superbpe

What problem does it solve?

Train and deploy SuperBPE tokenizers to reduce token usage across projects, enabling cost-efficient and faster model interactions.

Core Features & Use Cases

• Tokenizer training: Create domain-optimized tokenizers with high compression and wide framework compatibility.
• Validation & benchmarking: Assess token reductions and term-level tokenization quality for production readiness.
• Deployment integration: Export to HuggingFace JSON or other formats and integrate with OpenAI, Claude, or HF models in production.

Quick Start

Train a SuperBPE tokenizer on your corpus, validate it with a representative test set, and export to a format compatible with your model.