Create app.py

app.py

@@ -0,0 +1,49 @@
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import gradio as gr
+
+# Base model and adapter repo
+BASE_MODEL_NAME = "microsoft/phi-2"
+ADAPTER_REPO = "Shriti09/Microsoft-Phi-QLora" 
+
+# Load the tokenizer
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_NAME)
+tokenizer.pad_token = tokenizer.eos_token
+
+# Load the base model
+print("Loading base model...")
+base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL_NAME, device_map="auto")
+
+# Load adapter weights
+print("Loading LoRA adapter...")
+model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
+
+# Merge adapter into base model (optional, makes inference simpler)
+model = model.merge_and_unload()
+
+# Put model in eval mode
+model.eval()
+
+# Function to generate response from prompt
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_length=256,
+        do_sample=True,
+        top_p=0.95,
+        temperature=0.7
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Gradio UI
+gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=2, placeholder="Ask me something..."),
+    outputs="text",
+    title="Phi-2 QLoRA Chatbot",
+    description="Chat with Phi-2 fine-tuned with QLoRA adapters!"
+).launch()