switch to tradeoff distance

app.py

@@ -1,5 +1,4 @@
 import os
-import math
 import gradio as gr
 import pandas as pd
 import plotly.express as px
@@ -33,10 +32,10 @@ COLUMNS_MAPPING = {
     "backend.torch_dtype": "Load Dtype 📥",
     "optimizations": "Optimizations 🛠️",
     #
-    "perf": "Open LLM-Perf Score ⬆️",
+    "tradeoff": "Open LLM Tradeoff ⬇️",
     #
-    "generate.throughput(tokens/s)": "Throughput (tokens/s) ⬆️",
     "score": "Open LLM Score ⬆️",
+    "generate.throughput(tokens/s)": "Throughput (tokens/s) ⬆️",
     "forward.peak_memory(MB)": "Peak Memory (MB) ⬇️",
     "num_params": "#️⃣ Parameters (M) 📏",
 }
@@ -47,13 +46,13 @@ COLUMNS_DATATYPES = [
     "str",
     #
     "number",
-    "number",
     #
     "number",
     "number",
     "number",
+    "number",
 ]
-SORTING_COLUMN = ["Open LLM-Perf Score ⬆️"]
+SORTING_COLUMN = ["Open LLM Tradeoff ⬇️"]
 
 
 llm_perf_dataset_repo = load_dataset_repo(LLM_PERF_DATASET_REPO, OPTIMUM_TOKEN)
@@ -74,8 +73,8 @@ def get_benchmark_df(benchmark="1xA100-80GB"):
     # create composite score
     score_distance = 100 - bench_df["score"]
     latency_distance = bench_df["generate.latency(s)"]
-    bench_df["perf"] = 100 / (1 + (score_distance**2 + latency_distance**2) ** 0.5)
-    bench_df["perf"] = bench_df["perf"].round(2)
+    bench_df["tradeoff"] = (score_distance**2 + latency_distance**2) ** 0.5
+    bench_df["tradeoff"] = bench_df["tradeoff"].round(2)
 
     # add optimizations
     bench_df["optimizations"] = bench_df[