transformers-5-5-4

name	transformers-5-5-4
description	Complete toolkit for Hugging Face Transformers 5.5.4 providing pretrained models for NLP, vision, audio, video, and multimodal tasks with Pipeline API, Trainer, generation, and quantization. Use when building Python applications that integrate transformer models for text generation, image classification, speech recognition, fine-tuning, or any task requiring the Hugging Face ecosystem.
license	MIT
author	Tangled <[e-mail]>
version	0.1.0
tags	["nlp","machine-learning","deep-learning","pytorch","huggingface","pretrained-models","inference","training","computer-vision","audio","multimodal"]
category	machine-learning
external_references	["https://github.com/huggingface/transformers/tree/v5.5.4/docs","https://huggingface.co/docs/transformers/en/index"]

Transformers 5.5.4

Overview

Transformers acts as the model-definition framework for state-of-the-art machine learning models across text, computer vision, audio, video, and multimodal domains — for both inference and training. It centralizes model definitions so they are compatible across the ecosystem: training frameworks (Axolotl, Unsloth, DeepSpeed, FSDP, PyTorch-Lightning), inference engines (vLLM, SGLang, TGI), and adjacent libraries (llama.cpp, mlx).

Over 1M+ model checkpoints are available on the Hugging Face Hub. The library supports 450+ model architectures including BERT, GPT-2, Llama, Mistral, Gemma, Whisper, CLIP, DINOv2, and many more.

Transformers works with Python 3.10+ and PyTorch 2.4+.

When to Use

• Loading pretrained models for text generation, classification, question answering, or any NLP task
• Running inference on computer vision models (image classification, object detection, segmentation)
• Processing audio with automatic speech recognition or audio classification
• Building multimodal applications combining text, images, and audio
• Fine-tuning large language models with the Trainer API
• Optimizing inference with quantization, caching, compilation, or parallelism
• Chatting with LLMs using chat templates
• Deploying models to production with serialization and export

Core Concepts

Transformers is built around three core abstractions:

Configuration (PreTrainedConfig) — Specifies model attributes like number of hidden layers, vocabulary size, activation function, and attention heads. Each architecture has its own config class.

Model (PreTrainedModel) — The neural network defined by the configuration. Models come as barebones (returning hidden states) or with task-specific heads attached (e.g., LlamaForCausalLM for text generation vs LlamaModel for raw outputs). Use from_pretrained() to load weights from the Hub or a local directory.

Preprocessor — Converts raw inputs into tensors the model can process. Tokenizers handle text, image processors handle images, feature extractors handle audio, and processors combine multiple modalities.

Every pretrained model inherits from these three base classes, enabling a unified API across all architectures.

Installation / Setup

Install Transformers with pip or uv:

pip install transformers
# or
uv pip install transformers