Overview of Natively Supported Quantization Schemes in 🤗 Transformers
Published:Sep 12, 2023 00:00
•1 min read
•Hugging Face
Analysis
This article from Hugging Face likely provides a technical overview of the different quantization techniques supported within the 🤗 Transformers library. Quantization is a crucial technique for reducing the memory footprint and computational cost of large language models (LLMs), making them more accessible and efficient. The article would probably detail the various quantization methods available, such as post-training quantization, quantization-aware training, and possibly newer techniques like weight-only quantization. It would likely explain how to use these methods within the Transformers framework, including code examples and performance comparisons. The target audience is likely developers and researchers working with LLMs.
Key Takeaways
- •The article provides an overview of quantization techniques for LLMs.
- •It likely explains how to use these techniques within the 🤗 Transformers framework.
- •The goal is to improve the efficiency and accessibility of LLMs.
Reference
“The article likely includes code snippets demonstrating how to apply different quantization methods within the 🤗 Transformers library.”