sentencepiece

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

by Orchestra-Research 12k GitHub stars MIT Updated Jun 16, 2026
sentencepiece skill by Orchestra-Research
sentencepiece — Productivity skill by Orchestra-Research

Overview

sentencepiece is an open-source productivity skill for Claude Code and compatible agents, published by Orchestra-Research. Its author describes it as: “Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train o…”. The project has 12k stars on GitHub and is available under the MIT license. Add it to your setup with `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`.

What sentencepiece does

Unsupervised tokenizer that works on raw text without language-specific preprocessing.

What’s inside

The SKILL.md for sentencepiece is organised into these sections:

  • When to use SentencePiece
  • Quick start
  • Installation
  • Train model
  • Encode and decode
  • Language-independent design
  • Whitespace as symbol (▁)
  • Tokenization algorithms
  • BPE (Byte-Pair Encoding)
  • Unigram (default)
  • Training configuration
  • Essential parameters

Installation

Add sentencepiece to your agent with:

/plugin marketplace add Orchestra-Research/AI-Research-SKILLs

Review a skill’s source before installing it — skills can instruct your agent to run commands. This command comes from the skill’s public repository, which is the source of truth for setup steps. New to skills? Read how to install Claude Code skills.

Supported agents

  • Claude Code — ships a claude code plugin marketplace
  • Codex — declared in the repository topics

License

sentencepiece is published under the MIT license.

Strengths

  • Clear MIT license — safe to read and adapt within its terms
  • Ships in Orchestra-Research/AI-Research-SKILLs, an established project with 11,807 GitHub stars
  • Works with Claude Code, Codex

Author

Orchestra-Research 41 skills in the catalog · GitHub profile ↗
View skills

Topics

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Frequently asked questions

What does sentencepiece do?
Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.
How do I install sentencepiece?
Run /plugin marketplace add Orchestra-Research/AI-Research-SKILLs in your agent, then reload your skills. Review the source at https://github.com/Orchestra-Research/AI-Research-SKILLs before installing.
Is sentencepiece free to use?
Yes. sentencepiece is free and open source under the MIT license, so you can read, run, and adapt it within that license's terms.
Which agents does sentencepiece work with?
sentencepiece lists Claude Code and Codex — named in its SKILL.md, its plugin marketplace or its repository topics. Skills use the portable SKILL.md format, so other agents that read that format may load it too.
Where does sentencepiece come from?
sentencepiece ships inside Orchestra-Research/AI-Research-SKILLs, a repository that contains 41 catalogued skills in total. The repository's 11,807 GitHub stars apply to that whole collection, not to this skill on its own.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

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State-space model with O(n) complexity vs Transformers' O(n²). 5× faster inference, million-token sequences, no KV cache. Selective SSM with hardware-aware design. Mamba-1 (d_state=16) and Mamba-2 (d_state=128, multi-head). Models 130M-2.8B on HuggingFace.

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Educational GPT implementation in ~300 lines. Reproduces GPT-2 (124M) on OpenWebText. Clean, hackable code for learning transformers. By Andrej Karpathy. Perfect for understanding GPT architecture from scratch. Train on Shakespeare (CPU) or OpenWebText (multi-GPU).

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RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

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Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.

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12k GitHub stars MIT Updated Jun 16, 2026 Free