Instructions to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="diffuse-cpp/LLaDA-8B-Instruct-GGUF",
	filename="llada-8b-q4km.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Use Docker

docker model run hf.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "diffuse-cpp/LLaDA-8B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "diffuse-cpp/LLaDA-8B-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Ollama
How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
```

Unsloth Studio

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for diffuse-cpp/LLaDA-8B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for diffuse-cpp/LLaDA-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for diffuse-cpp/LLaDA-8B-Instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0
```

Lemonade

How to use diffuse-cpp/LLaDA-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull diffuse-cpp/LLaDA-8B-Instruct-GGUF:Q8_0

Run and chat with the model

lemonade run user.LLaDA-8B-Instruct-GGUF-Q8_0

List all available models

lemonade list

Carmenest commited on Mar 19

Commit

0092801

verified ·

1 Parent(s): deb1e7f

Add paper DOI reference

Browse files

Files changed (1) hide show

README.md +3 -5

README.md CHANGED Viewed

@@ -18,6 +18,8 @@ GGUF quantized versions of [GSAI-ML/LLaDA-8B-Instruct](https://huggingface.co/GS
 LLaDA is a **diffusion language model** that generates text by iterative unmasking rather than autoregressive token-by-token prediction.
 ## Available Quantizations
 | File | Quant | Size | Description |
@@ -66,9 +68,5 @@ cmake -B build -DCMAKE_BUILD_TYPE=Release
 cmake --build build -j$(nproc)
 # Generate with entropy_exit (recommended)
-python tools/generate.py \
-    --model-dir /path/to/LLaDA-8B-Instruct \
-    --gguf llada-8b-q4km.gguf \
-    -p "What is the capital of France?" \
-    -s 16 -t 12 --remasking entropy_exit
 ```

 LLaDA is a **diffusion language model** that generates text by iterative unmasking rather than autoregressive token-by-token prediction.
+> **Paper:** [Diffusion Language Models are Faster than Autoregressive on CPU](https://doi.org/10.5281/zenodo.19119814) -- C. Esteban, 2026
 ## Available Quantizations
 | File | Quant | Size | Description |
 cmake --build build -j$(nproc)
 # Generate with entropy_exit (recommended)
+python tools/generate.py     --model-dir /path/to/LLaDA-8B-Instruct     --gguf llada-8b-q4km.gguf     -p "What is the capital of France?"     -s 16 -t 12 --remasking entropy_exit
 ```