Upload Qwen_Qwen3-VL-32B-Instruct_1.py with huggingface_hub

Qwen_Qwen3-VL-32B-Instruct_1.py

@@ -11,10 +11,11 @@
 # ///
 
 try:
-    # Use a pipeline as a high-level helper
-    from transformers import pipeline
+    # Load model directly
+    from transformers import AutoProcessor, AutoModelForVision2Seq
     
-    pipe = pipeline("image-text-to-text", model="Qwen/Qwen3-VL-32B-Instruct")
+    processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-32B-Instruct")
+    model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen3-VL-32B-Instruct")
     messages = [
         {
             "role": "user",
@@ -24,7 +25,16 @@ try:
             ]
         },
     ]
-    pipe(text=messages)
+    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-32B-Instruct_1.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in Qwen_Qwen3-VL-32B-Instruct_1.txt')
 except Exception as e:
@@ -39,10 +49,11 @@ except Exception as e:
     with open('Qwen_Qwen3-VL-32B-Instruct_1.txt', 'a', encoding='utf-8') as f:
         import traceback
         f.write('''```CODE: 
-# Use a pipeline as a high-level helper
-from transformers import pipeline
+# Load model directly
+from transformers import AutoProcessor, AutoModelForVision2Seq
 
-pipe = pipeline("image-text-to-text", model="Qwen/Qwen3-VL-32B-Instruct")
+processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-32B-Instruct")
+model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen3-VL-32B-Instruct")
 messages = [
     {
         "role": "user",
@@ -52,7 +63,16 @@ messages = [
         ]
     },
 ]
-pipe(text=messages)
+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: