transformers AI技能包

Installation

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.

```py

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, as long as transformers serve is 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.

<details> <summary>Automatic speech recognition</summary>

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.'}

</details>

<details> <summary>Image classification</summary>

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}]

</details>

<details> <summary>Visual question answering</summary>

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'}]

</details>

Example models

You can test most of our models directly on their Hub model pages.

Expand each modality below to see a few example models for various use cases.

<details> <summary>Audio</summary>

• Audio classification with CLAP
• Automatic speech recognition with Parakeet, Whisper, GLM-ASR and Moonshine-Streaming
• Keyword spotting with Wav2Vec2
• Speech to speech generation with Moshi
• Text to audio with MusicGen
• Text to speech with CSM

</details>

<details> <summary>Computer vision</summary>

• Automatic mask generation with SAM
• Depth estimation with DepthPro
• Image classification with DINO v2
• Keypoint detection with SuperPoint
• Keypoint matching with SuperGlue
• Object detection with RT-DETRv2
• Pose Estimation with VitPose
• Universal segmentation with OneFormer
• Video classification with VideoMAE

</details>

<details> <summary>Multimodal</summary>

• Audio or text to text with Voxtral, Audio Flamingo
• Document question answering with LayoutLMv3
• Image or text to text with Qwen-VL
• Image captioning BLIP-2
• OCR-based document understanding with GOT-OCR2
• Table question answering with TAPAS
• Unified multimodal understanding and generation with Emu3
• Vision to text with Llava-OneVision
• Visual question answering with Llava
• Visual referring expression segmentation with Kosmos-2

</details>

<details> <summary>NLP</summary>

• Masked word completion with ModernBERT
• Named entity recognition with Gemma
• Question answering with Mixtral
• Summarization with BART
• Translation with T5
• Text generation with Llama
• Text classification with Qwen

</details>

venv

python -m venv .my-env source .my-env/bin/activate