--- library_name: transformers license: cc-by-nc-4.0 language: - en tags: - tokenizer - bpe - byte-level - tr-hash --- # TR-HASH Tokenizer 32K The 32,000-token ByteLevel BPE tokenizer used by the TR-HASH language-model research line and the 200B-token pretraining mixture. ## Properties - Vocabulary size: 32,000 - Model: ByteLevel BPE - Normalization: Unicode NFC - EOS: `` (ID 0) - PAD: `` (ID 1) - BOS: `` (ID 2) - UNK: `` (ID 3) - Declared model context: 2,048 tokens ## Usage ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "AETHORIA-AI/TR-HASH-Tokenizer-32K" ) encoded = tokenizer("Token routing starts with tokenization.") decoded = tokenizer.decode(encoded["input_ids"]) ``` This repository contains the tokenizer only. It does not define a chat template or a model architecture. ## Comparison with GPT `r50k_base` `r50k_base` is used here as the approximately 50K-token GPT reference. The comparison uses `transformers` without added special tokens for TR-HASH and `tiktoken` for `r50k_base`. | Encoding | Vocabulary | Tokens on fixed suite | Characters/token | |---|---:|---:|---:| | TR-HASH Tokenizer 32K | 32,000 | 375 | 3.712 | | GPT `r50k_base` | 50,257 | 354 | 3.932 | TR-HASH uses a **36.3% smaller vocabulary**. On the fixed 1,392-character suite included in [`benchmark_r50k.py`](benchmark_r50k.py), it produces 5.9% more tokens than `r50k_base`. At hidden size 1,024, the smaller vocabulary removes 18,695,168 embedding parameters, or about 37.4 MB in BF16/FP16, when input and output embeddings are tied. This small suite covers English prose, technical text, code/JSON, mathematics, and French. It is an illustrative, reproducible check rather than a claim about every corpus. Compression should be measured again on the intended training or deployment distribution. ```bash pip install transformers tiktoken python benchmark_r50k.py ``` ## Associated dataset [AETHORIA-AI/data-32k-200b-tokens](https://huggingface.co/datasets/AETHORIA-AI/data-32k-200b-tokens)