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name: transformers description: 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. category: llm-tools license: Apache-2.0 license compatibility: Some features require an Huggingface token metadata: upstream: K-Dense-AI/scientific-agent-skills upstream-url: https://github.com/K-Dense-AI/scientific-agent-skills upstream-path: skills/transformers upstream-license: MIT upstream-relationship: derived adapted-by: Synthetic Sciences skill-author: K-Dense Inc. version: 1.0.0 author: K-Dense Inc. tags: [NLP, Deep Learning, Hugging Face, LLM, Fine-Tuning] dependencies: ["transformers>=4.45.0", "torch>=2.0.0", "tokenizers>=0.19.0"]

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:

uv pip install torch transformers datasets evaluate accelerate

For vision tasks, add:

uv pip install timm pillow

For audio tasks, add:

uv pip install librosa soundfile

Credential Setup

HuggingFace token is auto-injected by openscience when connected via the dashboard.

# Verify credentials
[ -n "$HF_TOKEN" ] && echo "HF_TOKEN set" || echo "NOT SET"

If not set: add your Hugging Face token in Customize → Tools or export HF_TOKEN locally.

Quick Start

Use the Pipeline API for fast inference without manual configuration:

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:

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

Pattern 2: Custom Model Usage

For advanced control, load model and tokenizer separately:

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:

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