DiffReaper-6

DiffReaper-6 is a Large-scale Diffusion-based Large Language Model (Diffusion-LLM) developed by DifferenceLabs.

It represents a significant architectural leap over the previous 5L version, transitioning to a more robust denoiser and a deeper transformer-based backbone to achieve actual conversational coherence.

Model Details

• Architecture: Diffusion-Transformer (DiT) with Adaptive Layer Norm (adaLN-Single) modulation.
• Backbone: 24 Layers, 24 Attention Heads, 1536 Hidden Dimension.
• Tokenizer: BERT-base-uncased.
• Training Objective: MSE on Denoising Latents (Predicting original embeddings from noisy input).
• Conditioning: Prompt-concatenated latents with time-step embedding.

Training

The model is being trained on an RTX 5090 using the ultrachat-10k dataset, focusing on conversational flow and instruction following.

Goal

To prove that diffusion models can reach (and eventually exceed) the coherence of auto-regressive models while maintaining the creative "soul" and parallel generation benefits of diffusion.