Hugging Face Transformers - AI SDKs and Libraries Tool

Overview

Hugging Face Transformers is an open-source library that provides a unified API for thousands of pretrained models across text, vision, audio, video and multimodal tasks. It exposes high-level pipelines for common inference tasks (generation, classification, question answering, summarization, feature extraction) and lower-level primitives (AutoModel, AutoTokenizer) to load and customize models from the Hugging Face Model Hub. According to the GitHub repository, Transformers aims to make state-of-the-art transformer architectures accessible for both research and production use. The library supports multiple deep learning frameworks (PyTorch, TensorFlow and Flax/JAX), offers Trainer and utilities for fine-tuning, and integrates with related tooling: the Tokenizers library for fast tokenization, the Hugging Face Hub for model sharing, and Accelerate/bitsandbytes for optimized inference and mixed-precision or quantized execution. Transformers also provides export and deployment options (ONNX, TorchScript) and first-class support for parameter-efficient fine-tuning (PEFT/LoRA) workflows. Together these features let teams prototype quickly with pipelines and scale to production-grade inference and model sharing (see the GitHub repository and the Hugging Face Model Hub for detailed docs and examples).

GitHub Statistics

• Stars: 154,795
• Forks: 31,667
• Contributors: 439
• License: Apache-2.0
• Primary Language: Python
• Last Updated: 2026-01-09T16:17:04Z
• Latest Release: v4.57.3

Key Features

• High-level pipelines for fast inference across common tasks
• AutoModel/AutoTokenizer abstractions to load models from the Hub
• Multi-backend support: PyTorch, TensorFlow and Flax/JAX
• Trainer + utilities for supervised fine-tuning and evaluation
• Integration with Hugging Face Hub for push/pull and model cards
• Export and deployment: ONNX, TorchScript and serving-ready artifacts
• Optimized inference via Accelerate, bitsandbytes, and quantization
• PEFT / LoRA support for parameter-efficient fine-tuning workflows
• Multimodal model compatibility (text, vision, audio, video)

Example Usage

Example (python):

from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

# Quick inference with a pipeline
generator = pipeline("text-generation", model="gpt2")
print(generator("In the future, AI will", max_new_tokens=40, do_sample=True, temperature=0.8))

# Manual load for more control
tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")
inputs = tokenizer("Transformers make it easy to", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Benchmarks

Supported backends: PyTorch, TensorFlow, Flax (JAX) (Source: https://github.com/huggingface/transformers)

Model Hub integration: Direct push/pull and automatic download from Hugging Face Model Hub (Source: https://huggingface.co/models)

Deployment & optimization options: ONNX and TorchScript export; quantization and 8-bit/4-bit inference via integrations (Source: https://github.com/huggingface/transformers)

Key Information

• Category: SDKs and Libraries
• Type: AI SDKs and Libraries Tool