FlashTokenizer: The World’s Fastest CPU Tokenizer

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

- ⚡ Exceptional Speed: Tokenization speeds are 8–15x faster than traditional methods.
- 🛠️ High-performance C++: Efficient, low-level C++ implementation greatly reduces CPU overhead.
- 🔄 Parallel Processing with OpenMP: Takes full advantage of multicore processors for parallel execution.
- 📦 Easy Installation**: Quickly install and use via pip.
- 💻 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: https://github.com/NLPOptimize/flash-tokenizer

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:

▶️ FlashTokenizer Demo Video: https://www.youtube.com/watch?v=a_sTiAXeSE0

GitHub : https://github.com/NLPOptimize/flash-tokenizer

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!