datasets:
- d3LLM/Ling-Coder-dParallel-merged-512-120k
base_model: Dream-org/Dream-Coder-v0-Instruct-7B
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
tags:
- diffusion
- fast-inference
- d3llm
d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation 🚀
d3LLM-Dream-Coder is an ultra-fast diffusion language model introduced in the paper d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation. It is built on Dream-org/Dream-Coder-v0-Instruct-7B.
Model Description
d3LLM (pseuDo-Distilled Diffusion Large Language Model) is a framework designed to strike a balance between accuracy and parallelism in diffusion LLMs. It achieves up to 10× speedup over vanilla diffusion models like LLaDA/Dream and 5× speedup over autoregressive (AR) models.
The model utilizes two primary innovations:
- Pseudo-Trajectory Distillation: A training method that teaches the model which tokens can be decoded confidently at early steps.
- Entropy-Based Multi-Block Decoding: An inference strategy using a KV-cache refresh mechanism to maintain accuracy while maximizing parallelism.
Resources
- Paper: d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation
- Repository: https://github.com/hao-ai-lab/d3LLM
- Blog: https://hao-ai-lab.github.io/blogs/text-diffusion/
- Demo: https://d3llm-team.github.io/
Usage
For detailed usage instructions, evaluation scripts, and training code, please refer to the official GitHub repository. Since the model uses a custom architecture, ensure you have transformers==4.49.0 installed and use trust_remote_code=True when loading the model.
Citation
@article{arxiv'26:d3llm,
title = {d3LLM: Ultra-Fast Diffusion LLM using Pseudo-Trajectory Distillation},
author = {Yu-Yang Qian and Junda Su and Lanxiang Hu and Peiyuan Zhang and Zhijie Deng and Peng Zhao and Hao Zhang},
journal = {ArXiv preprint},
volume = {arXiv:2601.07568},
year = {2026}
}