Fast dLLM v2 (7B) — Efficient Block Diffusion LLM 📖 Introduction Autoregressive (AR) large language models (LLMs) have achieved remarkable performance across a wide range of natural language tasks, yet their inherent sequential decoding limits inference efficiency . We present Fast dLLM v2 — a carefully designed block diffusion language model (dLLM) that efficiently adapts a pretrained AR model ( Qwen2.5 7B Instruct ) into a diffusion style decoder for parallel text generation . ✨ Key Innovations Block Diffusion Mechanism + Complementary Attention Mask Enables blockwise bidirectional context modeling without sacrificing AR objectives. Hierarchical Caching Block level cache : Stores historical context representations across blocks. Sub block cache : Parallel decoding within partially generated blocks. Token Shift Mechanism Retains autoregressive characteristics while supporting bidirectional context within blocks. Parallel Decoding Pipeline Achieves up to 2.5× speedup over standard AR decoding without compromising quality . 🚀 Fast dLLM v2 uses only ~1B tokens for fine tuning — a 500× reduction vs. full attention diffusion LLMs (Dream: 580B tokens) — while matching or surpassing AR…
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