Upload 2 files

app.py

@@ -0,0 +1,56 @@
+import torch
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from peft import PeftModel
+
+# ✅ Model and Tokenizer Loading
+model_name = "microsoft/phi-2"
+#device_map = {"": 0}
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    low_cpu_mem_usage=True,
+    return_dict=True,
+    torch_dtype=torch.float16,
+    trust_remote_code=True,
+    device_map="auto",
+)
+
+# Load fine-tuned LoRA weights
+fine_tuned_model_path = "./phi2-qlora-adapter"
+model = PeftModel.from_pretrained(base_model, fine_tuned_model_path)
+model = model.merge_and_unload()  # Merge LoRA weights
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+# ✅ Set up text generation pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=500)
+
+
+# ✅ Chatbot Function
+def chat(user_input, history=[]):
+    """Generates a response from the fine-tuned Phi-2 model."""
+    prompt = f"\n\n### User:\n{user_input}\n\n### Assistant:\n"
+    response = generator(prompt, max_length=500, do_sample=True)
+    answer = response[0]["generated_text"].split("### Assistant:\n")[-1].strip()
+
+    # Append conversation history
+    history.append((user_input, answer))
+    return "", history
+
+
+# ✅ Create Gradio Chat Interface
+chatbot = gr.ChatInterface(
+    fn=chat,
+    title="Fine-Tuned Phi-2 Chat Assistant",
+    description="🚀 Chat with a fine-tuned Phi-2 model. Ask anything!",
+    theme="compact",
+)
+
+# ✅ Launch App
+if __name__ == "__main__":
+    chatbot.launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch
+transformers
+gradio
+peft
+accelerate