Instructions to use diffuse-cpp/Dream-v0-Instruct-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use diffuse-cpp/Dream-v0-Instruct-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="diffuse-cpp/Dream-v0-Instruct-7B-GGUF",
	filename="dream-7b-q4km.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use diffuse-cpp/Dream-v0-Instruct-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf diffuse-cpp/Dream-v0-Instruct-7B-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/Dream-v0-Instruct-7B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf diffuse-cpp/Dream-v0-Instruct-7B-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/Dream-v0-Instruct-7B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0

Use Docker

docker model run hf.co/diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use diffuse-cpp/Dream-v0-Instruct-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "diffuse-cpp/Dream-v0-Instruct-7B-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/Dream-v0-Instruct-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0

Ollama
How to use diffuse-cpp/Dream-v0-Instruct-7B-GGUF with Ollama:
```
ollama run hf.co/diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0
```

Unsloth Studio new

How to use diffuse-cpp/Dream-v0-Instruct-7B-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/Dream-v0-Instruct-7B-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/Dream-v0-Instruct-7B-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/Dream-v0-Instruct-7B-GGUF to start chatting

Docker Model Runner
How to use diffuse-cpp/Dream-v0-Instruct-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0
```

Lemonade

How to use diffuse-cpp/Dream-v0-Instruct-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull diffuse-cpp/Dream-v0-Instruct-7B-GGUF:Q8_0

Run and chat with the model

lemonade run user.Dream-v0-Instruct-7B-GGUF-Q8_0

List all available models

lemonade list

diffuse-cpp is now Apache-2.0 — Dream-7B excels at math and code on CPU

by Carmenest - opened Mar 31

Discussion

Carmenest

diffuse-cpp org Mar 31

We just open-sourced diffuse-cpp under Apache-2.0!

diffuse-cpp is the first C++ inference engine for Diffusion Language Models, built on GGML.

Dream-7B Benchmarks (AMD EPYC 12-Core, Q4_K_M, entropy_exit + cache)

Prompt	Dream-7B	llama.cpp	Speedup
Capital of France?	21.6 tok/s (2 steps)	8.51 tok/s	2.5x
15 x 23 = ?	21.6 tok/s (2 steps)	8.51 tok/s	2.5x
Translate to French	14.3 tok/s (6 steps)	8.51 tok/s	1.7x
Python is_prime()	8.2 tok/s (7 steps)	8.51 tok/s	1.0x

Dream correctly solves 15 x 23 = 345 in just 2 denoising steps at 21.6 tok/s.

Why diffusion on CPU?

Autoregressive models are memory-bound (one token = one full weight read). Diffusion models generate all tokens in parallel, making them compute-bound. Thread scaling: 7.4x at 12 cores vs 2.4x for AR.

Dream vs LLaDA

Strength	Dream-7B	LLaDA-8B
Math	21.6 tok/s (2 steps)	6.0 tok/s (16 steps)
Code	8.2 tok/s (7 steps)	4.5 tok/s (15 steps)
Translation	13-14 tok/s	23-28 tok/s

Use Dream for math, code, factual. Use LLaDA for translation.

Quick Start

huggingface-cli download diffuse-cpp/Dream-v0-Instruct-7B-GGUF dream-7b-q4km.gguf

git clone --recursive https://github.com/iafiscal1212/diffuse-cpp.git
cd diffuse-cpp && cmake -B build -DCMAKE_BUILD_TYPE=Release && cmake --build build -j$(nproc)

./build/diffuse-cli -m dream-7b-q4km.gguf --tokens "151644,8948,198,2610,525,264,10950,17847,13,151645,198,151644,872,198,3838,374,220,868,1303,220,1419,30,151645,198,151644,77091,198" -n 64 -s 16 -t 12 --remasking entropy_exit

diffuse-cpp
/

Dream-v0-Instruct-7B-GGUF

diffuse-cpp is now Apache-2.0 — Dream-7B excels at math and code on CPU

Dream-7B Benchmarks (AMD EPYC 12-Core, Q4_K_M, entropy_exit + cache)

Why diffusion on CPU?

Dream vs LLaDA

Quick Start

Links