Update app.py

app.py

@@ -1,3 +1,46 @@
-# Load model directly
-from transformers import AutoModel
-model = AutoModel.from_pretrained("anthracite-org/magnum-v4-12b-gguf")
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer - specify the correct model class
+tokenizer = AutoTokenizer.from_pretrained("anthracite-org/magnum-v4-12b-gguf")
+model = AutoModelForCausalLM.from_pretrained("anthracite-org/magnum-v4-12b-gguf", torch_dtype=torch.float16, device_map="auto")  # Adjust dtype and device_map as needed
+
+
+def generate_text(prompt, max_length=50, num_return_sequences=1, temperature=0.7, top_p=0.95, top_k=50):
+    try:
+        inputs = tokenizer(prompt, return_tensors="pt").to(model.device) # Ensure inputs are on the same device as the model
+
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_length,  # Use max_new_tokens instead of max_length with Transformers >= 4.30
+            do_sample=True,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            num_return_sequences=num_return_sequences
+        )
+
+        generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        return "\n\n".join(generated_text)
+
+    except Exception as e:  # Handle potential errors
+        return f"Error: {e}"
+
+
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter a prompt...", label="Prompt"),
+        gr.Slider(50, 200, value=100, label="Max Length"),
+        gr.Slider(0, 1, value=0.7, label="Temperature"),
+        gr.Slider(0, 1, value=0.95, label="Top p"),
+        gr.Slider(0, 50, value=50, step=1, label="Top k"),
+
+    ],
+    outputs="text",
+    title="Text Generation with magnum-v4-12b-gguf",
+    description="Generate text using the magnum model. Please be patient, as generation can take time."
+)
+
+iface.launch(share=True) # Consider adding share=True for others to try