How to implement a Byte-Level Tokenizer from scratch using sentencepiece for an LLM

Question

Can you tell me how to implement a Byte-Level Tokenizer from scratch using sentencepiece for an LLM?

Answer 1

You can implement a Byte-Level Tokenizer from scratch using SentencePiece by training a model with the --byte_fallback option to ensure byte-level granularity.

Here is the code snippet below:

In the above code we are using the following key points:

• SentencePieceTrainer for training a byte-level tokenizer.

• byte_fallback=True ensures that unseen characters are broken down into bytes.

• Loading and encoding/decoding with SentencePieceProcessor for practical use.

Hence, this method builds a tokenizer that can handle any input robustly at the byte level, making it well-suited for diverse LLM tasks.