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Marcel Petrick 92f7da00b4
chore : correct typos [no ci] (#20041) 
* fix(docs): correct typos found during code review

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- Fixed minor spelling mistakes in comments
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- No variables, logic, or functional code was modified.

Signed-off-by: Marcel Petrick <mail@marcelpetrick.it>

* Update docs/backend/CANN.md

Co-authored-by: Aaron Teo <taronaeo@gmail.com>

* Revert "Auxiliary commit to revert individual files from 846d1c301281178efbc6ce6060ad34c1ebe45af8"

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* Update tests/test-backend-ops.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

* Update tests/test-backend-ops.cpp

Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>

---------

Signed-off-by: Marcel Petrick <mail@marcelpetrick.it>
Co-authored-by: Aaron Teo <taronaeo@gmail.com>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
 		2026-03-05 08:50:21 +01:00
..
CMakeLists.txt Support diffusion models: Add Dream 7B (#14644) 2025-07-16 20:03:51 +08:00
README.md chore : correct typos [no ci] (#20041) 2026-03-05 08:50:21 +01:00
diffusion-cli.cpp Fix locale-dependent float printing in GGUF metadata (#17331) 2026-03-04 09:30:40 +01:00

README.md

Diffusion Text Generation

This directory contains implementations for Diffusion LLMs (DLLMs)

More Info:

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: 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