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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
This is the official implementation of the paper: A Cheaper and Better Diffusion Language Model with Soft-Masked Noise.
Official Codebase for DiffuSeq: Sequence to Sequence Text Generation With Diffusion Models and DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models.
The main features of shark-nlp/diffuseq are: Continuous Diffusion Models, Discrete Diffusion Models.
Open-source alternatives to shark-nlp/diffuseq include: ml-gsai/llada — LLaDA is a masked diffusion language model and conditional text generator. It generates text by iteratively refining… andrew-cr/tauldr — Paper Link. ashaba1in/smoothie — Paper: https://arxiv.org/pdf/2505.18853. bytedance-seed/cola-dlm — Continuous Latent Diffusion Language Model — a hierarchical latent-space text diffusion model with a block-causal DiT… david3684/flm — Flow Map Language Models: One-step Language Modeling via Continuous Denoising. amazon-science/masked-diffusion-lm — This is the official implementation of the paper: A Cheaper and Better Diffusion Language Model with Soft-Masked Noise.
