Create app.py

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+# Directory where your fine-tuned Phi-2 model and associated files are stored.
+# This directory should include files like:
+#   - adapter_config.json, adapter_model.safetensors,
+#   - tokenizer_config.json, tokenizer.json, merges.txt,
+#   - special_tokens_map.json, vocab.json, added_tokens.json, etc.
+model_dir = "./phi2-finetune"
+
+# Load the tokenizer.
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+
+# Load the base model. (Assumes the base model files are in model_dir.)
+base_model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto")
+
+# Load the adapter (PEFT) weights.
+model = PeftModel.from_pretrained(base_model, model_dir)
+
+def generate_response(prompt, max_new_tokens=200, temperature=0.7):
+    """
+    Generate a response from the fine-tuned Phi-2 model given a prompt.
+    """
+    # Tokenize the prompt and move tensors to the model's device.
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    # Generate output text using sampling.
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=temperature
+    )
+    
+    # Decode the generated tokens and return the response.
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Create a Gradio interface with example prompts.
+demo = gr.Interface(
+    fn=generate_response,
+    inputs=[
+        gr.Textbox(lines=4, label="Input Prompt"),
+        gr.Slider(50, 500, value=200, label="Max New Tokens"),
+        gr.Slider(0.0, 1.0, value=0.7, label="Temperature")
+    ],
+    outputs=gr.Textbox(label="Response"),
+    title="Phi-2 Fine-tuned Chat",
+    description="A Hugging Face Space app serving the fine-tuned Phi-2 model trained on OpenAssistant/oasst1 data.",
+    examples=[
+        ["Hello, how are you today?", 150, 0.7],
+        ["Translate this sentence from English to French: I love programming.", 200, 0.8],
+        ["Tell me a joke about artificial intelligence.", 180, 0.6]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()