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Transformers Comandos

HuggingFace Transformers fornece milhares de modelos pré-treinados para NLP, visão computacional, áudio e tarefas multimodais. Oferece uma API unificada para carregamento de modelos, tokenização, treinamento e inferência em PyTorch, TensorFlow e JAX.

Instalação

# Install with PyTorch backend
pip install transformers[torch]

# Install with all optional dependencies
pip install transformers[torch,sentencepiece,tokenizers,vision,audio]

# Install from source
pip install git+https://github.com/huggingface/transformers.git

# Install specific version
pip install transformers==4.46.0

# Verify
python -c "import transformers; print(transformers.__version__)"

Pipeline API (Quick Inference)

from transformers import pipeline

# Text generation
generator = pipeline("text-generation", model="meta-llama/Llama-3.1-8B-Instruct")
result = generator("Explain quantum computing:", max_new_tokens=200)

# Sentiment analysis
classifier = pipeline("sentiment-analysis")
result = classifier("I love this product!")
# [{'label': 'POSITIVE', 'score': 0.9998}]

# Summarization
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
summary = summarizer(long_text, max_length=130, min_length=30)

# Question answering
qa = pipeline("question-answering")
result = qa(question="What is BERT?", context="BERT is a transformer model...")

# Zero-shot classification
classifier = pipeline("zero-shot-classification")
result = classifier("I need to pay my bill", candidate_labels=["billing", "support", "sales"])

# Image classification
classifier = pipeline("image-classification", model="google/vit-base-patch16-224")
result = classifier("photo.jpg")

# Automatic speech recognition
asr = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")
result = asr("audio.mp3")

AutoModel and AutoTokenizer

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model_name = "meta-llama/Llama-3.1-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

# Tokenize input
inputs = tokenizer("Hello, how are you?", return_tensors="pt").to(model.device)

# Generate
outputs = model.generate(
    **inputs,
    max_new_tokens=100,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
)

# Decode
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Chat Template

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain transformers in ML."},
]

# Apply chat template
input_ids = tokenizer.apply_chat_template(
    messages, return_tensors="pt", add_generation_prompt=True
).to(model.device)

outputs = model.generate(input_ids, max_new_tokens=256)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)

Quantization with BitsAndBytes

from transformers import AutoModelForCausalLM, BitsAndBytesConfig
import torch

# 4-bit quantization (QLoRA-style)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-70B-Instruct",
    quantization_config=bnb_config,
    device_map="auto",
)

# 8-bit quantization
model_8bit = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-70B-Instruct",
    load_in_8bit=True,
    device_map="auto",
)

Trainer API

from transformers import (
    AutoModelForCausalLM, AutoTokenizer,
    Trainer, TrainingArguments, DataCollatorForLanguageModeling,
)
from datasets import load_dataset

model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
tokenizer.pad_token = tokenizer.eos_token

dataset = load_dataset("tatsu-lab/alpaca", split="train")

def tokenize(example):
    return tokenizer(example["text"], truncation=True, max_length=2048)

tokenized = dataset.map(tokenize, batched=True, remove_columns=dataset.column_names)

training_args = TrainingArguments(
    output_dir="./output",
    per_device_train_batch_size=2,
    gradient_accumulation_steps=8,
    num_train_epochs=3,
    learning_rate=2e-5,
    bf16=True,
    logging_steps=10,
    save_steps=500,
    save_total_limit=3,
    warmup_steps=100,
    weight_decay=0.01,
    lr_scheduler_type="cosine",
    report_to="wandb",
    gradient_checkpointing=True,
    push_to_hub=False,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized,
    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
)

trainer.train()
trainer.save_model("./final-model")

Push to Hub

from huggingface_hub import login

# Authenticate
login(token="hf_YOUR_TOKEN")

# Push model and tokenizer
model.push_to_hub("my-org/my-model")
tokenizer.push_to_hub("my-org/my-model")

# Push with Trainer
training_args = TrainingArguments(
    output_dir="./output",
    push_to_hub=True,
    hub_model_id="my-org/my-model",
)

Model Parallelism

# Automatic device placement across GPUs
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-70B-Instruct",
    device_map="auto",        # Automatic placement
    torch_dtype=torch.float16,
)

# Custom device map
device_map = {
    "model.embed_tokens": 0,
    "model.layers.0": 0,
    "model.layers.1": 0,
    "model.layers.2": 1,
    "model.layers.3": 1,
    "model.norm": 1,
    "lm_head": 1,
}

model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B",
    device_map=device_map,
)

Generation Config

from transformers import GenerationConfig

gen_config = GenerationConfig(
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
    top_k=50,
    repetition_penalty=1.1,
    do_sample=True,
    num_beams=1,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
)

outputs = model.generate(**inputs, generation_config=gen_config)

Common Auto Classes

ClassUse Case
AutoModelBase model (no head)
AutoModelForCausalLMText generation (GPT, Llama)
AutoModelForSeq2SeqLMSeq2seq (T5, BART)
AutoModelForSequenceClassificationText classification
AutoModelForTokenClassificationNER, POS tagging
AutoModelForQuestionAnsweringExtractive QA
AutoModelForImageClassificationImage classification
AutoModelForSpeechSeq2SeqSpeech-to-text
AutoTokenizerAuto-detect tokenizer
AutoProcessorMulti-modal processing

CLI Comandos

# Download model
huggingface-cli download meta-llama/Llama-3.1-8B-Instruct

# Upload model
huggingface-cli upload my-org/my-model ./model-dir

# Login
huggingface-cli login

# Check cache
huggingface-cli scan-cache

# Delete cached models
huggingface-cli delete-cache