# BYOD: Build Your Own DLM

BYOD is a LoRA-only recipe for converting autoregressive language models into diffusion language models: every base parameter stays frozen, each layer's causal mask becomes bidirectional, and a masked-diffusion objective trains at most 102.4M tokens in 3.1–4.0 hours on one Google Colab GPU, turning Gemma 2 9B, Llama 3.1 8B, Qwen2.5 7B and Ministral 8B into models that generate from fully masked sequences, with adapters at 4.1–5.8% of final parameters. The strongest conversion, BYOD-Gemma, reaches 50.6% average accuracy on nine tasks against 51.9% for LLaDA 8B Instruct in the same harness; open-generation quality at low NFE budgets remains the main limitation.

BYOD is a LoRA-only recipe for converting autoregressive language models into diffusion language models: every base parameter stays frozen, each layer's causal mask becomes bidirectional, and a masked-diffusion objective trains at most 102.4M tokens in 3.1–4.0 hours on one Google Colab GPU, turning Gemma 2 9B, Llama 3.1 8B, Qwen2.5 7B and Ministral 8B into models that generate from fully masked sequences, with adapters at 4.1–5.8% of final parameters. The strongest conversion, BYOD-Gemma, reaches 50.6% average accuracy on nine tasks against 51.9% for LLaDA 8B Instruct in the same harness; open-generation quality at low NFE budgets remains the main limitation.