single header, split models list (#3)

app.py

@@ -1,25 +1,25 @@
 import gradio as gr
 
 from src.assets.text_content import TITLE, INTRODUCTION_TEXT
-from src.utils import get_data, compare_plots, filter_search
+from src.utils import compare_plots, filter_search, get_csv_data, split_models
 
 ############################ For Leaderboards #############################
-DATA_PATH = 'versions'
-latest_flag = True #Set flag to iclude latest data in Details and Versions Tab
-latest_df, latest_vname, previous_df, previous_vname = get_data(DATA_PATH, latest_flag)
+# Get CSV data
+global latest_df, all_dfs, all_vnames
+latest_df, all_dfs, all_vnames = get_csv_data()
 
 global prev_df
-prev_df = previous_df[0]
+prev_df = all_dfs[0]
 def select_prev_df(name):
-    ind = previous_vname.index(name)
-    prev_df = previous_df[ind]
+    ind = all_vnames.index(name)
+    prev_df = all_dfs[ind]
     return prev_df
 
 ############################ For Plots ####################################
-global plot_df, MODEL_COLS
+global plot_df, MODEL_COLS, OPEN_MODELS, COMM_MODELS
 plot_df = latest_df[0]
 MODEL_COLS = list(plot_df['Model'].unique())
-
+OPEN_MODELS, COMM_MODELS = split_models(MODEL_COLS)
 
 ############# MAIN APPLICATION ######################
 demo = gr.Blocks()
@@ -35,7 +35,7 @@ with demo:
                     show_label=False,
                     elem_id="search-bar",
                 )
-                        
+                                    
             leaderboard_table = gr.components.Dataframe(
                 value=latest_df[0],
                 elem_id="leaderboard-table",
@@ -57,16 +57,26 @@ with demo:
                 leaderboard_table,
                 queue=True
             )
+
         with gr.TabItem("📈 Plot", id=3):
             with gr.Row():
-                model_cols = gr.CheckboxGroup(
-                    MODEL_COLS, 
-                    label="Select Models 🤖", 
+                open_model_cols = gr.CheckboxGroup(
+                    OPEN_MODELS, 
+                    label="Select Models - Open Weight 🌐", 
                     value=[],
                     elem_id="column-select",
                     interactive=True,
                 )
 
+            with gr.Row():
+                comm_model_cols = gr.CheckboxGroup(
+                    COMM_MODELS, 
+                    label="Select Models - Closed Weight 💼", 
+                    value=[],
+                    elem_id="column-select-2",
+                    interactive=True,
+                )
+
             with gr.Row():
                 plot_grdf = gr.DataFrame(
                     value=plot_df,
@@ -76,9 +86,16 @@ with demo:
                 # Output block for the plot
                 plot_output = gr.Plot()
 
-            model_cols.change(
+            open_model_cols.change(
                 compare_plots,
-                [plot_grdf, model_cols],
+                [plot_grdf, open_model_cols, comm_model_cols],
+                plot_output,
+                queue=True
+            )
+
+            comm_model_cols.change(
+                compare_plots,
+                [plot_grdf, open_model_cols, comm_model_cols],
                 plot_output,
                 queue=True
             )
@@ -86,7 +103,7 @@ with demo:
         with gr.TabItem("🔄 Versions and Details", elem_id="details", id=2):
             with gr.Row():
                 ver_selection = gr.Dropdown(
-                    previous_vname, label="Select Version 🕹️", value=previous_vname[0]
+                    all_vnames, label="Select Version 🕹️", value=all_vnames[0]
                 )
             with gr.Row():
                 search_bar_prev = gr.Textbox(
@@ -122,7 +139,8 @@ with demo:
                 prev_table,
                 queue=True
             )
-
     demo.load()
+
 demo.queue()
+
 demo.launch()

src/assets/text_content.py

@@ -1,13 +1,14 @@
 TITLE = """<h1 align="center" id="space-title"> 🏆 CLEM Leaderboard</h1>"""
 
 INTRODUCTION_TEXT = """
+<h6 align="center">
 The CLEM Leaderboard aims to track, rank and evaluate current cLLMs (chat-optimized Large Language Models) with the suggested pronounciation “clems”. 
 
