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

Libraries

How to use bdbj/Dream-v0-Instruct-7B-SM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bdbj/Dream-v0-Instruct-7B-SM", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bdbj/Dream-v0-Instruct-7B-SM", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bdbj/Dream-v0-Instruct-7B-SM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bdbj/Dream-v0-Instruct-7B-SM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bdbj/Dream-v0-Instruct-7B-SM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bdbj/Dream-v0-Instruct-7B-SM

SGLang

How to use bdbj/Dream-v0-Instruct-7B-SM with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bdbj/Dream-v0-Instruct-7B-SM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bdbj/Dream-v0-Instruct-7B-SM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bdbj/Dream-v0-Instruct-7B-SM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bdbj/Dream-v0-Instruct-7B-SM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bdbj/Dream-v0-Instruct-7B-SM with Docker Model Runner:
```
docker model run hf.co/bdbj/Dream-v0-Instruct-7B-SM
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Dream-v0-Instruct-7B-SM

This is the instruct model of Dream 7B with soft-masking (SM), which is an open diffusion large language model with top-tier performance. More details about the model and usage can be found in the blog and github bellow:

Blog: https://hkunlp.github.io/blog/2025/dream/
Github: https://github.com/HKUNLP/Dream

More information on SM will be linked in the future.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for bdbj/Dream-v0-Instruct-7B-SM

Quantizations

2 models