unsloth-mcp-server: Skill for Claude Code

SuperBPE - Advanced Tokenization

Expert guidance for SuperBPE tokenizer training, optimization, and deployment across any LLM project.

What is SuperBPE?

SuperBPE is a 2025 tokenization method that achieves significant improvements over standard BPE:

Key Benefits

• 20-33% fewer tokens - More efficient encoding
• Faster inference - Fewer tokens to process
• Lower API costs - Pay per token reduction
• Better context utilization - Fit 40% more content in same window
• Domain-specific optimization - Train for your specific use case
• Framework-agnostic - Use with any LLM (OpenAI, Anthropic, open-source)

How It Works

SuperBPE improves upon standard BPE by:

1. Selective merge inheritance - Inherits 70-90% of BPE merges
2. Domain-aware training - Optimizes for your specific corpus
3. Frequency-based optimization - Prioritizes common patterns
4. Special token handling - Better handling of domain-specific tokens

Performance Impact

Standard BPE:  "The implementation utilizes convolutional neural networks" → 12 tokens
SuperBPE:      "The implementation utilizes convolutional neural networks" → 8 tokens
Reduction:     33% fewer tokens

Monthly savings example:
- 100M tokens/month at $20/1M tokens
- 30% reduction = 30M fewer tokens
- Savings: $600/month = $7,200/year

Quick Start

1. Train SuperBPE Tokenizer

from unsloth.tokenizer import train_superbpe

tokenizer = train_superbpe(
    corpus_path="./training_data.txt",    # Local file or HF dataset
    output_path="./tokenizers/my_tokenizer.json",
    vocab_size=50000,
    num_inherit_merges=40000  # 80% of vocab_size (recommended)
)

2. Compare with Standard Tokenizers

from unsloth.tokenizer import compare_tokenizers

results = compare_tokenizers(
    text="Your sample text here...",
    tokenizer1="meta-llama/Llama-3.2-1B",      # Standard BPE
    tokenizer2="./tokenizers/my_tokenizer.json" # Your SuperBPE
)

print(f"Standard BPE: {results['tokenizer1']['tokens']} tokens")
print(f"SuperBPE: {results['tokenizer2']['tokens']} tokens")
print(f"Reduction: {results['reduction']}")  # e.g., "25.3%"