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char-fork/llama.cpp

git clone https://git.t4t.associates/char-fork/llama.cpp

Charlotte Somenc/dec in server context8300f14112026-09-15

files

CMakeLists.txt	490 B
README.md	2.3 KiB
diffusion-cli.cpp	9.7 KiB
diffusion.cpp	16.7 KiB
diffusion.h	2.5 KiB

Diffusion Text Generation

This directory contains implementations for Diffusion LLMs (DLLMs)

More Info:

https://github.com/ggml-org/llama.cpp/pull/14644
https://github.com/ggml-org/llama.cpp/pull/14771

Parameters

The diffusion CLI supports various parameters to control the generation process:

Core Diffusion Parameters

--diffusion-steps: Number of diffusion steps (default: 256)
--diffusion-algorithm: Algorithm for token selection
- 0: DIFFUSION_ALGORITHM_ORIGIN - Token will be generated in a purely random order from https://arxiv.org/abs/2107.03006.
- 1: DIFFUSION_ALGORITHM_ENTROPY_BASED - Entropy-based selection
- 2: DIFFUSION_ALGORITHM_MARGIN_BASED - Margin-based selection
- 3: DIFFUSION_ALGORITHM_RANDOM - Random selection
- 4: DIFFUSION_ALGORITHM_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