# Transformers

> Hugging Face Transformers para cargar modelos del Hub, hacer inferencia con pipelines, generar texto y afinar modelos con Trainer en tareas de NLP, visión, audio y multimodales. Para AutoModel, pipelines o TrainingArguments.

Fuente: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/transformers
Markdown: https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/transformers.md

Repositorio: https://github.com/K-Dense-AI/scientific-agent-skills
Autor: K-Dense-AI
Licencia: Apache-2.0 license
Actualizado: hace 2 meses
Coste de contexto: 74 tok instalada, 1.8k tok al activarse, 13.9k tok con todos los archivos del bundle
Bundle: 6 archivos, 54 KB
Permisos que pide: read write edit bash

## Instalación

Un skill son archivos markdown: los mismos archivos valen para cualquier agente y lo único que cambia es el directorio de destino, es decir la bandera `--agent`. Añade `-g` para instalarlo en todos los proyectos de la máquina.

```bash
# Claude Code
npx -y skills add K-Dense-AI/scientific-agent-skills --skill transformers --agent claude-code
# Cursor
npx -y skills add K-Dense-AI/scientific-agent-skills --skill transformers --agent cursor
# Codex
npx -y skills add K-Dense-AI/scientific-agent-skills --skill transformers --agent codex
# Gemini CLI
npx -y skills add K-Dense-AI/scientific-agent-skills --skill transformers --agent gemini
# Windsurf
npx -y skills add K-Dense-AI/scientific-agent-skills --skill transformers --agent windsurf
# Cline
npx -y skills add K-Dense-AI/scientific-agent-skills --skill transformers --agent cline
```

## Qué hace

- Carga modelos preentrenados del Hub de Hugging Face con AutoModel/AutoTokenizer y control fino de dispositivo/precisión
- Ejecuta inferencia rápida con la API de pipelines (generación de texto, clasificación, QA, NER, visión, audio)
- Genera texto con LLMs controlando la estrategia de decodificación (greedy, beam search, sampling, top-k/top-p)
- Afina modelos en datos propios con la API Trainer (precisión mixta, entrenamiento distribuido, logging)
- Gestiona tokenización con padding, truncado y tokens especiales

## Cuándo usarla

- Cargar y usar modelos del Hugging Face Hub para NLP, visión, audio o tareas multimodales
- Necesitas inferencia rápida con pipelines sin preprocesamiento personalizado
- Generar texto con un LLM controlando parámetros de decodificación
- Afinar (fine-tune) un modelo preentrenado en un dataset propio con Trainer

## Cuándo no

- El trabajo es ML general fuera de la librería Transformers

## Qué la activa

- "Carga este modelo de Hugging Face y clasifica estos textos"
- "Genera texto con este LLM usando max_new_tokens=50"
- "Afina este modelo con Trainer sobre mi dataset"
- "Tokeniza este texto para pasarlo al modelo"

## Antes de instalar

- Requiere Python 3.10+, PyTorch 2.4+ y transformers 5.x; los modelos con acceso restringido o privados en el Hub necesitan un token HF_TOKEN o `hf auth login`.

## Archivos

- SKILL.md — 7 KB
- references/generation.md — 9 KB
- references/models.md — 9 KB
- references/pipelines.md — 9 KB
- references/tokenizers.md — 10 KB
- references/training.md — 10 KB

## SKILL.md

Reproducido tal cual desde K-Dense-AI/scientific-agent-skills bajo Apache-2.0 license. Esta sección es el documento original y está en inglés.

# 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

Tested against **transformers 5.12.0** (current PyPI release; June 2026). Requires **Python 3.10+**; the `torch` extra currently requires **PyTorch 2.4+**.

```bash
uv pip install "transformers[torch]==5.12.0" huggingface_hub==1.19.0 datasets==5.0.0 evaluate==0.4.6 accelerate==1.14.0
```

For vision tasks, add:

```bash
uv pip install timm==1.0.27 pillow==12.2.0
```

For audio tasks, add:

```bash
uv pip install librosa==0.11.0 soundfile==0.14.0
```

These pins are for reproducible examples. For exploratory work, loosen them only after checking the Transformers and Hub release notes for API changes.

