transformers

# 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(...)`.