Why it matters

Almost every LLM app uses transformers or something built on it (vLLM, TGI). Understanding the library's primitives shapes what you can build efficiently.

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The architecture

Model classes: AutoModel, AutoModelForCausalLM, AutoModelForSequenceClassification. Auto* infers architecture from checkpoint.

Tokenizer: AutoTokenizer. Loads matching tokenizer for a model.

Config: AutoConfig. Model hyperparameters.

Transformers library primitivesAutoModelload any architectureAutoTokenizermatching tokenizerTrainer / Pipelinehigh-level APIsUnified API abstracts architectural differences; same code works for BERT and LLaMA
Library structure.
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How it works end to end

Loading: from_pretrained('model_name') downloads from Hub, caches locally, instantiates.

Backends: PyTorch primary; TensorFlow and Flax also supported.

Task heads: task-specific model wrappers (classification, QA, generation) add heads to base models.

Utility APIs: pipelines (task-focused), Trainer (fine-tuning), generation utilities.