Upload MiniMaxAI_MiniMax-M2_1.py with huggingface_hub

MiniMaxAI_MiniMax-M2_1.py

@@ -1,9 +1,14 @@
 # /// script
 # requires-python = ">=3.12"
 # dependencies = [
+#     "numpy",
+#     "einops",
+#     "pandas",
+#     "protobuf",
 #     "torch",
 #     "torchvision",
 #     "transformers",
+#     "timm",
 #     "diffusers",
 #     "sentence-transformers",
 #     "accelerate",
@@ -14,23 +19,8 @@
 
 try:
     # Load model directly
-    from transformers import AutoTokenizer, AutoModelForCausalLM
-    
-    tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-M2")
-    model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2")
-    messages = [
-        {"role": "user", "content": "Who are you?"},
-    ]
-    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]:]))
+    from transformers import AutoModelForCausalLM
+    model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2", trust_remote_code=True, torch_dtype="auto")
     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:
@@ -46,23 +36,8 @@ except Exception as e:
         import traceback
         f.write('''```CODE: 
 # Load model directly
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/MiniMax-M2")
-model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2")
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
-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]:]))
+from transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2", trust_remote_code=True, torch_dtype="auto")
 ```
 
 ERROR: