment variable:

```bash

export HUGGINGFACE_TOKEN="your_token_here"

```

Get tokens at: https://huggingface.co/settings/tokens

Use the Pipeline API for fast inference without manual configuration:

```python

from transformers import pipeline

# Text generation

generator = pipeline("text-generation", model="gpt2")

result = generator("The future of AI is", max_length=50)

# Text classification

classifier = pipeline("text-classification")

result = classifier("This movie was excellent!")

# Question answering

qa = pipeline("question-answering")

result = qa(question="What is AI?", context="AI is artificial intelligence...")

```

1. Pipelines for Quick Inference

Use for simple, optimized inference across many tasks. Supports text generation, classification, NER, question answering, summarization, translation, image classification, object detection, audio classification, and more.

When to use: Quick prototyping, simple inference tasks, no custom preprocessing needed.

See references/pipelines.md for comprehensive task coverage and optimization.

2. Model Loading and Management

Load pre-trained models with fine-grained control over configuration, device placement, and precision.

When to use: Custom model initialization, advanced device management, model inspection.

See references/models.md for loading patterns and best practices.

3. Text Generation

Generate text with LLMs using various decoding strategies (greedy, beam search, sampling) and control parameters (temperature, top-k, top-p).

When to use: Creative text generation, code generation, conversational AI, text completion.

See references/generation.md for generation strategies and parameters.

4. Training and Fine-Tuning

Fine-tune pre-trained models on custom datasets using the Trainer API with automatic mixed precision, distributed training, and logging.

When to use: Task-specific model adaptation, domain adaptation, improving model performance.

See references/training.md for training workflows and best practices.

5. Tokenization

Convert text to tokens and token IDs for model input, with padding, truncation, and special token handling.

When to use: Custom preprocessing pipelines, understanding model inputs, batch processing.

See references/tokenizers.md for tokenization details.

Pattern 1: Simple Inference

For straightforward tasks, use pipelines:

```python

pipe = pipeline("task-name", model="model-id")

output = pipe(input_data)

```

Pattern 2: Custom Model Usage

For advanced control, load model and tokenizer separately:

```python

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("model-id")

model = AutoModelForCausalLM.from_pretrained("model-id", device_map="auto")

inputs = tokenizer("text", return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=100)

result = tokenizer.decode(outputs[0])

```

Pattern 3: Fine-Tuning

For task adaptation, use Trainer:

```python

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(

output_dir="./results",

num_train_epochs=3,

per_device_train_batch_size=8,

)

trainer = Trainer(

model=model,

args=training_args,

train_dataset=train_dataset,

)

trainer.train()

```

For detailed information on specific components:

• Pipelines: references/pipelines.md - All supported tasks and optimization
• Models: references/models.md - Loading, saving, and configuration
• Generation: references/generation.md - Text generation strategies and parameters
• Training: references/training.md - Fine-tuning with Trainer API
• Tokenizers: references/tokenizers.md - Tokenization and preprocessing