What is the Auto-class pattern in Hugging Face and how do you run inference with a raw model?

The Auto* classes (AutoTokenizer, AutoModel, AutoModelForSequenceClassification, etc.) are factory classes that read a model's config.json from the Hub and automatically instantiate the correct tokenizer or model architecture without you needing to know which specific class to use. This makes code model-agnostic — you can swap a BERT model for a RoBERTa or DistilBERT model by changing only the model name string.

For custom inference beyond what pipeline() provides, you load the tokenizer and model separately, tokenize the input, run the forward pass, and post-process the logits. Understanding this lower-level workflow is essential for fine-tuning, batched inference at scale, and extracting intermediate representations (embeddings).

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_name = 'distilbert-base-uncased-finetuned-sst-2-english'
tokenizer  = AutoTokenizer.from_pretrained(model_name)
model      = AutoModelForSequenceClassification.from_pretrained(model_name)
model.eval()  # disable dropout

texts = ['I love this movie!', 'This was a terrible waste of time.']
inputs = tokenizer(texts, return_tensors='pt', padding=True, truncation=True)

with torch.no_grad():
    outputs = model(**inputs)

logits = outputs.logits                          # (batch, num_labels)
probs  = torch.softmax(logits, dim=-1)           # convert to probabilities
preds  = torch.argmax(probs, dim=-1)             # class index
labels = [model.config.id2label[p.item()] for p in preds]
print(labels)   # ['POSITIVE', 'NEGATIVE']

# ── Extracting text embeddings (for semantic search / RAG)
from transformers import AutoModel

embed_model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
inputs2 = tokenizer(['Hello world', 'Hi earth'], return_tensors='pt',
                     padding=True, truncation=True)
with torch.no_grad():
    hidden = embed_model(**inputs2).last_hidden_state  # (2, seq_len, 384)
    # Mean-pool over token dimension
    mask   = inputs2['attention_mask'].unsqueeze(-1).float()
    embeds = (hidden * mask).sum(1) / mask.sum(1)      # (2, 384)
print('Embedding shape:', embeds.shape)