Getting Started with Hugging Face Transformers

The Transformers library is the easiest way to use pre-trained AI models.

Installation

```bash

pip install transformers torch

```

Text Generation (LLM)

```python

from transformers import pipeline

generator = pipeline("text-generation", model="meta-llama/Llama-3.2-3B-Instruct")

result = generator("Explain quantum computing in simple terms:", max_new_tokens=200)

print(result[0]["generated_text"])

```

Sentiment Analysis

```python

classifier = pipeline("sentiment-analysis")

result = classifier("I love using AI for coding!")

print(result) # [{'label': 'POSITIVE', 'score': 0.9998}]

```

Image Classification

```python

classifier = pipeline("image-classification", model="google/vit-base-patch16-224")

result = classifier("https://example.com/cat.jpg")

print(result) # [{'label': 'tabby cat', 'score': 0.95}]

```

Zero-Shot Classification

```python

classifier = pipeline("zero-shot-classification")

result = classifier(

"This is a tutorial about machine learning",

candidate_labels=["education", "politics", "technology"]

)

print(result["labels"][0]) # 'technology'

```

Key Concepts

| Concept | Description |

|---------|-------------|

| **Pipeline** | High-level API — one line to load model + tokenizer |

| **AutoModel** | Lower-level — more control over model architecture |

| **AutoTokenizer** | Handles text → tokens → text conversion |

| **Trainer** | Fine-tuning API with logging, checkpointing, evaluation |

| **Hub** | Central repository for sharing models and datasets |