How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
This repo contains a PyTorch implementation for the paper Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution by Aaron Lou, Chenlin Meng and Stefano Ermon.
The main features of louaaron/score-entropy-discrete-diffusion are: Discrete Diffusion Models, Language Diffusion Models.
Projects with overlapping indexed features include: ml-gsai/llada — LLaDA is a masked diffusion language model and conditional text generator. It generates text by iteratively refining… shentianxiao/film — This repo contains the code for the Fill-in Language Model (FiLM) described in the paper FiLM: Fill-in Language Models… kuleshov-group/mdlm — By Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush,… liusulin/ddpd — Code repository for the paper Think While You Generate: Discrete Diffusion with Planned Denoising, by Sulin Liu, Juno… dreamlm/dream-coder — Dream-Coder 7B is a diffusion LLM for code trained exclusively on open-source data across its development… ashenweli/discrete-diffusion-models-for-language-genaration — This thesis aimstoinvestigate thepotential of discrete diffusion models in the context ofnaturallanguagegeneration.
LLaDA is a masked diffusion language model and conditional text generator. It generates text by iteratively refining masked tokens through a diffusion process rather than predicting the next token in a sequence. The project functions as a vision-language diffusion model, converting visual inputs into text responses. It also serves as a preference optimization framework that uses log-likelihood estimation and evidence lower bounds to tune model responses. The system supports multi-round conversational AI and text sequence evaluation. It integrates vision-language embedding for cross-modal con
Code repository for the paper Think While You Generate: Discrete Diffusion with Planned Denoising, by Sulin Liu, Juno Nam, Andrew Campbell, Hannes Stärk, Yilun Xu, Tommi Jaakkola, Rafael Gómez-Bombarelli. Tweet and video for the main idea.
By Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, Volodymyr Kuleshov
This repo contains the code for the Fill-in Language Model (FiLM) described in the paper FiLM: Fill-in Language Models for Any Order Generation (Shen et al., 2023).