한국어 | English (TBD)
Research codebase exploring training-time dynamic parameter count for Transformers — graph representation is the tool, dynamic growth / pruning / architecture search during training is the goal. (Not sparse activation like MoE: those keep total params fixed.)
Python 3.11+, Jupyter notebooks for experiments, package code in src/graphlm/.
See CLAUDE.md for the full paradigm definition.
pip install -e ".[dev]"
make test
make lintSee docs/ko/README.md for the full guide, and
.claude/rules/ for development conventions.
src/graphlm/ Python package (models, graph utils, training, eval)
notebooks/ Jupyter experiment notebooks
tests/ pytest tests (mirrors src/graphlm/)
data/ experiment data (gitignored)
docs/ CI docs, Korean documentation, paper summaries (docs/papers/)
.claude/ AI collaboration rules and infra
TBD.