warning about fp16

app.py

@@ -131,7 +131,7 @@ def main():
     st.write(WARNING_TEXT[language])
     show_pos_alpha = st.checkbox("Show positive alphas", value=False)
     if act_type == "softmax":
-        alpha = st.slider("α", min_value=-40, max_value=40 if show_pos_alpha else 0, step=1, value=0)
+        alpha = st.slider("α", min_value=-30, max_value=30 if show_pos_alpha else 0, step=1, value=0)
     else:
         alpha = st.slider("α", min_value=-5, max_value=5 if show_pos_alpha else 0, step=1, value=0)
     with st.expander("Advanced settings"):
@@ -155,10 +155,16 @@ def main():
         figure.update_layout(paper_bgcolor="#FFFFFF", plot_bgcolor='#FFFFFF', showlegend=False)
         st.plotly_chart(figure, use_container_width=True)
         st.subheader("What is it?")
-        st.write("""
-            Text generation with an external classifier requires a huge amount of computation. Therefore text generating with CAIF could be slow. To overcome this issue, we can apply reweighting not for every step. Our hypothesis is that we can run reweighting only if entropy of the next token is above certain threshold. This strategy will reduce the amont of computation. Note that if entropy threshold is too high, we don't get desired attribute in generated text
-        """)
+        st.write("Text generation with an external classifier requires a huge amount of computation. "
+                 "Therefore text generating with CAIF could be slow. "
+                 "To overcome this issue, we can apply reweighting not for every step. "
+                 "Our hypothesis is that we can run reweighting only "
+                 "if entropy of the next token is above certain threshold. "
+                 "This strategy will reduce the amount of computation. "
+                 "Note that if entropy threshold is too high, we don't get desired attribute in generated text")
         fp16 = st.checkbox("FP16", value=True)
+        st.write("FP16 computation is faster in comparison with full precision, "
+                 "but sometimes could yield Nones (especially with large alphas).")
     st.session_state["generated_text"] = None
     if "sst" in cls_model_name:
         prompt = st.text_input(TEXT_PROMPT_LABEL[language], "The movie")