parameters beta_expander

apps/mic.py

@@ -27,17 +27,18 @@ def app(state):
     st.write("\n")
     st.write(read_markdown("intro.md"))
 
-    with st.beta_expander("Generation Parameters"):
+    # st.sidebar.title("Generation Parameters")
+    max_length = 64
+
+    with st.sidebar.beta_expander('Generation Parameters'):
         do_sample = st.sidebar.checkbox("Sample", value=False, help="Sample from the model instead of using beam search.")
         top_k = st.sidebar.number_input("Top K", min_value=10, max_value=200, value=50, step=1, help="The number of highest probability vocabulary tokens to keep for top-k-filtering.")
         num_beams = st.sidebar.number_input(label="Number of Beams", min_value=2, max_value=10, value=4, step=1, help="Number of beams to be used in beam search.")
         temperature = st.sidebar.select_slider(label="Temperature", options = list(np.arange(0.0,1.1, step=0.1)), value=1.0, help ="The value used to module the next token probabilities.", format_func=lambda x: f"{x:.2f}")
         top_p = st.sidebar.select_slider(label = "Top-P", options = list(np.arange(0.0,1.1, step=0.1)),value=1.0, help="Nucleus Sampling : If set to float < 1, only the most probable tokens with probabilities that add up to :obj:`top_p` or higher are kept for generation.", format_func=lambda x: f"{x:.2f}")
+        if st.sidebar.button("Clear All Cache"):
+            caching.clear_cache()
 
-    if st.sidebar.button("Clear All Cache"):
-        caching.clear_cache()
-
-    max_length = 64
 
     @st.cache
     def load_model(ckpt):