 The benchmarking approach is described in [Clembench: Using Game Play to Evaluate Chat-Optimized Language Models as Conversational Agents](https://arxiv.org/abs/2305.13455).
 
 Source code benchmarking "clems" is available here: [Clembench](https://github.com/clembench/clembench)
 
-All generated files and results from the benchmark runs are available here: [clembench-runs](https://github.com/clembench/clembench-runs)
+All generated files and results from the benchmark runs are available here: [clembench-runs](https://github.com/clembench/clembench-runs) </h6>
 """
 
 SHORT_NAMES = {

src/utils.py

@@ -2,46 +2,58 @@ import os
 import pandas as pd
 import matplotlib.pyplot as plt
 import numpy as np
-
 from src.assets.text_content import SHORT_NAMES
 
-def update_cols(df: pd.DataFrame) -> pd.DataFrame:
+# Set the folder name to save csv files
+global csvs_path
+csvs_path = 'versions'
+
+def get_csv_data():
     '''
-    Change three header rows to a single header row
+    Get data from csv files saved locally
     Args:
-        df: Raw dataframe containing 3 separate header rows
-            Remove this function if the dataframe has only one header row
-
-    Returns:
-        df: Updated dataframe which has only 1 header row instead of 3
+        None
+    Returns: 
+        latest_df: singular list containing dataframe of the latest version of the leaderboard with only 4 columns 
+        all_dfs: list of dataframes for previous versions + latest version including columns for all games
+        all_vnames: list of the names for the previous versions + latest version (For Details and Versions Tab Dropdown)
     '''
-    default_cols = list(df.columns)
-
-    # First 4 columns are initalised in 'update', Append additional columns for games Model, Clemscore, ALL(PLayed) and ALL(Main Score)
-    update = ['Model', 'Clemscore', 'Played', 'Quality Score']
-    game_metrics = default_cols[4:]
-
-    # Change columns Names for each Game
-    for i in range(len(game_metrics)):
-        if i%3 == 0:
-            game = game_metrics[i]
-            update.append(str(game).capitalize() + "(Played)")
-            update.append(str(game).capitalize() + "(Quality Score)") 
-            update.append(str(game).capitalize() + "(Quality Score[std])")
-
-    # Create a dict to change names of the columns
-    map_cols = {}
-    for i in range(len(default_cols)):
-        map_cols[default_cols[i]] = str(update[i])
-
-    df = df.rename(columns=map_cols)
-    df = df.iloc[2:]
-
-    return df
+    list_vers = os.listdir(csvs_path)
+    list_vers = [s.split('.csv')[0] for s in list_vers]
+    # Sort by latest version
+    float_content = [float(s[1:]) for s in list_vers]
+    float_content.sort(reverse=True)
+    list_vers = ['v'+str(s) for s in float_content]
+
+    DFS = []
+    for csv in list_vers:
+        read_path = os.path.join(csvs_path, csv + '.csv')
+        df = pd.read_csv(read_path)
+        df = process_df(df)
+        df = df.sort_values(by=list(df.columns)[1], ascending=False) # Sort by clemscore
+        DFS.append(df)
+
+    # Only keep relavant columns for the main leaderboard
+    latest_df_dummy = DFS[0]
+    all_columns = list(latest_df_dummy.columns)
+    keep_columns = all_columns[0:4]
+    latest_df_dummy = latest_df_dummy.drop(columns=[c for c in all_columns if c not in keep_columns])
+
+    latest_df = [latest_df_dummy]
+    all_dfs = []
+    all_vnames = []
+    for df, name in zip(DFS, list_vers):
+        all_dfs.append(df)
+        all_vnames.append(name) 
+
+    return latest_df, all_dfs, all_vnames
 
