ML-GSAI/LLaDA

Features

• Masked Text Diffusion - Implements a masked diffusion architecture that iteratively refines tokens into final text.
• Multimodal Diffusion Models - A system that converts visual inputs into text responses using a diffusion process for complex multimodal tasks.
• Vision-Language Cross-Attention Fusions - Fuses visual encoder features with text embeddings to enable image-conditioned text generation.
• Cross-Attention Conditioning - Implements cross-attention mechanisms to inject visual and textual context into the diffusion denoising process.
• Iterative Refinement Generation - Generates text by repeatedly updating and refining masked sequences until convergence.
• Vision-Language Models - Integrates visual and linguistic processing to describe images and answer questions.
• Diffusion Masked Language Models - Generates text by iteratively refining masked tokens via a diffusion process.
• Preference Optimization - Refines model responses based on human preferences using log-likelihood and evidence lower bounds.
• Visual-to-Text Generation - Converts visual inputs into text responses using a diffusion process for multimodal tasks.
• Discrete Diffusion Models - Utilizes a discrete state space and categorical distributions for token transitions during diffusion.
• Variational Lower Bound Estimations - Calculates likelihoods via a variational lower bound to optimize model responses for specific preferences.
• Conditional Masked Generators - Produces conditional text sequences by iteratively filling in missing tokens through masking.
• Sequence Likelihood Estimators - Calculates probability estimates of sequences using evidence lower bounds for preference optimization.
• Sequence Likelihood Scores - Measures the log-likelihood of text sequences to evaluate model predictive accuracy.
• Language Diffusion Models - Large language diffusion models for text generation.

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