huggingface-transformers

Hugging Face Transformers Best Practices

Comprehensive guide to using the Hugging Face Transformers library including model loading, tokenization, fine-tuning workflows, pipeline usage, custom datasets, and deployment optimization.

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Quick Reference

When to use this skill:

• Loading and using pre-trained transformers (BERT, GPT, T5, LLaMA, etc.)
• Fine-tuning models on custom data
• Implementing NLP tasks (classification, QA, generation, etc.)
• Optimizing inference (quantization, ONNX, etc.)
• Debugging tokenization issues
• Using Hugging Face pipelines
• Deploying transformers to production

Models covered:

• Encoders: BERT, RoBERTa, DeBERTa, ALBERT
• Decoders: GPT-2, GPT-Neo, LLaMA, Mistral
• Encoder-Decoders: T5, BART, Flan-T5
• Vision: ViT, CLIP, Stable Diffusion

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Part 1: Model Loading Patterns

Pattern 1: Basic Model Loading

from transformers import AutoModel, AutoTokenizer

# Load model and tokenizer
model_name = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# For specific tasks
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    num_labels=3  # For 3-class classification
)

Pattern 2: Loading with Specific Configuration

from transformers import AutoConfig, AutoModel

# Modify configuration
config = AutoConfig.from_pretrained("bert-base-uncased")
config.hidden_dropout_prob = 0.2  # Custom dropout
config.attention_probs_dropout_prob = 0.2

# Load model with custom config
model = AutoModel.from_pretrained("bert-base-uncased", config=config)

# Or create model from scratch with config
model = AutoModel.from_config(config)

Pattern 3: Loading Quantized Models (Memory Efficient)

from transformers import AutoModel, BitsAndBytesConfig
import torch

# 8-bit quantization (50% memory reduction)
quantization_config = BitsAndBytesConfig(load_in_8bit=True)

model = AutoModel.from_pretrained(
    "meta-llama/Llama-2-7b-hf",
    quantization_config=quantization_config,
    device_map="auto"  # Automatic device placement
)

# 4-bit quantization (75% memory reduction)
quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True
)

model = AutoModel.from_pretrained(
    "meta-llama/Llama-2-13b-hf",
    quantization_config=quantization_config,
    device_map="auto"
)