Update app.py

app.py

@@ -2,29 +2,29 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import gradio as gr
 
 # Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("AiCloser/Qwen2.5-32B-AGI")
+tokenizer = AutoTokenizer.from_pretrained("bartowski/Qwen2.5-Coder-32B-Instruct-abliterated-GGUF")
 model = AutoModelForCausalLM.from_pretrained(
-    "AiCloser/Qwen2.5-32B-AGI",
+    "bartowski/Qwen2.5-Coder-32B-Instruct-abliterated-GGUF",
     device_map="auto",
     torch_dtype="auto",
-    resume_download=True  # Allow resumable downloads
+    resume_download=True  # Enable resumable downloads
 )
 
-# Define text generation function
+# Define a function for generating text
 def generate_text(prompt):
     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
     outputs = model.generate(**inputs, max_length=200)
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Create Gradio interface
+# Create a Gradio interface
 interface = gr.Interface(
     fn=generate_text,
     inputs="text",
     outputs="text",
-    title="Qwen 2.5-32B Text Generator",
-    description="Generate text using the Qwen2.5-32B-AGI model. Enter a prompt below."
+    title="Qwen 2.5 Coder 32B Text Generator",
+    description="Enter a prompt to generate text using the Qwen2.5-Coder-32B-Instruct-abliterated-GGUF model."
 )
 
-# Launch interface
+# Launch the interface
 if __name__ == "__main__":
     interface.launch()