| # Diffusion Text Generation |
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| This directory contains implementations for Diffusion LLMs (DLLMs) |
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| More Info: |
| - https://github.com/ggml-org/llama.cpp/pull/14644 |
| - https://github.com/ggml-org/llama.cpp/pull/14771 |
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| ## Parameters |
| The diffusion CLI supports various parameters to control the generation process: |
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|
| ### Core Diffusion Parameters |
| - `--diffusion-steps`: Number of diffusion steps (default: 256) |
| - `--diffusion-algorithm`: Algorithm for token selection |
| - `0`: ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006. |
| - `1`: ENTROPY_BASED - Entropy-based selection |
| - `2`: MARGIN_BASED - Margin-based selection |
| - `3`: RANDOM - Random selection |
| - `4`: CONFIDENCE_BASED - Confidence-based selection (default) |
| - More documentation here https://github.com/DreamLM/Dream |
| - `--diffusion-visual`: Enable live visualization during generation |
| |
| ### Scheduling Parameters |
| Choose one of the following scheduling methods: |
| |
| **Timestep-based scheduling:** |
| - `--diffusion-eps`: Epsilon value for timestep scheduling (e.g., 0.001) |
| |
| **Block-based scheduling:** |
| - `--diffusion-block-length`: Block size for block-based scheduling (e.g., 32) |
| |
| ### Sampling Parameters |
| - `--temp`: Temperature for sampling (0.0 = greedy/deterministic, higher = more random) |
| - `--top-k`: Top-k filtering for sampling |
| - `--top-p`: Top-p (nucleus) filtering for sampling |
| - `--seed`: Random seed for reproducibility |
| |
| ### Model Parameters |
| - `-m`: Path to the GGUF model file |
| - `-p`: Input prompt text |
| - `-ub`: Maximum sequence length (ubatch size) |
| - `-c`: Context size |
| - `-b`: Batch size |
| |
| ### Examples |
| #### Dream architecture: |
| ``` |
| llama-diffusion-cli -m dream7b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-eps 0.001 --diffusion-algorithm 3 --diffusion-steps 256 --diffusion-visual |
| ``` |
| |
| #### LLaDA architecture: |
| ``` |
| llama-diffusion-cli -m llada-8b.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-block-length 32 --diffusion-steps 256 --diffusion-visual |
| ``` |
| |
| #### RND1 architecture: |
| ``` |
| llama-diffusion-cli -m RND1-Base-0910.gguf -p "write code to train MNIST in pytorch" -ub 512 --diffusion-algorithm 1 --diffusion-steps 256 --diffusion-visual --temp 0.5 --diffusion-eps 0.001 |
| ``` |
| |