Check your version:

```python
import transformers
print(transformers.__version__)
```

## Authentication

Many models on the Hugging Face Hub are gated or private. Authenticate before loading them.

**Recommended:** CLI login (stores token in `~/.cache/huggingface/token`):

```bash
hf auth login
```

**Python:**

```python
from huggingface_hub import login
login()  # Interactive prompt; do not hardcode tokens in scripts
```

**Servers / CI:** set `HF_TOKEN` in the environment (never commit tokens to git or shell profiles):

```bash
export HF_TOKEN="..."  # Read token from a secret manager, not source code
```

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

**Security:** Never paste tokens into notebooks, repos, or shared configs. Prefer `hf auth login` over exporting tokens in `.bashrc` or `.zshrc`.

Use the narrowest token scope that works: `read` for private or gated model downloads, `write` only for uploads. If a long-running environment should not send the stored token on every Hub request, set `HF_HUB_DISABLE_IMPLICIT_TOKEN=1` and pass a token only where authentication is required.

## Transformers v5

Transformers v5 is **PyTorch-only** (TensorFlow and JAX backends were removed). For upgrades from v4, see the [v5 migration guide](https://github.com/huggingface/transformers/blob/main/MIGRATION_GUIDE_V5.md). New projects should pair **transformers 5.x** with **huggingface_hub 1.x**.

**Gated or custom architectures:** accept the model license on the Hub, then load with `trust_remote_code=True` only when the model card requires custom code you have reviewed.

**Cache location:** set `HF_HOME` for all Hugging Face caches, or `HF_HUB_CACHE` just for Hub files. Use `HF_HUB_OFFLINE=1` only after required model snapshots are already cached.

## Quick Start

Use the Pipeline API for fast inference without manual configuration:

```python
from transformers import pipeline

# Text generation (prefer max_new_tokens for causal LMs)
generator = pipeline("text-generation", model="Qwen/Qwen2.5-1.5B")
result = generator("The future of AI is", max_new_tokens=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

## Dónde encaja

- Categoría: [Herramientas para desarrolladores](https://skillsagentes.com/categorias/herramientas-desarrollo.md) — Skills que cambian cómo tu agente escribe, revisa y despliega código.
- Creador: [K-Dense-AI](https://skillsagentes.com/creators/k-dense-ai.md) — 163 skills en el directorio
- [Todas las skills](https://skillsagentes.com/skills.md)
- [Ranking de instalaciones](https://skillsagentes.com/ranking.md)

## Otras skills del mismo repositorio

- [Citation Management](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/citation-management.md): Gestión integral de citas académicas: busca en OpenAlex, PubMed y Google Scholar, extrae metadatos precisos, valida citas y genera entradas BibTeX correctamente formateadas.
- [Scientific Slides](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/scientific-slides.md): Crea decks de diapositivas y presentaciones para charlas de investigación: PowerPoint, presentaciones de conferencia, seminarios, defensas de tesis. Da estructura, plantillas, guía de tiempos y validación visual.
- [Literature Review](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/literature-review.md): Realiza revisiones bibliográficas sistemáticas y completas usando varias bases académicas (PubMed, arXiv, bioRxiv, Semantic Scholar). Genera markdown y PDF con citas verificadas en varios estilos (APA, Nature, Vancouver).
- [Infographics](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/infographics.md): Crea infografías profesionales con Nano Banana Pro AI y refinamiento iterativo inteligente. Usa Gemini 3.6 Flash para revisar la calidad e integra investigación con Perplexity Sonar. Soporta 10 tipos, 8 estilos y paletas para daltonismo.
- [Latex Posters](https://skillsagentes.com/skills/k-dense-ai/scientific-agent-skills/latex-posters.md): Crea pósteres de investigación profesionales en LaTeX con beamerposter, tikzposter o baposter, para conferencias y comunicación científica: layout, colores, columnas múltiples e integración de figuras.

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