Overview of natively supported quantization schemes in 🤗 Transformers
Hugging Face provides an overview of quantization schemes natively supported in the Transformers library. Quantization reduces model size and improves inference speed. The library supports various quantization methods, including dynamic quantization, static quantization, and quantization-aware training. You can use these methods to deploy models more efficiently.
Key takeaways
- Hugging Face Transformers supports dynamic, static, and quantization-aware training.
- Quantization reduces model size and speeds up inference.
- Efficient deployment relies on choosing the right quantization method.
Overview of natively supported quantization schemes in 🤗 Transformers
Hugging Face provides an overview of quantization schemes natively supported in the Transformers library. Quantization reduces model size and improves inference speed. The library supports various quantization methods, including dynamic quantization, static quantization, and quantization-aware training. You can use these methods to deploy models more efficiently.
Key takeaways
- Hugging Face Transformers supports dynamic, static, and quantization-aware training.
- Quantization reduces model size and speeds up inference.
- Efficient deployment relies on choosing the right quantization method.