adding 2b to filters to account for gemma based models

app.py

@@ -154,10 +154,10 @@ with demo:
                                      value=["No Affiliation","Intel Innovator","Student Ambassador","Intel Liftoff", "Intel Engineering", "Other"])
                     
                 with gr.Column():
-                    filter_size = gr.CheckboxGroup(choices=[1,3,5,7,13,35,60,70,100],
+                    filter_size = gr.CheckboxGroup(choices=[1,2,3,5,7,13,35,60,70,100],
                                      label="Model Sizes (Billion of Parameters)",
                                      elem_id="parameter_size",
-                                     value=[1,3,5,7,13,35,60,70,100])
+                                     value=[1,2,3,5,7,13,35,60,70,100])
                     filter_precision = gr.CheckboxGroup(choices=["fp32","fp16","bf16","int8","fp8", "int4"],
                                      label="Model Precision",
                                      elem_id="precision",
@@ -178,7 +178,7 @@ with demo:
             initial_filtered_df = update_df(["Gaudi","Xeon","GPU Max","Arc GPU","Core Ultra"], 
                                 ["Intel Developer Cloud","AWS","Azure","Google Cloud Platform","Local"], 
                                 ["No Affiliation","Intel Innovator","Student Ambassador","Intel Liftoff", "Intel Engineering", "Other"], 
-                                [1,3,5,7,13,35,60,70,100], 
+                                [1,2,3,5,7,13,35,60,70,100], 
                                 ["fp32","fp16","bf16","int8","fp8", "int4"], 
                                 ["pretrained","fine-tuned","chat-models","merges/moerges"])