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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Transformers acts as the model-definition framework for state-of-the-art machine learning with text, computer vision, audio, video, and multimodal models, for both inference and training.
It centralizes the model definition so that this definition is agreed upon across the ecosystem. transformers is the
pivot across frameworks: if a model definition is supported, it will be compatible with the majority of training
frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning, ...), inference engines (vLLM, SGLang, TGI, ...),
and adjacent modeling libraries (llama.cpp, mlx, ...) which leverage the model definition from transformers.
We pledge to help support new state-of-the-art models and democratize their usage by having their model definition be simple, customizable, and efficient.
There are over 1M+ Transformers model checkpoints on the Hugging Face Hub you can use.
Explore the Hub today to find a model and use Transformers to help you get started right away.
Transformers works with Python 3.10+, and PyTorch 2.4+.
Create and activate a virtual environment with venv or uv, a fast Rust-based Python package and project manager.
# venv
python -m venv .my-env
source .my-env/bin/activate
# uv
uv venv .my-env
source .my-env/bin/activate
Install Transformers in your virtual environment.
# pip
pip install "transformers[torch]"
# uv
uv pip install "transformers[torch]"
Install Transformers from source if you want the latest changes in the library or are interested in contributing. However, the latest version may not be stable. Feel free to open an issue if you encounter an error.
git clone https://github.com/huggingface/transformers.git
cd transformers
# pip
pip install '.[torch]'
# uv
uv pip install '.[torch]'
Get started with Transformers right away with the Pipeline API. The Pipeline is a high-level inference class that supports text, audio, vision, and multimodal tasks. It handles preprocessing the input and returns the appropriate output.
Instantiate a pipeline and specify model to use for text generation. The model is downloaded and cached so you can easily reuse it again. Finally, pass some text to prompt the model.
from transformers import pipeline
pipeline = pipeline(task="text-generation", model="Qwen/Qwen2.5-1.5B")
pipeline("the secret to baking a really good cake is ")
[{'generated_text': 'the secret to baking a really good cake is 1) to use the right ingredients and 2) to follow the recipe exactly. the recipe for the cake is as follows: 1 cup of sugar, 1 cup of flour, 1 cup of milk, 1 cup of butter, 1 cup of eggs, 1 cup of chocolate chips. if you want to make 2 cakes, how much sugar do you need? To make 2 cakes, you will need 2 cups of sugar.'}]
To chat with a model, the usage pattern is the same. The only difference is you need to construct a chat history (the input to Pipeline) between you and the system.
[!TIP] You can also chat with a model directly from the command line, as long as
transformers serveis running.transformers chat Qwen/Qwen2.5-0.5B-Instruct
import torch
from transformers import pipeline
chat = [
{"role": "system", "content": "You are a sassy, wise-cracking robot as imagined by Hollywood circa 1986."},
{"role": "user", "content": "Hey, can you tell me any fun things to do in New York?"}
]
pipeline = pipeline(task="text-generation", model="meta-llama/Meta-Llama-3-8B-Instruct", dtype=torch.bfloat16, device_map="auto")
response = pipeline(chat, max_new_tokens=512)
print(response[0]["generated_text"][-1]["content"])
Expand the examples below to see how Pipeline works for different modalities and tasks.
from transformers import pipeline
pipeline = pipeline(task="automatic-speech-recognition", model="openai/whisper-large-v3")
pipeline("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
from transformers import pipeline
pipeline = pipeline(task="image-classification", model="facebook/dinov2-small-imagenet1k-1-layer")
pipeline("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
[{'label': 'macaw', 'score': 0.997848391532898},
{'label': 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
'score': 0.0016551691805943847},
{'label': 'lorikeet', 'score': 0.00018523589824326336},
{'label': 'African grey, African gray, Psittacus erithacus',
'score': 7.85409429227002e-05},
{'label': 'quail', 'score': 5.502637941390276e-05}]
from transformers import pipeline
pipeline = pipeline(task="visual-question-answering", model="Salesforce/blip-vqa-base")
pipeline(
image="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/idefics-few-shot.jpg",
question="What is in the image?",
)
[{'answer': 'statue of liberty'}]
Easy-to-use state-of-the-art models:
Lower compute costs, smaller carbon footprint:
Choose the right framework for every part of a model's lifetime:
Easily customize a model or an example to your needs:
Selected from shared topics, language and repository description—not editorial ratings.
NathanKolbas /
State-of-the-art Machine Learning for Dart. Run 🤗 Transformers cross-platform on your device, with no need for a server!