diff --git a/__init__.py b/__init__.py
index 1d6cdc6..a765251 100644
--- a/__init__.py
+++ b/__init__.py
@@ -60,6 +60,7 @@ node_list = [
     "audioldm2",
     "playmusic",
     "mcllava",
+    "moondream2",
 ]
 
 NODE_CLASS_MAPPINGS = {}
diff --git a/nodes/moondream2.py b/nodes/moondream2.py
new file mode 100644
index 0000000..0950c3e
--- /dev/null
+++ b/nodes/moondream2.py
@@ -0,0 +1,72 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+from pathlib import Path
+import torch
+from torchvision.transforms import ToPILImage
+from huggingface_hub import snapshot_download
+import folder_paths
+
+
+# Define the directory for saving files related to your new model
+files_for_moondream2 = Path(folder_paths.folder_names_and_paths["LLavacheckpoints"][0][0]) / "files_for_moondream2"
+files_for_moondream2.mkdir(parents=True, exist_ok=True)  # Ensure the directory exists
+
+class Moondream2Predictor:
+    def __init__(self):
+        self.model_path = snapshot_download("vikhyatk/moondream2", 
+                                            local_dir=files_for_moondream2,
+                                            force_download=False,  # Set to True if you always want to download, regardless of local copy
+                                            local_files_only=False,  # Set to False to allow downloading if not available locally
+                                            revision="2024-03-04",  # Specify the revision date for version control
+                                            local_dir_use_symlinks="auto",  # or set to True/False based on your symlink preference
+                                            ignore_patterns=["*.bin", "*.jpg", "*.png"])  # Customize based on need
+        self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
+        self.model = AutoModelForCausalLM.from_pretrained(self.model_path, trust_remote_code=True).to(self.device)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
+
+    def generate_predictions(self, image_path, question):
+        # Load and process the image
+        image_input = Image.open(image_path).convert("RGB")
+        enc_image = self.model.encode_image(image_input)
+
+        # Generate predictions
+        generated_text = self.model.answer_question(enc_image, question, self.tokenizer)
+
+        return generated_text
+
+class Moondream2model:
+    def __init__(self):
+        self.predictor = Moondream2Predictor()
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "text_input": (
+                    "STRING",
+                    {
+                        "multiline": True,
+                        "default": "",
+                    },
+                ),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+
+    FUNCTION = "moondream2_generate_predictions"
+
+    CATEGORY = "VLM Nodes/Moondream2"
+
+    def moondream2_generate_predictions(self, image, text_input):
+        # Convert tensor image to PIL Image
+        pil_image = ToPILImage()(image[0].permute(2, 0, 1))
+        temp_path = files_for_moondream2 / "temp_image.png"
+        pil_image.save(temp_path)      
+        
+        response = self.predictor.generate_predictions(temp_path, text_input)
+        return (response, )
+
+NODE_CLASS_MAPPINGS = {"Moondream2model": Moondream2model}
+NODE_DISPLAY_NAME_MAPPINGS = {"Moondream2model": "Moondream-2 Node"}