Instructions to use moondream/moondream3-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moondream/moondream3-preview with Transformers:

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

pipe = pipeline("image-text-to-text", model="moondream/moondream3-preview", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("moondream/moondream3-preview", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use moondream/moondream3-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moondream/moondream3-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "moondream/moondream3-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/moondream/moondream3-preview

SGLang

How to use moondream/moondream3-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 "moondream/moondream3-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": "moondream/moondream3-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 "moondream/moondream3-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": "moondream/moondream3-preview",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use moondream/moondream3-preview with Docker Model Runner:
```
docker model run hf.co/moondream/moondream3-preview
```

vikhyatk commited on Sep 23, 2025

Commit

b976bef

verified ·

1 Parent(s): b80ac52

Upload HfMoondream

Browse files

Files changed (2) hide show

config.json +9 -2
hf_moondream.py +2 -2

config.json CHANGED Viewed

@@ -6,8 +6,15 @@
     "AutoConfig": "hf_moondream.HfConfig",
     "AutoModelForCausalLM": "hf_moondream.HfMoondream"
   },
-  "config": {},
-  "model_type": "moondream1",
   "torch_dtype": "bfloat16",
   "transformers_version": "4.51.1"
 }

     "AutoConfig": "hf_moondream.HfConfig",
     "AutoModelForCausalLM": "hf_moondream.HfMoondream"
   },
+  "config": {
+    "skills": [
+      "query",
+      "caption",
+      "detect",
+      "point"
+    ]
+  },
+  "model_type": "moondream3",
   "torch_dtype": "bfloat16",
   "transformers_version": "4.51.1"
 }

hf_moondream.py CHANGED Viewed

@@ -27,11 +27,11 @@ def extract_question(text):
 class HfConfig(PretrainedConfig):
     _auto_class = "AutoConfig"
-    model_type = "moondream1"
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
-        self.config = {}
 class HfMoondream(PreTrainedModel):

 class HfConfig(PretrainedConfig):
     _auto_class = "AutoConfig"
+    model_type = "moondream3"
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
+        self.config = {"skills": ["query", "caption", "detect", "point"]}
 class HfMoondream(PreTrainedModel):