create space

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+import streamlit as st
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", flash_attn=True, flash_rotary=True, fused_dense=True, device_map="cuda", trust_remote_code=True)
+
+prompt = st.text_input("Input prompt", value="Write a detailed analogy between mathematics and a lighthouse.")
+length = st.number_input("Max token length", value=200)
+inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
+outputs = model.generate(**inputs, max_length=length)
+text = tokenizer.batch_decode(outputs)[0]
+st.write(text)