transformers

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This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.

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npx skill4agent add davila7/claude-code-templates transformers

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transformer-modelshuggingface-transformersnlp-tasksmodel-fine-tuningmultimodal-ai

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Transformers

Overview

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.

Installation

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

Authentication

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

Quick Start

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...")

Core Capabilities

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.

Common Patterns

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()

Reference Documentation

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