🛏️ trimbed

Trim a tokenizer's vocabulary, and optionally its model's embedding table, down to the subset you actually need. You can specify "what you need" explicitly, by presets, or by providing a corpus (one or more datasets) as an anchor for what you'd actually like to keep.

The most typical use would be to provide a corpus of the domain and/or language that is relevant for you, and then specifying to, for example, keep only the top 32k tokens from that corpus, or to remove the tail of the corpus by only keeping 99.95% of its tokens. But more options are available!

Read the documentation for the full guides and the API reference.

Installation

pip install trimbed                       # tokenizer trimming only
pip install "trimbed[model]"              # + torch, for trimming embeddings
pip install "trimbed[model,convert]"      # + sentencepiece/protobuf, for spiece-only tokenizers

Or preferably with uv add.

From a git clone the above would be uv sync, uv sync --extra model and uv sync --all-extras.

Quickstart

Installing the package comes with the trimbed command, which is the main entrypoint for users. It has a number of subcommands: trim, count, inspect and presets. trimbed --help lists them.

If you have only cloned the repository, python -m trimbed.cli works too.

# What am I dealing with?
trimbed inspect --model codefuse-ai/F2LLM-v2-160M

# Trim tokenizer + model from a config file
trimbed trim --config my_config.yaml

# Same config but overridden one value and nothing written (dry-run)
trimbed trim --config my_config.yaml --dry-run selection.top_k=30000

# Or without a config, keeping a preset
# (only all alphanumeric tokens in the vocab are kept),
# and the model is not trimmed
trimbed trim --model google-bert/bert-base-multilingual-cased \
    --keep-preset alphanumeric --output-dir trimmed/bert --no-trim-model

From Python:

from trimbed import TrimConfig, TrimPipeline

config = TrimConfig.from_yaml("my_config.yaml")
report = TrimPipeline(config).run()
print(report.render())

examples/ has a few Python examples: inspecting a tokenizer, trimming from rules alone, trimming over a corpus with the model, and registering your own preset.

What it does

• One code path for every family. trimbed operates on the tokenizers backend document (tokenizer.json) rather than on SentencePiece protobufs, so BPE, WordPiece, Unigram and WordLevel are all covered. The vocabulary surgery itself is delegated to skeletoken.
• Selection with provenance. Structural tokens, must-keep rules and the corpus frequency ranking are unioned, closed over BPE merge dependencies, then capped. Every kept token records why it survived.
• Prompts and chat templates stay intact. keep_texts and keep_chat_template keep the ids a prompt is made of, so it goes on tokenizing exactly as it does now.
• The model follows. Embedding table, output head, head bias, tied and untied, encoder-decoders, pad_to_multiple_of alignment rows, and the token ids stored on the config and generation config.
• It proves its work. Both tokenizers re-encode sampled texts, and both models can be run and compared, before anything is called a success.

Documentation

Contributing

See CONTRIBUTING.md. In short: make style, make test and make build-docs before you push.

Acknowledegments

We rely heavily on the internals of skeletoken to map out tokenizers to a standardized Pydantic format.