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piotr-szleg-bards-ai
commited on
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·
f829331
1
Parent(s):
2980f51
2024-02-14 10:47:29 Publish script update
Browse files- app.py +92 -30
- data/2024-02-13 12:59:41.960562_time_of_day_comparison.csv +97 -0
- data/2024-02-14 10:19:15.390578_time_of_day_comparison.csv +101 -0
- data/general_plots.csv +0 -0
- data/output_plots.csv +46 -4
- data/time_of_day_plots.csv +0 -0
- pipeline/models.py +325 -6
app.py
CHANGED
@@ -3,6 +3,10 @@ import re
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import gradio as gr
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import pandas as pd
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import plotly
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from pandas.api.types import is_numeric_dtype
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from pipeline.config import LLMBoardConfig, QueriesConfig
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@@ -61,15 +65,15 @@ model_costs_df = pd.read_csv("data/2024-02-05 12:03:45.281624_model_costs.csv")
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time_of_day_plots = pd.read_csv("data/time_of_day_plots.csv")
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output_plots = pd.read_csv("data/output_plots.csv")
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collapse_languages = False
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collapse_output_method = False
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def filter_dataframes(input: str):
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global
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input = input.lower()
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return dataframes()
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def dataframes():
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global collapse_languages, collapse_output_method,
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summary_df_columns = summary_df.columns.to_list()
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group_columns = LLMBoardConfig().group_columns.copy()
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group_columns.remove("template_name")
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summary_df_processed = summary_df[summary_df_columns].groupby(by=group_columns).mean().reset_index()
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return (
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),
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dataframe_style(model_costs_df[model_costs_df.model.str.lower().str.contains(searched_model_name)]),
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)
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@@ -138,11 +153,58 @@ def snake_case_to_title(text):
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return " ".join(title_words)
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filter_textbox = gr.Textbox(label="Model name
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filter_button = gr.Button("Filter
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collapse_languages_button = gr.Button("Collapse languages")
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collapse_output_method_button = gr.Button("Collapse output method")
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last_textbox = 0
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with gr.Blocks() as demo:
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gr.HTML("<h1>LLM Board</h1>")
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@@ -150,6 +212,9 @@ with gr.Blocks() as demo:
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with gr.Row():
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filter_textbox.render()
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filter_button.render()
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with gr.Tab("About this project"):
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gr.Markdown(
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@@ -158,12 +223,7 @@ with gr.Blocks() as demo:
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)
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)
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with gr.Tab("General plots"):
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plot = plotly.io.from_json(row["plot_json"])
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plot.update_layout(autosize=True)
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gr.Plot(plot, label=row["header"], scale=1)
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if pd.notna(row["description"]):
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gr.Markdown(str(row["description"]))
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with gr.Tab("Output characteristics"):
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with gr.Row():
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collapse_languages_button.render()
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@@ -179,17 +239,10 @@ To count words we split the output string by whitespace `\w` regex character.
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Chunk sizes are measured in the characters count."""
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)
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plot = plotly.io.from_json(row["plot_json"])
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plot.update_layout(autosize=True)
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gr.Plot(plot, label=row["header"], scale=1)
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with gr.Tab("Performance by time of the day"):
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# display only first plot for all models
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plot = plotly.io.from_json(row["plot_json"])
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plot.update_layout(autosize=True)
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gr.Plot(plot, label=row["header"], scale=1)
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time_periods_explanation_ui = gr.DataFrame(
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dataframe_style(time_periods_explanation_df), label="Times of day ranges"
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)
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@@ -206,10 +259,7 @@ Measurements were made during a normal work week.
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"""
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)
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# display rest of the plots
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plot = plotly.io.from_json(row["plot_json"])
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plot.update_layout(autosize=True)
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gr.Plot(plot, label=row["header"], scale=1)
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with gr.Tab("Costs comparison"):
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models_costs_ui = gr.DataFrame(dataframe_style(model_costs_df), label="Costs comparison")
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outputs=[summary_ui, time_of_day_comparison_ui, models_costs_ui],
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api_name="filter_dataframes",
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)
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collapse_languages_button.click(
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fn=collapse_languages_toggle,
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outputs=[summary_ui, collapse_languages_button],
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import gradio as gr
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import pandas as pd
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import plotly
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import io
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import plotly.express as px
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import json
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from pandas.api.types import is_numeric_dtype
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from pipeline.config import LLMBoardConfig, QueriesConfig
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time_of_day_plots = pd.read_csv("data/time_of_day_plots.csv")
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output_plots = pd.read_csv("data/output_plots.csv")
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searched_query = ""
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collapse_languages = False
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collapse_output_method = False
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def filter_dataframes(input: str):
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global searched_query
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input = input.lower()
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searched_query = input
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return dataframes()
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def dataframes():
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global collapse_languages, collapse_output_method, searched_query, summary_df, time_of_day_comparison_df, model_costs_df
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summary_df_columns = summary_df.columns.to_list()
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group_columns = LLMBoardConfig().group_columns.copy()
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group_columns.remove("template_name")
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summary_df_processed = summary_df[summary_df_columns].groupby(by=group_columns).mean().reset_index()
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searched_model_names = searched_query.split("|")
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searched_model_names = [n.lower().strip() for n in searched_model_names]
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searched_model_names = [n for n in searched_model_names if n]
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def for_dataframe(df):
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if not searched_model_names:
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return df
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return dataframe_style(pd.concat(
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df[df.model.str.lower().str.contains(n)]
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for n in searched_model_names
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))
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return (
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for_dataframe(summary_df_processed),
