Create app.py

app.py

@@ -0,0 +1,54 @@
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import gradio as gr
+
+torch.random.manual_seed(0)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/Phi-3-mini-128k-instruct", 
+    device_map="auto", 
+    torch_dtype="auto", 
+    trust_remote_code=True,
+    low_cpu_mem_usage=True,
+)
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-128k-instruct")
+
+messages = [
+    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
+    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
+]
+
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+
+generation_args = {
+    "max_new_tokens": 500,
+    "return_full_text": False,
+    "temperature": 0.2,
+    "do_sample": True,
+}
+def phi3_fun(message,chat_history):
+  messages=[
+    {"role": "user", "content": message},
+]
+  output = pipe(messages, **generation_args)
+  respond = output[0]['generated_text']
+  return respond
+
+title = "Phi-3 "
+examples = [
+    'How are You?',
+    "talk about your self",
+]
+gr.ChatInterface(
+    fn=phi3_fun,
+    title =title,
+    examples = examples
+).launch(debug=True)
+
+# output = pipe(messages, **generation_args)
+# print(output[0]['generated_text'])