huggingface-transformers

Load pretrained models and tokenizers using AutoModel and AutoTokenizer classes to run inference in Python. This provides quick patterns for executing tasks like sentiment classification directly through ready-made pipelines or manual model loading.

Hugging Face Transformers supports quantization to optimize models for accelerated inference and production-ready pipelines. This technique reduces model memory footprint and improves execution speed during deployment.

Export Hugging Face Transformer models to ONNX format using the Optimum library to create production-ready pipelines. This process generates an optimized model representation suitable for accelerated inference across different deployment environments.

Fine-tune a Hugging Face Transformer with LoRA in Python by applying parameter-efficient methods using the PEFT library alongside the Trainer API. This approach adapts pretrained models efficiently by updating a small set of adapter parameters rather than the entire model.

You can export Hugging Face Transformers to ONNX format using the Optimum library for accelerated inference and production-ready pipelines. This deployment optimization converts Python models into a standardized format for better performance in production environments.

You need a Python environment with transformers, datasets, and optional PEFT and Optimum libraries installed to execute fine-tuning, quantization, and ONNX export workflows. These dependencies enable the full range of model deployment features.