Create app.py

app.py

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+import torch
+import torch.nn as nn
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config
+from rotary_embedding_torch import RotaryEmbedding
+import gradio as gr
+import spaces
+# Define the max length used during training
+max_length = 8192
+
+# Load the model and tokenizer from Hugging Face Hub
+model_name = "archit11/gpt2final"
+config = GPT2Config.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+# Add rotary embeddings
+rotary_emb = RotaryEmbedding(
+    dim=32,
+    interpolate_factor=4.0,
+)
+for layer in model.transformer.h:
+    layer.attn.rotary_emb = rotary_emb
+
+# Set the model to evaluation mode
+model.eval()
+
+# Define the inference function
+@spaces.GPU(duration=120)
+def generate_text(prompt, max_length=100, temperature=0.7, top_p=0.9):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt")
+    
+    # Generate text
+    with torch.no_grad():
+        output = model.generate(
+            input_ids,
+            max_length=max_length,
+            temperature=temperature,
+            top_p=top_p,
+            num_return_sequences=1,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(lines=5, label="Prompt"),
+        gr.Slider(minimum=10, maximum=1000, value=100, step=10, label="Max Length"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top-p")
+    ],
+    outputs=gr.Textbox(lines=10, label="Generated Text"),
+    title="Custom GPT-2 Text Generation",
+    description="Enter a prompt to generate text using the custom-trained GPT-2 model."
+)
+
+# Launch the interface
+iface.launch()