A 125-million-parameter Llama-style language model, pretrained from random weights on a legal and financial corpus. Give it the start of a sentence and it continues in the legal register.
This is a base model, not a chatbot. It was trained on next-token prediction only, so it continues text rather than answering questions. Prompt it with the opening of a sentence and watch it complete the thought.
The honest quality metric is held-out validation perplexity: 8.35 (lower is better), reached over 4 epochs of pretraining. It speaks the legal register fluently (case-citation phrasing, procedural language) but it does not know facts, at 125M parameters a model holds only about 31MB of usable knowledge. Grounded facts would need retrieval (RAG).
Corpus: US case law (~40%), SEC filings (~40%), educational web text (~20%). First call may take ~15–30s while the model wakes from idle.