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SymbolicLight V1 Open Code

This directory contains the public Python implementation for SymbolicLight V1, including tooling for the released 0.8B checkpoint.

Contents

  • model.py: SymbolicLight model definition.
  • train_base.py: distributed pre-training entry point with an aggregate domain-level data recipe.
  • pretokenize.py: tokenization helper for local corpora.
  • eval_08.py: PyTorch checkpoint evaluation and generation helper.
  • chat.py: interactive single-process chat loop for local checkpoint testing.
  • data_pipeline.py: aggregate-domain parquet and memmap data pipeline.
  • train_tokenizer.py: tokenizer wrapper.

Data Policy

This public package does not include raw training text, raw validation text, source-level dataset names, source identifiers, download URLs, or source-level manifests. To train from scratch, prepare your own legally available corpus under the aggregate domain directories expected by train_base.py.

For a package-level reproducibility summary, see ../REPRODUCIBILITY.md and ../paper_results_manifest.json.

License

The source code, training scripts, inference scripts, tokenizer assets, cleaned weights-only checkpoint, and public documentation are released under Apache License, Version 2.0, unless a file states otherwise. Training and validation data are not included and are not licensed through this repository.

Example 

pip install -r requirements.txt

python eval_08.py \
  --checkpoint_path ../weights/pytorch/latest.pt \
  --generate_only \
  --prompts "SymbolicLight is" \
  --max_new_tokens 1 \
  --device cpu

python chat.py \
  --checkpoint_path ../weights/pytorch/latest.pt \
  --tokenizer_path ../tokenizer/sl_tokenizer.model \
  --device cuda \
  --allow_windows_cuda

For this pre-training checkpoint, chat.py uses a constrained decoding path and defaults to --prompt_format answer. This reduces repetition and keeps replies closer to short direct answers, but it does not turn the checkpoint into a polished assistant model.

The checkpoint in ../weights/pytorch/latest.pt is a pre-training checkpoint. It is not instruction-aligned and should be evaluated as a scale-up/pre-training artifact rather than as a polished assistant model.

On Windows, the script uses a CPU-safe checkpoint loader by default. CUDA transfer for this large checkpoint is treated as experimental and requires --allow_windows_cuda.