Upload MiniMaxAI_MiniMax-M2_1.py with huggingface_hub

MiniMaxAI_MiniMax-M2_1.py

@@ -11,14 +11,24 @@
 # ///
 
 try:
-    # Use a pipeline as a high-level helper
-    from transformers import pipeline
+    # Load model directly
+    from transformers import AutoTokenizer, AutoModelForCausalLM
     
-    pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2")
+    tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-M2")
+    model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2")
     messages = [
         {"role": "user", "content": "Who are you?"},
     ]
-    pipe(messages)
+    inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
     with open('MiniMaxAI_MiniMax-M2_1.txt', 'w', encoding='utf-8') as f:
         f.write('Everything was good in MiniMaxAI_MiniMax-M2_1.txt')
 except Exception as e:
@@ -33,14 +43,24 @@ except Exception as e:
     with open('MiniMaxAI_MiniMax-M2_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 AutoTokenizer, AutoModelForCausalLM
 
-pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2")
+tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-M2")
+model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2")
 messages = [
     {"role": "user", "content": "Who are you?"},
 ]
-pipe(messages)
+inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
 ```
 
 ERROR: