arcee-ai/transformers

arcee-ai/transformers

Description: 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

License: Apache-2.0

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Forks: 1

Open issues: 0

Created: 2025-06-04T21:20:39Z

Pushed: 2025-06-24T03:41:52Z

Default branch: main

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Parent repository: huggingface/transformers

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README:

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State-of-the-art pretrained models for inference and training

Transformers is a library of pretrained text, computer vision, audio, video, and multimodal models for inference and training. Use Transformers to fine-tune models on your data, build inference applications, and for generative AI use cases across multiple modalities.

There are over 500K+ 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.

Installation

Transformers works with Python 3.9+ PyTorch 2.1+, TensorFlow 2.6+, and Flax 0.4.1+.

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]

Quickstart

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. > ``shell > 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", torch_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.

Automatic speech recognition

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.'}

Image classification

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}]

Visual question answering

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'}]

Why should I use Transformers?

1. Easy-to-use state-of-the-art models:

• High performance on natural language understanding & generation, computer vision, audio, video, and multimodal tasks.
• Low barrier to entry for researchers, engineers, and developers.
• Few user-facing abstractions with just three classes to learn.
• A unified API for using all our pretrained models.

1. Lower compute costs, smaller carbon footprint:

• Share trained models instead of training from scratch.
• Reduce compute time and production costs.
• Dozens of model architectures with 1M+ pretrained checkpoints across all modalities.

1. Choose the right framework for every part of a models lifetime:

• Train state-of-the-art models in 3 lines of code.
• Move a single model between PyTorch/JAX/TF2.0 frameworks at will.
• Pick the right framework for training, evaluation, and production.

1. Easily customize a model or an example to your needs:

• We provide examples for each…