 def process_df(df: pd.DataFrame) -> pd.DataFrame:
     '''
-    Process dataframe - Remove repition in model names, convert datatypes to sort by "float" instead of "str"
+    Process dataframe 
+    - Remove repition in model names 
+    - Convert datatypes to sort by "float" instead of "str" for sorting
+    - Update column names
     Args:
         df: Unprocessed Dataframe (after using update_cols)
     Returns:
@@ -66,91 +78,77 @@ def process_df(df: pd.DataFrame) -> pd.DataFrame:
         else:
             models_list.append(splits[0] + "--" + splits[1])
     df[model_col_name] = models_list
+
+    # Update column names
+    update = ['Model', 'Clemscore', '% Played', 'Quality Score']
+    game_metrics = list_column_names[4:]
+
+    for col in game_metrics:
+        splits = col.split(',')
+        update.append(splits[0].capitalize() + "" + splits[1])
     
+    map_cols = {}
+    for i in range(len(update)):
+        map_cols[list_column_names[i]] = str(update[i])
+
+    df = df.rename(columns=map_cols)    
     return df
 
-def get_data(path: str, flag: bool):
+def filter_search(df: pd.DataFrame, query: str) -> pd.DataFrame:
     '''
-    Get a list of all version names and respective Dataframes 
-    Args: 
-        path: Path to the directory containing CSVs of different versions -> v0.9.csv, v1.0.csv, ....
-        flag: Set this flag to include the latest version in Details and Versions tab
-    Returns: 
-        latest_df: singular list containing dataframe of the latest version of the leaderboard with only 4 columns 
-        latest_vname: list of the name of latest version 
-        previous_df: list of dataframes for previous versions (can skip latest version if required) 
-        previous_vname: list of the names for the previous versions (INCLUDED IN Details and Versions Tab)
-
+    Filter the dataframe based on the search query
+    Args:
+        df: Unfiltered dataframe
+        query: a string of queries separated by ";"
+    Return:
+        filtered_df: Dataframe containing searched queries in the 'Model' column 
     '''
-    # Check if Directory is empty
-    list_versions = os.listdir(path)
-    if not list_versions:
-        print("Directory is empty")
+    queries = query.split(';')
+    list_cols = list(df.columns)
+    df_len = len(df)
+    filtered_models = []
+    models_list = list(df[list_cols[0]])
+    for q in queries:
+        q = q.lower()
+        q = q.strip()
+        for i in range(df_len):
+            model_name = models_list[i]
+            if q in model_name.lower():
+                filtered_models.append(model_name) # Append model names containing query q
 
-    else:
-        files = [file for file in list_versions if file.endswith('.csv')]
-        files.sort(reverse=True)
-        file_names = [os.path.splitext(file)[0] for file in files]
-
-        DFS = []
-        for file in files:
-            df = pd.read_csv(os.path.join(path, file))
-            df = update_cols(df) # Remove if by default there is only one header row
-            df = process_df(df) # Process Dataframe
-            df = df.sort_values(by=list(df.columns)[1], ascending=False) # Sort by clemscore
-            DFS.append(df)
-
-        # Only keep relavant columns for the main leaderboard
-        latest_df_dummy = DFS[0]
-        all_columns = list(latest_df_dummy.columns)
-        keep_columns = all_columns[0:4]
-        latest_df_dummy = latest_df_dummy.drop(columns=[c for c in all_columns if c not in keep_columns])
-
-        latest_df = [latest_df_dummy]
-        latest_vname = [file_names[0]]
-        previous_df = []
-        previous_vname = []
-        for df, name in zip(DFS, file_names):
-            previous_df.append(df)
-            previous_vname.append(name) 
-        
-        if not flag:
-            previous_df.pop(0)
-            previous_vname.pop(0)
-
-        return latest_df, latest_vname, previous_df, previous_vname
-    
-    return None
+    filtered_df = df[df[list_cols[0]].isin(filtered_models)]
 
+    if query == "":
+        return df
 
-# ['Model', 'Clemscore', 'All(Played)', 'All(Quality Score)']
-def compare_plots(df: pd.DataFrame, LIST: list):
+    return filtered_df
+
+###################################FOR PLOTS##################################################
+
+def plot_graph(df:pd.DataFrame, LIST:list):
     '''
-    Quality Score v/s % Played plot by selecting models
+    Takes in a list of models to plot
     Args:
-        LIST: The list of models to show in the plot, updated from frontend
+        df: A dummy dataframe of latest version
+        LIST: List of models to plot
     Returns:
-        fig: The plot
+        Fig: figure to plot
     '''
     short_names = label_map(LIST)
-
     list_columns = list(df.columns)
+    
     df = df[df[list_columns[0]].isin(LIST)]
 
     X = df[list_columns[2]]
     fig, ax = plt.subplots()
     for model in LIST:
         short = short_names[model]
-        # same_flag = short_names[model][1]
         model_df = df[df[list_columns[0]] == model]
         x = model_df[list_columns[2]]
         y = model_df[list_columns[3]]
         color = plt.cm.rainbow(x / max(X))  # Use a colormap for different colors
         plt.scatter(x, y, color=color)
-        # if same_flag:
         plt.annotate(f'{short}', (x, y), textcoords="offset points", xytext=(0, -15), ha='center', rotation=0)
-        # else:
-        #     plt.annotate(f'{short}', (x, y), textcoords="offset points", xytext=(20, -3), ha='center', rotation=0)
     ax.grid(which='both', color='grey', linewidth=1, linestyle='-', alpha=0.2)
     ax.set_xticks(np.arange(0,110,10))
     plt.xlim(-10, 110)
@@ -162,6 +160,23 @@ def compare_plots(df: pd.DataFrame, LIST: list):
 
     return fig
 
+
+# ['Model', 'Clemscore', 'All(Played)', 'All(Quality Score)']
+def compare_plots(df: pd.DataFrame, LIST1: list, LIST2: list):
+    '''
+    Quality Score v/s % Played plot by selecting models
+    Args:
+        df: A dummy dataframe of latest version
+        LIST1: The list of open source models to show in the plot, updated from frontend
+        LIST2: The list of commercial models to show in the plot, updated from frontend
+    Returns:
+        fig: The plot
+    '''
+    # Combine lists for Open source and commercial models
+    LIST = LIST1 + LIST2
+    fig = plot_graph(df, LIST)
+    return fig
+    
 def shorten_model_name(full_name):
     # Split the name into parts
     parts = full_name.split('-')
@@ -191,13 +206,6 @@ def label_map(model_list: list) -> dict:
     '''
     short_names = {}
     for model_name in model_list:
-        # splits = model_name.split('--')
-        # if len(splits) != 1:
-        #     splits[0] = SHORT_NAMES[splits[0] + '-']
-        #     splits[1] = SHORT_NAMES[splits[1] + '-']
-        #     # Define the short name and indicate there are two different models
-        #     short_names[model_name] = [splits[0] + '--' + splits[1], 0]
-        # else:
         if model_name in SHORT_NAMES:
             short_name = SHORT_NAMES[model_name]
         else:
@@ -207,32 +215,24 @@ def label_map(model_list: list) -> dict:
         short_names[model_name] = short_name
 
     return short_names
-
-def filter_search(df: pd.DataFrame, query: str) -> pd.DataFrame:
+    
+def split_models(MODEL_LIST: list):
     '''
-    Filter the dataframe based on the search query
-    Args:
-        df: Unfiltered dataframe
-        query: a string of queries separated by ";"
-    Return:
-        filtered_df: Dataframe containing searched queries in the 'Model' column 
+    Split the models into open source and commercial
     '''
-    queries = query.split(';')
-    list_cols = list(df.columns)
-    df_len = len(df)
-    filtered_models = []
-    models_list = list(df[list_cols[0]])
-    for q in queries:
-        q = q.lower()
-        for i in range(df_len):
-            model_name = models_list[i]
-            if q in model_name.lower():
-                filtered_models.append(model_name) # Append model names containing query q
+    open_models = []
+    comm_models = []
+
+    for model in MODEL_LIST:
+        if model.startswith(('gpt-', 'claude-', 'command')):
+            comm_models.append(model)
+        else:
+            open_models.append(model)
+
+    open_models.sort(key=lambda o: o.upper())
+    comm_models.sort(key=lambda c: c.upper())
+    return open_models, comm_models
+
 
-    filtered_df = df[df[list_cols[0]].isin(filtered_models)]
 
-    if query == "":
-        return df
 
-    return filtered_df
-    

versions/v0.9.csv

@@ -1,6 +1,4 @@
-,-,all,all,imagegame,imagegame,imagegame,privateshared,privateshared,privateshared,referencegame,referencegame,referencegame,taboo,taboo,taboo,wordle,wordle,wordle,wordle_withclue,wordle_withclue,wordle_withclue,wordle_withcritic,wordle_withcritic,wordle_withcritic
-,clemscore,Average % Played,Average Quality Score,% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std)
-model,,,,,,,,,,,,,,,,,,,,,,,,
+,"-, clemscore","all, Average % Played","all, Average Quality Score","imagegame, % Played","imagegame, Quality Score","imagegame, Quality Score (std)","privateshared, % Played","privateshared, Quality Score","privateshared, Quality Score (std)","referencegame, % Played","referencegame, Quality Score","referencegame, Quality Score (std)","taboo, % Played","taboo, Quality Score","taboo, Quality Score (std)","wordle, % Played","wordle, Quality Score","wordle, Quality Score (std)","wordle_withclue, % Played","wordle_withclue, Quality Score","wordle_withclue, Quality Score (std)","wordle_withcritic, % Played","wordle_withcritic, Quality Score","wordle_withcritic, Quality Score (std)"
 claude-v1.3-t0.0--claude-v1.3-t0.0,37.07,74.76,49.58,0.0,,,100.0,84.87,18.87,100.0,82.5,38.48,76.92,68.75,38.71,100.0,0.0,0.0,100.0,30.56,40.13,46.43,30.77,48.04
 falcon-40b-t0.0--falcon-40b-t0.0,0.71,0.95,75.0,0.0,,,0.0,,,0.0,,,0.0,,,0.0,,,3.33,50.0,,3.33,100.0,
 gpt-3.5-turbo-t0.0--gpt-3.5-turbo-t0.0,37.02,85.86,43.12,97.5,60.28,25.95,64.0,72.83,13.07,100.0,55.0,50.38,69.49,71.95,44.79,100.0,0.0,0.0,93.33,28.57,46.0,76.67,13.19,30.16
@@ -11,4 +9,4 @@ koala-13b-t0.0--koala-13b-t0.0,1.48,14.76,10.0,0.0,,,0.0,,,0.0,,,0.0,,,86.67,0.0
 luminous-supreme-t0.0--luminous-supreme-t0.0,0.0,16.24,0.0,0.0,,,0.0,,,0.0,,,0.0,,,100.0,0.0,0.0,3.33,0.0,,10.34,0.0,0.0
 oasst-12b-t0.0--oasst-12b-t0.0,1.74,20.85,8.33,0.0,,,0.0,,,15.0,33.33,51.64,0.0,,,100.0,0.0,0.0,16.67,0.0,0.0,14.29,0.0,0.0
 text-davinci-003-t0.0--text-davinci-003-t0.0,15.78,44.5,35.46,57.5,38.7,27.78,16.0,14.1,25.21,82.5,36.36,48.85,28.81,76.47,43.72,66.67,1.25,5.59,36.67,31.36,38.99,23.33,50.0,50.0
-vicuna-13b-t0.0--vicuna-13b-t0.0,4.24,13.58,31.25,0.0,,,0.0,,,0.0,,,5.08,100.0,0.0,56.67,0.0,0.0,13.33,25.0,50.0,20.0,0.0,0.0
+vicuna-13b-t0.0--vicuna-13b-t0.0,4.24,13.58,31.25,0.0,,,0.0,,,0.0,,,5.08,100.0,0.0,56.67,0.0,0.0,13.33,25.0,50.0,20.0,0.0,0.0

versions/v1.0.csv

@@ -1,9 +1,8 @@
-,-,all,all,imagegame,imagegame,imagegame,privateshared,privateshared,privateshared,referencegame,referencegame,referencegame,taboo,taboo,taboo,wordle,wordle,wordle,wordle_withclue,wordle_withclue,wordle_withclue,wordle_withcritic,wordle_withcritic,wordle_withcritic
-,clemscore,Average % Played,Average Quality Score,% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std),% Played,Quality Score,Quality Score (std)
-model,,,,,,,,,,,,,,,,,,,,,,,,
+,"-, clemscore","all, Average % Played","all, Average Quality Score","imagegame, % Played","imagegame, Quality Score","imagegame, Quality Score (std)","privateshared, % Played","privateshared, Quality Score","privateshared, Quality Score (std)","referencegame, % Played","referencegame, Quality Score","referencegame, Quality Score (std)","taboo, % Played","taboo, Quality Score","taboo, Quality Score (std)","wordle, % Played","wordle, Quality Score","wordle, Quality Score (std)","wordle_withclue, % Played","wordle_withclue, Quality Score","wordle_withclue, Quality Score (std)","wordle_withcritic, % Played","wordle_withcritic, Quality Score","wordle_withcritic, Quality Score (std)"
 CodeLlama-34b-Instruct-hf-t0.0--CodeLlama-34b-Instruct-hf-t0.0,10.34,23.96,43.15,7.5,37.67,36.83,0.0,,,15.0,83.33,40.82,74.58,29.55,44.87,53.33,0.0,0.0,13.33,8.33,16.66,4.0,100.0,
 Mistral-7B-Instruct-v0.1-t0.0--Mistral-7B-Instruct-v0.1-t0.0,2.72,17.5,15.56,,,,20.0,0.0,0.0,,,,50.85,46.67,49.01,0.0,,,16.67,0.0,0.0,0.0,,
 Wizard-Vicuna-13B-Uncensored-HF-t0.0--Wizard-Vicuna-13B-Uncensored-HF-t0.0,2.06,9.49,21.71,0.0,,,0.0,,,7.5,66.67,57.74,22.03,30.77,48.04,3.33,0.0,,30.0,11.11,33.33,3.57,0.0,
+WizardLM-13b-v1.2-t0.0--WizardLM-13b-v1.2-t0.0,7.82,40.49,19.31,0.0,,,26.0,21.37,20.34,100.0,35.0,48.3,47.46,51.79,48.08,33.33,0.0,0.0,43.33,7.69,27.74,33.33,0.0,0.0
 WizardLM-70b-v1.0-t0.0--WizardLM-70b-v1.0-t0.0,16.7,51.65,32.34,0.0,,,24.0,62.3,19.84,100.0,32.5,47.43,62.71,67.57,47.46,60.0,0.0,0.0,70.0,21.43,40.53,44.83,10.26,28.49
 claude-2-t0.0--claude-2-t0.0,33.71,82.12,41.05,0.0,,,100.0,75.89,18.57,100.0,45.0,50.38,91.53,73.15,39.71,100.0,0.0,0.0,90.0,21.6,36.56,93.33,30.65,43.22
 claude-2.1-t0.0--claude-2.1-t0.0,36.38,83.08,43.79,0.0,,,100.0,73.01,23.8,100.0,55.0,50.38,94.92,69.35,41.41,100.0,0.67,3.65,93.33,29.29,40.95,93.33,35.42,41.74
@@ -16,6 +15,7 @@ gpt-3.5-turbo-1106-t0.0--gpt-3.5-turbo-1106-t0.0,30.45,77.12,39.49,40.0,51.25,34
