Instructions to use dill-dev/NanoDream3-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dill-dev/NanoDream3-preview with Transformers:

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

pipe = pipeline("image-text-to-text", model="dill-dev/NanoDream3-preview", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("dill-dev/NanoDream3-preview", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dill-dev/NanoDream3-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dill-dev/NanoDream3-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dill-dev/NanoDream3-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dill-dev/NanoDream3-preview

SGLang

How to use dill-dev/NanoDream3-preview 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 "dill-dev/NanoDream3-preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dill-dev/NanoDream3-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "dill-dev/NanoDream3-preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dill-dev/NanoDream3-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dill-dev/NanoDream3-preview with Docker Model Runner:
```
docker model run hf.co/dill-dev/NanoDream3-preview
```

NanoDream3-preview / structured_outputs.png

dill-dev

Initial upload of NanoDream2-2B

e914f21 verified 2 months ago

download

history blame contribute delete

1.87 MB

Xet Pointer Details

( Raw pointer file )

Xet hash:: 936ec8958d75a1696db8cbd3a5be8e9804cf60f6c3b741862763188cc95314d9
Size of remote file:: 1.87 MB
SHA256:: 655bc4f2897175ca479b40308c3a0ae8e368b943b92bfbe73075821906a83b0f

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.