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app.py

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+import spaces
+import gradio as gr
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+
+title = """S&T ORA"""
+
+model_path = "bigcode/starcoder2-15b"
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+model = AutoModelForCausalLM.from_pretrained( model_path)
+
+@spaces.GPU
+def generate_text(prompt, temperature=0.9, max_length=1200):
+    # Encode the inputs
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    attention_mask = torch.ones(inputs.shape, dtype=torch.long)
+    inputs = inputs.to("cuda")
+    attention_mask = attention_mask.to("cuda")    
+    outputs = model.generate(
+        inputs, 
+        attention_mask=attention_mask,
+        max_length=max_length, 
+        top_p=0.9, 
+        temperature=temperature, 
+        do_sample=True, 
+        pad_token_id=tokenizer.eos_token_id
+    )
+    return tokenizer.decode(outputs[0])
+
+def gradio_app():
+    with gr.Blocks() as demo:
+        gr.Markdown(title)
+        prompt = gr.Code(label="Enter your code prompt", value="def print_hello_world():")
+        with gr.Row():
+            temperature = gr.Slider(minimum=0.1, maximum=1.0, step=0.1, value=0.5, label="Temperature")
+            max_length = gr.Slider(minimum=100, maximum=1024, step=10, value=100, label="Generate Length")
+        generate_btn = gr.Button("Try✨StarCoder")
+        output = gr.Code(label="✨StarCoder:", lines=40)
+
+        generate_btn.click(
+            fn=generate_text,
+            inputs=[prompt, temperature, max_length],
+            outputs=output
+        )
+
+    demo.launch()
+
+if __name__ == "__main__":
+    gradio_app()

requirements.txt

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+torch
+git+https://github.com/huggingface/transformers.git