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transformers (Python library)

type: entityconfidence: highupdated: 2026-06-23sources: 3

transformers is the model-definition framework for state-of-the-art ML across text, vision, audio, video, and multimodal — inference and training. It centralizes model definitions so a supported model works across the ecosystem: training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, ...), inference engines (vLLM, SGLang, TGI, ...), and adjacent libraries (llama.cpp, mlx, ...). 1M+ checkpoints on the Hub.

Install

Tested on Python 3.10+ and PyTorch 2.4+. pip install transformers (or uv pip install transformers). Verify:

python -c "from transformers import pipeline; print(pipeline('sentiment-analysis')('hugging face is the best'))"
# [{'label': 'POSITIVE', 'score': 0.9998704791069031}]

Three base classes

Every pretrained model is built from three classes:

PreTrainedConfig — model attributes (attention heads, vocab size, ...).
PreTrainedModel — the architecture (e.g. LlamaModel vs LlamaForCausalLM).
Preprocessor — converts raw inputs to tensors (e.g. PreTrainedTokenizer, ImageProcessingMixin).

The AutoClass API (AutoModelFor*, AutoTokenizer) infers the architecture from the model name and loads it with from_pretrained. See transformers basics.

Two APIs: Pipeline and Trainer

Pipeline — simple, optimized inference for many tasks (text generation, image segmentation, ASR, document QA, ...). Trainer — a complete PyTorch training/eval loop (mixed precision, torch.compile, FlashAttention, distributed). Provide a model, dataset, preprocessor, and data collator; configure with TrainingArguments.

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(
    output_dir="distilbert-rotten-tomatoes",
    learning_rate=2e-5,
    per_device_train_batch_size=8,
    num_train_epochs=2,
    push_to_hub=True,
)
trainer = Trainer(model=model, args=training_args, train_dataset=dataset["train"], ...)
trainer.train()
trainer.push_to_hub()

For LLM/VLM text generation (generate, streaming, decoding strategies) see running llms with transformers.