Upload app.py

app.py

@@ -0,0 +1,35 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, logging
+import gradio as gr
+
+
+model_name = "microsoft/phi-2"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True
+)
+model.config.use_cache = False
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Loading adapter (trained LORA weights)
+ckpt = '/content/drive/MyDrive/S27/results/checkpoint-500'
+model.load_adapter(ckpt)
+# adapter_path = 'checkpoint-500'
+# model.load_adapter(adapter_path)
+
+def inference(prompt):
+
+  pipe = pipeline(task="text-generation",model=model,tokenizer=tokenizer,max_length=200)
+  result = pipe(f"<s>[INST] {prompt} [/INST]")
+  return result[0]['generated_text']
+    
+with gr.Blocks() as demo:
+    prompt = gr.Textbox(label="Prompt")
+    output = gr.Textbox(label="Output Box")
+    greet_btn = gr.Button("Generate")
+    greet_btn.click(fn=inference, inputs=prompt, outputs=output, api_name="inference")
+
+demo.launch()