Upload LiquidAI_LFM2-VL-3B_1.py with huggingface_hub

LiquidAI_LFM2-VL-3B_1.py

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+# /// script
+# requires-python = ">=3.12"
+# dependencies = [
+#     "torch",
+#     "torchvision",
+#     "transformers",
+#     "accelerate",
+#     "peft",
+# ]
+# ///
+
+try:
+    # Load model directly
+    from transformers import AutoProcessor, AutoModelForImageTextToText
+    
+    processor = AutoProcessor.from_pretrained("LiquidAI/LFM2-VL-3B")
+    model = AutoModelForImageTextToText.from_pretrained("LiquidAI/LFM2-VL-3B")
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
+                {"type": "text", "text": "What animal is on the candy?"}
+            ]
+        },
+    ]
+    inputs = processor.apply_chat_template(
+    	messages,
+    	add_generation_prompt=True,
+    	tokenize=True,
+    	return_dict=True,
+    	return_tensors="pt",
+    ).to(model.device)
+    
+    outputs = model.generate(**inputs, max_new_tokens=40)
+    print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
+    with open('LiquidAI_LFM2-VL-3B_1.txt', 'w', encoding='utf-8') as f:
+        f.write('Everything was good in LiquidAI_LFM2-VL-3B_1.txt')
+except Exception as e:
+    with open('LiquidAI_LFM2-VL-3B_1.txt', 'w', encoding='utf-8') as f:
+        import traceback
+        traceback.print_exc(file=f)
+finally:
+    from huggingface_hub import upload_file
+    upload_file(
+        path_or_fileobj='LiquidAI_LFM2-VL-3B_1.txt',
+        repo_id='model-metadata/code_execution_files',
+        path_in_repo='LiquidAI_LFM2-VL-3B_1.txt',
+        repo_type='dataset',
+    )