Upload 2 files

app.py

@@ -0,0 +1,42 @@
+import streamlit as st
+import transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load the Phi 2 model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    "microsoft/phi-2",
+    trust_remote_code=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    # "kroonen/phi-2-GGUF",
+    "microsoft/phi-2",
+    device_map="auto",
+    trust_remote_code=True,
+    torch_dtype=torch.float32
+)
+
+# Streamlit UI
+st.title("Microsoft Phi 2 Streamlit App")
+
+# User input prompt
+prompt = st.text_area("Enter your prompt:", """Write a short summary about how to create a healthy lifestyle.""")
+
+# Generate output based on user input
+if st.button("Generate Output"):
+    with torch.no_grad():
+        token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt",  
+                                     return_attention_mask=False
+                                    )
+        output_ids = model.generate(
+            token_ids.to(model.device),
+            # max_new_tokens=512,
+            do_sample=True,
+            temperature=0.3,
+            max_length=200
+        )
+
+    output = tokenizer.decode(output_ids[0][token_ids.size(1):])
+    st.text("Generated Output:")
+    st.write(output)

requirements.txt

@@ -0,0 +1,6 @@
+transformers
+sentencepiece
+accelerate
+bitsandbytes
+einops
+streamlit