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Convert your models to ONNX with Optimum: The complete guide
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Convert your models to ONNX with Optimum: The complete guide

Learn how to convert your Transformers models to ONNX format with the 🤗 Optimum library. Optimize your models for production, reduce latency, and simplify your deployments.

blogger · bloggerJune 10, 20263 min read🇫🇷 Français

Optimizing LLMs for Production with ONNX and 🤗 Optimum

Optimizing Large Language Models (LLMs) for production is a major challenge: finding the optimal balance between latency, accuracy, and computational cost. Using ONNX (Open Neural Network Exchange) has become a standard strategy for exporting models from the PyTorch or TensorFlow ecosystem to optimized runtime environments.

Thanks to the 🤗 Optimum library from Hugging Face, this process has become more accessible than ever.

Why Convert Your Models to ONNX?

The ONNX format enables interoperability between different deep learning frameworks and specialized hardware execution engines (such as ONNX Runtime). The benefits are numerous:

  • Performance Acceleration: ONNX Runtime uses hardware-specific optimizations (graph fusion, quantization, layer-wise execution) to drastically reduce latency.
  • Hardware Independence: A model exported in ONNX can be deployed on various types of instances (CPU, Nvidia GPU, or specialized processors) with optimized performance by default.
  • Interoperability: It facilitates deployment on lightweight servers or in containerized environments where installing heavy frameworks (like full PyTorch) is restrictive.

The Role of 🤗 Optimum in the Conversion

🤗 Optimum is an extension of the transformers library that acts as a bridge between Hugging Face models and hardware acceleration tools. It automates the complexity involved in the ONNX export process, which previously required manual manipulation of the computation graph.

Key Steps for an Efficient Conversion

The library offers a unified API for exporting and validating models. Here is how to transform a model into ONNX:

  1. Loading: Use the ORTModelFor... class, which automatically instantiates the model and its runtime.
  2. Exporting: The optimum-cli export onnx command allows you to convert models directly from the Hub without writing complex scripts.
  3. Validation: Optimum compares the output of the original model with its ONNX version to ensure that accuracy (the output score) is preserved after conversion.

Comparison: Performance and Workflow

Feature Standard Model (Torch) ONNX Model (via Optimum)
Package size High (Torch dependencies) Optimized
CPU latency Standard Very low
Flexibility High (research) Optimized (production)
Deployment complexity Medium Low

Production Integration

Once the model is exported, using ONNX Runtime allows you to benefit from advanced techniques like quantization (int8), which reduces model weight size while minimizing loss of accuracy.

💡 Cloud & Infrastructure Impact: For cloud deployments, this translates directly into reduced inference costs. Instances can process more requests per second for the same or lower memory consumption.

For developers working on applications based on generative models, moving to ONNX via Optimum is a recommended step before any large-scale production deployment. This approach guarantees a robust and high-performance technological foundation, ready for high-availability environments.