🚀 Introducing FlashTokenizer: The World's Fastest CPU Tokenizer!

🤗Transformers
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FlashTokenizer: The World’s Fastest CPU Tokenizer

:play_button: FlashTokenizer Demo Video

As large language models (LLMs) and artificial intelligence applications become increasingly widespread, the demand for high-performance natural language processing tools continues to grow. Tokenization is a crucial step in language model inference, directly impacting overall inference speed and efficiency. Today, we’re excited to introduce FlashTokenizer, a groundbreaking high-performance tokenizer.

What is FlashTokenizer?

FlashTokenizer is an ultra-fast CPU tokenizer optimized specifically for large language models, particularly those in the BERT family. Developed in high-performance C++, it delivers extremely rapid tokenization speeds while maintaining exceptional accuracy.

Compared to traditional tokenizers like BertTokenizerFast, FlashTokenizer achieves a remarkable 8 to 15 times speed improvement, significantly reducing inference processing time.

Key Features

  • :high_voltage: Exceptional Speed: Tokenization speeds are 8-15x faster than traditional methods.
  • :hammer_and_wrench: High-performance C++: Efficient, low-level C++ implementation greatly reduces CPU overhead.
  • :counterclockwise_arrows_button: Parallel Processing with OpenMP: Takes full advantage of multicore processors for parallel execution.
  • :package: Easy Installation: Quickly install and use via pip.
  • :laptop: Cross-Platform Compatibility: Seamlessly supports Windows, macOS, and Ubuntu.

How to Use

Installing FlashTokenizer is straightforward and quick using pip:

pip install flash-tokenizer

For detailed usage instructions and example code, please visit our official GitHub repository: FlashTokenizer GitHub.

Use Cases

  • Frequent text processing tasks for large language model inference.
  • Real-time applications requiring high-speed inference performance.
  • Running LLM inference in CPU environments to reduce hardware costs.

Experience FlashTokenizer

To demonstrate FlashTokenizer’s performance clearly, we’ve created a demonstration video. Click the link below to see it in action:

We welcome everyone to try it out, provide feedback, and contribute to its ongoing improvement.

Give FlashTokenizer a try today, and accelerate your language model inference!

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