FlameForge makes fine-tuning approachable. Instead of editing brittle YAML files and memorizing CUDA flags, you launch a terminal interface, pick a model, and load your data.

Continued pre-training adapts a base model to a new domain or writing style. Provide a corpus of raw text — documentation, transcripts, or notes — and the model learns the vocabulary and cadence of that domain.

Large language models are trained to predict the next token in a sequence. By exposing them to in-domain text, the distribution of likely continuations shifts toward your domain, improving downstream performance.

Memory safety matters on laptops. On Apple Silicon the GPU shares unified memory with the operating system, so a runaway allocation can freeze the whole machine. FlameForge budgets memory conservatively and monitors usage during training.

A short document can be a single paragraph. Documents are separated by blank lines, so this corpus contains several independent passages that the tokenizer will pack into training sequences.