Transformers CLI

For one-off inference, training, quantization, or export, invoke the transformers command directly rather than writing Python. Run transformers --help for the full command list; run transformers <command> --help for flags per command.

Invocation rules

All inputs are named flags, never positional. Wrong invocations like transformers classify "my text" or transformers ner "sentence" will fail with Got unexpected extra argument. The text / image / audio / file argument is always a flag: --text, --image, --audio, --file.

Always invoke as transformers <cmd> .... Do not use python -m transformers ... patterns — the console script is what the transformers package installs.

Use transformers --format json for machine-readable output: transformers --format json classify --text "...".

Example invocations (copy these shapes)

Text (classify, ner, token-classify, summarize, translate, fill-mask):

transformers classify --text "I loved this movie"
transformers classify --text "..." --model distilbert/distilbert-base-uncased-finetuned-sst-2-english
transformers ner --text "Apple CEO Tim Cook visited Paris." --model dslim/bert-base-NER
transformers summarize --file article.txt --model facebook/bart-large-cnn
transformers translate --text "The weather is nice" --model Helsinki-NLP/opus-mt-en-de

Question answering (takes --question and --context):

transformers qa --question "Who invented it?" --context "Graham Bell invented the telephone in 1876."

Image (caption, image-classify, detect, segment, depth, vqa, ocr):

transformers caption --image photo.jpg --model llava-hf/llava-interleave-qwen-0.5b-hf
transformers image-classify --image photo.jpg
transformers vqa --image photo.jpg --question "What color is the car?"

Audio (transcribe, audio-classify, speak):

transformers transcribe --audio clip.wav --model openai/whisper-tiny
transformers audio-classify --audio clip.wav
transformers speak --text "Hello" --output hello.wav

Tokenize / inspect / embed:

transformers tokenize --model HuggingFaceTB/SmolLM2-360M-Instruct --text "tokenize me"
transformers inspect meta-llama/Llama-3.2-1B-Instruct
transformers embed --text "some sentence" --model BAAI/bge-small-en-v1.5

Generate (text completion):

transformers generate --prompt "Once upon a time" --model HuggingFaceTB/SmolLM2-360M-Instruct

Available commands

• transformers classify — Classify text into categories
• transformers ner — Extract named entities from text (NER)
• transformers token-classify — Tag tokens with labels (POS tagging, chunking, etc.)
• transformers qa — Answer a question given a context paragraph (extractive QA)
• transformers table-qa — Answer a question about tabular data (CSV)
• transformers summarize — Summarize text
• transformers translate — Translate text between languages
• transformers fill-mask — Predict the masked token in a sentence
• transformers image-classify — Classify an image
• transformers detect — Detect objects in an image
• transformers segment — Segment an image
• transformers depth — Estimate a depth map from an image
• transformers keypoints — Match keypoints between two images
• transformers video-classify — Classify a video
• transformers transcribe — Transcribe speech from an audio file
• transformers audio-classify — Classify an audio file into categories
• transformers speak — Synthesize speech from text and save to a WAV file
• transformers audio-generate — Generate audio (e.g. music) from a text description and save to a WAV file
• transformers vqa — Visual question answering using AutoModelForImageTextToText
• transformers document-qa — Extractive document question answering using
• transformers caption — Generate a caption for an image using AutoModelForImageTextToText
• transformers ocr — Extract text from an image using AutoModelForImageTextToText
• transformers multimodal-chat — Single-turn conversation with a model that accepts mixed inputs
• transformers generate — Generate text from a prompt with full control over decoding
• transformers detect-watermark — Detect whether text contains a watermark
• transformers embed — Compute embeddings for text or images
• transformers tokenize — Tokenize text and display the resulting tokens
• transformers inspect — Inspect a model's configuration without downloading weights
• transformers inspect-forward — Examine attention weights and hidden states from a forward pass
• transformers benchmark-quantization — Compare quality and performance across quantization methods
• transformers train — Fine-tune or pretrain a model on a dataset
• transformers quantize — Quantize a model and save it
• transformers export — Export a model to a deployment-friendly format

When to use what

• Atomic task (single inference / training / export): use the CLI.
• Composed workflow (chain models, custom logic): write Python. The CLI commands' source in transformers.cli.agentic.* is the canonical template — each file loads a model with AutoModel* + AutoProcessor/AutoTokenizer, runs a forward pass, and post-processes. Copy that pattern rather than reaching for pipeline(...).