This repository contains the LoRA adapter for the Dream-org/Dream-v0-Base-7B model, trained using the Discrete Diffusion Forcing (D2F) method. This adapter allows the Dream-Base-7B diffusion LLM (dLLM) to achieve inference speeds that are significantly faster than both its original version and leading autoregressive (AR) models like LLaMA3, while maintaining comparable output quality. The D2F method and its results are detailed in the paper: D2F: Diffusion LLMs Can Do Faster-Than-AR Inference via Discrete Diffusion Forcing. — Official Code: D2F GitHub Repository — Demo Space: D2F-LLaDA-Instruct-8B Diffusion LLMs (dLLMs) have long promised ultra-fast parallel decoding, but this potential was historically crippled by two main bottlenecks: 1. KV Cache Incompatibility: Their bidirectional attention mechanism prevented the use of the Key-Value Cache, a critical optimization in AR models. 2. Strict Inter-Block Dependency: Previous attempts at block-based generation required each block to be fully generated before starting the next, preventing true parallelism. 1. Hybrid Architecture: D2F reframes text generation as a block-autoregressive process. Within a block: Attention remains…
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Автор: SJTU-DENG-Lab
Теги: d2f, diffusion-llm, dream, lora, en, endpoints_compatible
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Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
