mint-llm 0.0.5

Meaning-Informed Next-token Transformation

MINT

Meaning-Informed Next-token Transformation

Project Goals

MINT adds a transformation layer that redistributes next-token probabilities according to semantic token similarity based on the model's embedding space. The aim is to produce more varied, human-like text without sacrificing coherence.

Installation

Create a virtual environment and install the package in editable mode:

pip install -e .

This installs the mint package and provides the mint command-line interface.

To use the published release from PyPI (when available), run:

pip install mint-llm

CLI Usage

Run the CLI with:

mint --help

The mint command exposes several subcommands. The typical workflow is shown below.

Using MINT

Run the commands below to build and apply the redistribution layer:

1. Pick a checkpoint from the Hugging Face Hub (optional).

   mint pick <model_id> checkpoint/
   

2. Extract token embeddings from the checkpoint.

   mint extract checkpoint/model.safetensors embeddings.safetensors
   

3. Blend the embeddings into a low-rank similarity factor.

   mint blend embeddings.safetensors mint_out/ --rank 1024 --gpu 0
   # → mint_out/W.safetensors (plus R.safetensors with --keep-residual)
   

   Use -r/--rank to set factor rank (default 1024). Add --cpu or --gpu IDX to choose the device. Pass --keep-residual to also save a sparse R.safetensors file.

4. Brew new text from the wrapped model.

   mint brew model_id_or_path mint_out/ --prompt "Hello"
   

   Omit --prompt or pass --interactive to read prompts from stdin.

5. Infuse the tested similarity matrix into a local model and save the result to a directory.

   mint infuse path/to/model mint_out/ infused-model --alpha 0.1
   

   from mint.wrapper import load_wrapped_model
   from mint.logits import SRLogitsProcessor
   
   model, tokenizer, layer = load_wrapped_model("model_id_or_path", "mint_out/")
   processor = SRLogitsProcessor(layer)
   

See the notebooks and examples/quickstart.py for a more detailed walk-through and an automated script. You can also explore the generator interactively using the CLI.

Additional Utilities

The CLI exposes optional commands for working with checkpoints:

• Crush merge sharded checkpoints referenced by an index file.

  mint crush checkpoint/model.safetensors.index.json checkpoint/model.safetensors
  

• Chop split a .safetensors checkpoint into shards. Provide a shard count or size:

  mint chop model.safetensors shards/ --shards 2
  
  mint chop model.safetensors shards/ --size-mb 500
  

Brand-based ISVD Functions

MINT ships helper utilities implementing Brand's Incremental Singular Value Decomposition alongside Zhang et al.'s update strategy. Use initialize_isvd, update_isvd4, and update_isvd4_check from mint.brand_svd to maintain low-rank factors as new embedding vectors are streamed. These functions support optional weighting matrices to match the similarity metric. We continuously refine the implementation and optimize performance—see the Brand SVD folder for the original papers.

Quickstart Script

Run examples/quickstart.py for an end-to-end demonstration. The script mirrors the mint CLI commands: extract, blend and brew.

Required argument:

• --prompt – input text to generate from.

Optional arguments default to values defined in tests/utils/model_config.json:

• --checkpoint – checkpoint path. If this points to a *.safetensors.index.json file the required shards are downloaded and merged automatically. If omitted model_url is used.
• --model – model identifier or path. When a model ID is provided the checkpoint shards are fetched and merged automatically. Defaults to model_id or one derived from model_url.
• --embeddings – output file for embeddings (default embeddings.safetensors).
• --similarity – output directory for W.safetensors (and optionally R.safetensors, default .cache/mint).

python examples/quickstart.py --prompt "Hello"

The script extracts embeddings, builds the similarity matrix and generates text using the wrapped model.

Examples

Practical examples are provided in the notebooks directory. They demonstrate embedding extraction, building a similarity matrix and brewing text from a short prompt.

Development

Install development dependencies with:

pip install -e '.[dev]'

Use the provided Makefile to run common tasks:

make format     # check black formatting
make lint       # run ruff and mypy (if configured)
make lint-fast  # run ruff only, skip mypy
make test       # run the pytest suite
make all        # runs all checks

make commands format, lint, lint-fast, and all can also be suffixed with -fix (e.g. make lint-fix or make all-fix) to attempt to automatically fix issues. make fix will run all-fix.

Tests are executed with -Werror, so any warnings will fail the build.

Continuous integration uses .github/workflows/lint.yml and .github/workflows/tests.yml. Tagged releases trigger .github/workflows/publish.yml, which uses scripts/prepare_pypi_readme.py to prepare the README for PyPI before uploading.

Contributing

Development tasks are tracked in todos.json. See project_proposal-MINT.md for the full technical plan. Release notes are available in CHANGELOG.md. Feel free to open issues or pull requests to contribute.

Citation

cff-version: 1.2.0
title: MINT - Meaning-Informed Next-token Transformation
message: 'If you reference this project, please cite it as below.'
type: software
authors:
  - given-names: Bryan
    family-names: O'Malley
    email: [e-mail]
identifiers:
  - type: url
    value: 'https://github.com/Reithan/MINT'
    description: github repo for MINT
repository-code: 'https://github.com/Reithan/MINT'
url: 'https://github.com/Reithan/MINT'
abstract: >-
  MINT adds a post-softmax decoding layer that redistributes
  token log-probs according to token similarity based on
  the model's embedding space. The aim is to produce more
  varied, human-like text without sacrificing coherence.
keywords:
  - llm
  - ai
  - svd
  - isvd
  - transformers
  - safetensors
  - text-generation
  - chat-completion
  - huggingface
commit: d506b14d3bc033aef43d481fed70869e1dda6367
version: v0.0.5-pre-release
date-released: '2025-06-19'

references:
  - type: conference-paper
    title: "Incremental Singular Value Decomposition of Uncertain Data with Missing Values"
    authors:
      - given-names: Matthew
        family-names: Brand
    year: 2002
    doi: "10.1007/3-540-47969-4_47"
    url: "https://link.springer.com/chapter/10.1007/3-540-47969-4_47"
    citation: "@inproceedings{brand2002incremental,\n  author = {Brand, M.},\n  title = {{Incremental Singular Value Decomposition of Uncertain Data with Missing Values}},\n  booktitle = {European Conference on Computer Vision (ECCV)},\n  volume = {2350},\n  pages = {707--720},\n  year = {2002},\n  doi = {10.1007/3-540-47969-4_47},\n  url = {https://link.springer.com/chapter/10.1007/3-540-47969-4_47}\n}"
  - type: article
    title: "An answer to an open question in the incremental SVD"
    authors:
      - given-names: Yangwen
        family-names: Zhang
    year: 2022
    url: "https://arxiv.org/abs/2204.05398"
    citation: "@article{zhang2022answer,\n  author = {Zhang, Yangwen},\n  title = {{An answer to an open question in the incremental SVD}},\n  journal = {arXiv preprint arXiv:2204.05398},\n  year = {2022},\n  url = {https://arxiv.org/abs/2204.05398}\n}"