Create app.py

app.py

@@ -0,0 +1,71 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_ID = "AlexKitipov/Phi-3-mini-128k-instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+    device_map="auto"
+)
+
+SYSTEM_PROMPT = "You are a helpful AI assistant."
+
+def build_prompt(history, user_message):
+    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
+    for user, assistant in history:
+        if user:
+            messages.append({"role": "user", "content": user})
+        if assistant:
+            messages.append({"role": "assistant", "content": assistant})
+    messages.append({"role": "user", "content": user_message})
+
+    if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template:
+        return tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True
+        )
+
+    # fallback formatting
+    prompt = SYSTEM_PROMPT + "\n"
+    for m in messages:
+        role = m["role"].upper()
+        prompt += f"{role}: {m['content']}\n"
+    prompt += "ASSISTANT:"
+    return prompt
+
+
+def chat_fn(message, history):
+    prompt = build_prompt(history, message)
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    generated = tokenizer.decode(
+        output[0][inputs["input_ids"].shape[-1]:],
+        skip_special_tokens=True
+    )
+
+    return generated
+
+
+demo = gr.ChatInterface(
+    fn=chat_fn,
+    title="Phi-3-mini-128k Chat",
+    description="Chat with the Phi-3-mini-128k-instruct model."
+)
+
+if __name__ == "__main__":
+    demo.launch()