llama.cpp/examples/diffusion/README.md

# 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


Example of using Dream architechture: `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`

Example of using LLaDA architechture: `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`
Add LLaDA 8b Diffusion model (#14771) * Add support for Llada-8b: diffusion model * Add README * Fix README and convert_hf_to_gguf * convert_hf_to_gguf.py: address review comments * Make everything in a single example * Remove model-specific sampling * Remove unused argmax * Remove braced initializers, improve README.md a bit * Add diffusion specific gguf params in set_vocab, remove setting rope_theta and rms_norm_eps * Remove adding the mask token * Move add_add_bos_token to set_vocab * use add_bool in gguf_writer.py 2025-07-31 19:49:09 +08:00			`# 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`


			Example of using Dream architechture: `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`

			Example of using LLaDA architechture: `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`