Upload Qwen_Qwen3-VL-2B-Instruct_0.py with huggingface_hub

Qwen_Qwen3-VL-2B-Instruct_0.py

@@ -11,7 +11,30 @@
 # ///
 
 try:
-    !pip install -U transformers
+    # Load model directly
+    from transformers import AutoProcessor, AutoModelForVision2Seq
+    
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-2B-Instruct")
+    model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen3-VL-2B-Instruct")
+    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('Qwen_Qwen3-VL-2B-Instruct_0.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in Qwen_Qwen3-VL-2B-Instruct_0.txt')
 except Exception as e:
@@ -26,7 +49,30 @@ except Exception as e:
     with open('Qwen_Qwen3-VL-2B-Instruct_0.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-!pip install -U transformers
+# Load model directly
+from transformers import AutoProcessor, AutoModelForVision2Seq
+
+processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-2B-Instruct")
+model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen3-VL-2B-Instruct")
+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]:]))
 ```
 
 ERROR: