Upload app.py

app.py

@@ -0,0 +1,37 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+model_name = "Azurro/APT-1B-Base"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+generator = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+def generate_text(prompt, max_length, temperature, top_k, top_p, beams):
+    output = generator(prompt, 
+                       max_length=max_length, 
+                       temperature=temperature, 
+                       top_k=top_k,
+                       do_sample=True,
+                       top_p=top_p,
+                       num_beams=beams)
+    return output[0]['generated_text']
+
+input_text = gr.inputs.Textbox(label="Input Text")
+max_length = gr.inputs.Slider(1, 200, step=1, default=100, label="Max Length")
+temperature = gr.inputs.Slider(0.1, 1.0, step=0.1, default=0.8, label="Temperature")
+top_k = gr.inputs.Slider(1, 200, step=1, default=10, label="Top K")
+top_p = gr.inputs.Slider(0.1, 2.0, step=0.1, default=0.95, label="Top P")
+beams = gr.inputs.Slider(1, 20, step=1, default=1, label="Beams")
+
+outputs = gr.outputs.Textbox(label="Generated Text")
+
+gr.Interface(generate_text, inputs=[input_text, max_length, temperature, top_k, top_p, beams], outputs=outputs).launch()