xlm-core 0.3.0a0

XLM Framework

XLM is a unified framework for developing and comparing small non-autoregressive language models. It uses PyTorch as the deep learning framework, PyTorch Lightning for training utilities, and Hydra for configuration management. XLM provides core components for flexible data handling and training, useful architectural implementations for non-autoregressive workflows, and support for arbitrary runtime code injection. Custom model implementations that leverage the core components of xlm can be found in the xlm-models package. The package also includes a few preconfigured synthetic planning and language-modeling datasets.

Usage:

pip install xlm-core pip install “xlm-core[safe]” # optional: SAFE molecule preprocessing / evaluators pip install “xlm-core[molgen]” # optional: fuller GenMol / Biomemo stack (molgen_requirements.txt) pip install “xlm-core[llm_eval]” # optional: ANTLR build of math-verify (LLM benchmarks) pip install “xlm-core[mauve]” # optional: MAUVE post-hoc text evaluation (mauve-text) pip install “xlm-core[all]” # union of safe + molgen + llm_eval + mauve (used in CI) xlm job_type=[JOB_TYPE] job_name=[JOB_NAME] experiment=[CONFIG_PATH]

The job_type argument can be one of train ,eval and generate. The experiment argument should point to the root hydra config file.