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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use diffuse-cpp/Dream-v0-Instruct-7B-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/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 serve -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

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

Atomic Chat

Dream-v0-Instruct-7B-GGUF / README.md

Carmenest

docs: improve model card with quickstart, benchmarks, Apache-2.0

af328fc verified 4 months ago

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	---
	license: apache-2.0
	tags:
	- diffusion
	- dream
	- gguf
	- cpu-inference
	- diffuse-cpp
	language:
	- en
	base_model: Dream-org/Dream-v0-Instruct-7B
	pipeline_tag: text-generation
	---

	# Dream-v0-Instruct-7B-GGUF

	GGUF quantizations of [Dream-org/Dream-v0-Instruct-7B](https://huggingface.co/Dream-org/Dream-v0-Instruct-7B) for use with [diffuse-cpp](https://github.com/iafiscal1212/diffuse-cpp), the first C++ inference engine for Diffusion Language Models.

	Dream is a masked diffusion language model based on the Qwen2.5-7B backbone with Grouped Query Attention (GQA). It generates all tokens in parallel through iterative refinement, excelling at math and factual tasks.

	Dream correctly solves 15 x 23 = 345 in just 2 denoising steps at 21.6 tok/s — 2.5x faster than llama.cpp.

	## Available Quantizations

	\| File \| Type \| Size \| Description \|
	\|------\|------\|------\|-------------\|
	\| `dream-7b-f16.gguf` \| F16 \| ~15 GB \| Full precision, best quality \|
	\| `dream-7b-q8_0.gguf` \| Q8_0 \| ~8.2 GB \| 8-bit quantization, near-lossless \|
	\| `dream-7b-q4km.gguf` \| Q4_K_M \| ~5.0 GB \| 4-bit mixed, best speed/quality ratio \|

	Recommended: Q4_K_M for most users.

	## Quick Start

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

	# Build diffuse-cpp (v0.2.0+)
	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)

	# Run
	./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
	```

	## Performance

	Benchmarked on AMD EPYC 4465P 12-Core, Q4_K_M, entropy_exit + inter-step cache, B=64:

	\| Prompt \| tok/s \| Steps \| vs llama.cpp \|
	\|--------\|-------\|-------\|-------------\|
	\| Capital of France? \| 21.6 \| 2 \| 2.5x \|
	\| 15 x 23? \| 21.6 \| 2 \| 2.5x \|
	\| Translate to French \| 14.3 \| 6 \| 1.7x \|
	\| Translate to Spanish \| 13.2 \| 10 \| 1.6x \|
	\| Python is_prime() \| 8.2 \| 7 \| 1.0x \|
	\| Why sky blue? \| 4.9 \| 16 \| 0.6x \|
	\| List planets \| 4.9 \| 16 \| 0.6x \|
	\| Poem about ocean \| 4.5 \| 16 \| 0.5x \|
	\| Average \| 11.6 \| \| 1.4x \|

	- Dream excels at math and code (converges in 2-7 steps)
	- 5 of 8 prompts match or beat llama.cpp (8.51 tok/s baseline)
	- llama.cpp baseline: Qwen2.5-7B-Instruct, Q4_K_M, same hardware

	## Dream vs LLaDA

	\| Strength \| Dream-7B \| LLaDA-8B \|
	\|----------\|----------\|----------\|
	\| Math/Arithmetic \| 21.6 tok/s (2 steps) \| 6.0 tok/s (16 steps) \|
	\| Code generation \| 8.2 tok/s (7 steps) \| 4.5 tok/s (15 steps) \|
	\| Translation \| 13-14 tok/s \| 23-28 tok/s \|
	\| Creative writing \| 4.5 tok/s \| 5.0 tok/s \|

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

	## Model Details

	- Architecture: Qwen2.5-7B backbone with bidirectional attention
	- Parameters: 7.62B
	- Layers: 28
	- Hidden size: 3584
	- Attention: GQA (28 query / 4 KV heads)
	- FFN: SwiGLU, intermediate 18944
	- Vocabulary: 152,064 tokens
	- RoPE theta: 1,000,000
	- Mask token ID: 151666
	- QKV biases: Yes (kept at F32 in all quantizations)

	## Conversion Details

	339 tensors (255 weights + 84 QKV biases). Converted with `convert-dream.py` from diffuse-cpp.

	## Citation

	```bibtex
	@software{diffuse_cpp_2026,
	title={diffuse-cpp: High-Performance Inference for Diffusion Language Models},
	author={Carmen Esteban},
	year={2026},
	url={https://github.com/iafiscal1212/diffuse-cpp}
	}
	```

	## License

	Apache 2.0