asd

app.py

@@ -1,7 +1,35 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
 import gradio as gr
+import spaces
+# Load the model and tokenizer
+model_name = "NoaiGPT/merged-llama3-8b-instruct-1720894657"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Move model to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Define the prediction function
+@spaces.GPU
+def generate_text(prompt):
+    # Tokenize the input and move to GPU if available
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    # Generate text using the model
+    outputs = model.generate(inputs.input_ids, max_length=200, num_return_sequences=1)
+    # Decode the generated text
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
+    outputs="text",
+    title="LLaMA 3 Text Generation",
+    description="Generate text using the LLaMA 3 model fine-tuned for instruction-following tasks."
+)
+
+# Launch the interface
+interface.launch()