 gpt-4-0314-t0.0--gpt-4-0314-t0.0,58.81,93.79,62.7,65.0,88.92,16.24,100.0,91.12,7.48,100.0,77.5,42.29,91.53,79.63,32.48,100.0,2.78,7.37,100.0,50.78,41.69,100.0,48.17,41.42
 gpt-4-0613-t0.0--gpt-4-0613-t0.0,60.9,97.22,62.64,97.5,97.28,10.38,100.0,97.34,5.02,100.0,80.0,40.51,83.05,81.97,29.03,100.0,4.25,8.62,100.0,46.11,41.85,100.0,31.5,38.1
 gpt-4-1106-preview-t0.0--gpt-4-1106-preview-t0.0,60.33,97.95,61.59,97.5,94.15,14.85,100.0,83.25,12.51,100.0,90.0,30.38,88.14,83.97,29.88,100.0,7.5,13.01,100.0,49.11,42.93,100.0,23.17,37.74
+gpt4all-13b-snoozy-t0.0--gpt4all-13b-snoozy-t0.0,0.0,2.92,0.0,0.0,,,0.0,,,0.0,,,0.0,,,0.0,,,13.33,0.0,0.0,7.14,0.0,0.0
 koala-13B-HF-t0.0--koala-13B-HF-t0.0,1.25,23.22,5.38,0.0,,,0.0,,,0.0,,,52.54,16.13,37.39,100.0,0.0,0.0,10.0,0.0,0.0,0.0,,
 llama-2-13b-chat-hf-t0.0--llama-2-13b-chat-hf-t0.0,1.89,3.43,55.09,0.0,,,24.0,55.09,33.68,0.0,,,0.0,,,0.0,,,0.0,,,0.0,,
 llama-2-70b-chat-hf-t0.0--llama-2-70b-chat-hf-t0.0,1.39,3.79,36.74,0.0,,,14.0,13.48,11.98,12.5,60.0,54.77,0.0,,,0.0,,,0.0,,,0.0,,
@@ -23,4 +23,8 @@ llama-2-7b-chat-hf-t0.0--llama-2-7b-chat-hf-t0.0,0.24,6.05,4.0,0.0,,,0.0,,,0.0,,
 oasst-sft-4-pythia-12b-epoch-3.5-t0.0--oasst-sft-4-pythia-12b-epoch-3.5-t0.0,0.0,14.76,0.0,0.0,,,0.0,,,0.0,,,0.0,,,100.0,0.0,0.0,3.33,0.0,,0.0,,
 sheep-duck-llama-2-13b-t0.0--sheep-duck-llama-2-13b-t0.0,6.74,34.86,19.34,0.0,,,0.0,,,97.5,33.33,47.76,89.83,0.0,0.0,0.0,,,23.33,19.05,37.8,33.33,25.0,42.49
 sheep-duck-llama-2-70b-v1.1-t0.0--sheep-duck-llama-2-70b-v1.1-t0.0,17.12,40.82,41.93,40.0,23.19,28.06,0.0,,,35.0,57.14,51.36,59.32,74.29,44.34,34.78,0.0,0.0,63.33,43.86,43.82,53.33,53.12,49.9
-vicuna-33b-v1.3-t0.0--vicuna-33b-v1.3-t0.0,9.15,17.47,52.36,15.0,23.67,24.34,40.0,34.58,26.47,0.0,,,37.29,50.0,51.18,0.0,,,23.33,53.57,46.61,6.67,100.0,0.0
+vicuna-13b-v1.5-t0.0--vicuna-13b-v1.5-t0.0,7.21,34.74,20.74,0.0,,,24.0,0.0,0.0,80.0,28.12,45.68,49.15,37.93,49.38,26.67,0.0,0.0,36.67,30.3,45.84,26.67,28.12,45.19
+vicuna-33b-v1.3-t0.0--vicuna-33b-v1.3-t0.0,9.15,17.47,52.36,15.0,23.67,24.34,40.0,34.58,26.47,0.0,,,37.29,50.0,51.18,0.0,,,23.33,53.57,46.61,6.67,100.0,0.0
+vicuna-7b-v1.5-t0.0--vicuna-7b-v1.5-t0.0,3.46,12.86,26.91,0.0,,,4.0,0.0,0.0,0.0,,,62.71,24.32,43.5,0.0,,,13.33,50.0,57.74,10.0,33.33,57.74
+zephyr-7b-alpha-t0.0--zephyr-7b-alpha-t0.0,0.75,7.51,10.0,0.0,,,0.0,,,0.0,,,0.0,,,0.0,,,33.33,20.0,42.16,19.23,0.0,0.0
+zephyr-7b-beta-t0.0--zephyr-7b-beta-t0.0,1.23,3.95,31.25,0.0,,,0.0,,,0.0,,,0.0,,,0.0,,,13.33,25.0,50.0,14.29,37.5,47.87