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for_dataframe(time_of_day_comparison_df),
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for_dataframe(model_costs_df)
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)
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return " ".join(title_words)
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filter_textbox = gr.Textbox(label="Model name parts *", scale=2)
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filter_button = gr.Button("Filter", scale=1)
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collapse_languages_button = gr.Button("Collapse languages")
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collapse_output_method_button = gr.Button("Collapse output method")
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last_textbox = 0
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plots = []
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single_model_plots = []
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def filter_plots(searched_query:str):
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searched_model_names = searched_query.split("|")
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searched_model_names = [n.lower().strip() for n in searched_model_names]
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searched_model_names = [n for n in searched_model_names if n]
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def filter_dataframe(df):
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if not searched_model_names:
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return df
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return pd.concat(
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df[df.model.str.lower().str.contains(n)]
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for n in searched_model_names
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)
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results = []
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for plot_display, plot, row in plots:
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visible = True
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if "df" in row and pd.notna(row["df"]):
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buffer = io.StringIO(row["df"])
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df = pd.read_csv(buffer)
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df = filter_dataframe(df)
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plot = px.bar(
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df,
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**json.loads(row["arguments"])
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)
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plot.update_layout(autosize=True)
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elif "for model" in row["header"] and searched_model_names:
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plot_model = row["header"].split("for model")[1].lower()
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if not any(n in plot_model for n in searched_model_names):
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visible = False
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results.append(gr.Plot(plot, visible=visible))
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return results
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def display_plot(plot_df_row):
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row = dict(plot_df_row)
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plot = plotly.io.from_json(row["plot_json"])
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plot.update_layout(autosize=True)
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plots.append((
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gr.Plot(plot, label=row["header"], scale=1),
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plot,
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row))
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if "description" in row and pd.notna(row["description"]):
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gr.Markdown(str(row["description"]))
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with gr.Blocks() as demo:
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gr.HTML("<h1>LLM Board</h1>")
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with gr.Row():
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filter_textbox.render()
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filter_button.render()
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gr.Markdown(
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"\* You can use `|` operator to display multiple models at once, for example \"gpt|mistral|zephyr\""
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)
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with gr.Tab("About this project"):
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gr.Markdown(
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)
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)
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with gr.Tab("General plots"):
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general_plots.apply(display_plot, axis=1)
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with gr.Tab("Output characteristics"):
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with gr.Row():
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collapse_languages_button.render()
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Chunk sizes are measured in the characters count."""
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)
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output_plots.apply(display_plot, axis=1)
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with gr.Tab("Performance by time of the day"):
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# display only first plot for all models
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time_of_day_plots[0:1].apply(display_plot, axis=1)
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time_periods_explanation_ui = gr.DataFrame(
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dataframe_style(time_periods_explanation_df), label="Times of day ranges"
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)
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"""
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)
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# display rest of the plots
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time_of_day_plots[1:].apply(display_plot, axis=1)
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with gr.Tab("Costs comparison"):
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models_costs_ui = gr.DataFrame(dataframe_style(model_costs_df), label="Costs comparison")
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outputs=[summary_ui, time_of_day_comparison_ui, models_costs_ui],
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api_name="filter_dataframes",
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)
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filter_button.click(
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fn=filter_plots,
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inputs=filter_textbox,
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outputs=[v[0] for v in plots],
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api_name="filter_plots",
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)
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filter_textbox.submit(
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fn=filter_plots,
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inputs=filter_textbox,
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outputs=[v[0] for v in plots],
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api_name="filter_plots",
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)
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collapse_languages_button.click(
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fn=collapse_languages_toggle,
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outputs=[summary_ui, collapse_languages_button],
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data/2024-02-13 12:59:41.960562_time_of_day_comparison.csv
ADDED
@@ -0,0 +1,97 @@
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model,time_of_day,execution_time,characters_count,words_count
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Chronos Hermes (13B),night,4.168874144554138,360.0,58.5
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Falcon Instruct (7B),night,1.0781378746032715,42.0,7.5
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LLaMA-2 Chat (7B),night,1.686687707901001,377.5,61.5
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Mistral (7B) Instruct v0.2 (Together AI),night,1.7709604501724243,311.0,48.0
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Mistral-7B-Instruct-v0.2,morning,3.734026002883911,362.9,58.35
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Mistral-7B-Instruct-v0.2,afternoon,3.228973722457886,222.0625,32.25
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Mistral-7B-Instruct-v0.2,late afternoon,3.2048643112182615,219.8625,31.8
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Mistral-7B-Instruct-v0.2,evening,3.397640073299408,261.18333333333334,40.1
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Mistral-7B-Instruct-v0.2,late evening,3.389284573495388,175.79375,25.68125
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Mistral-7B-Instruct-v0.2,midnight,1.9149879813194275,37.8,2.95
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Mixtral-8x7B-Instruct-v0.1,early morning,4.526968242530536,285.045,41.86
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Mixtral-8x7B-Instruct-v0.1,morning,3.9661054956285575,304.82,47.28
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Mixtral-8x7B-Instruct-v0.1,afternoon,5.362903979589355,369.3192307692308,54.353846153846156
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Mixtral-8x7B-Instruct-v0.1,late afternoon,5.80184749175942,347.9681818181818,47.27272727272727
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Mixtral-8x7B-Instruct-v0.1,evening,3.6435119574237023,326.69,48.545
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Mixtral-8x7B-Instruct-v0.1,late evening,5.62397656769588,395.15714285714284,49.02857142857143
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Mixtral-8x7B-Instruct-v0.1,midnight,4.639010797279158,323.0394736842105,42.69210526315789
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Mixtral-8x7B-Instruct-v0.1,night,4.009439338194697,301.24545454545455,42.21818181818182
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OpenHermes-2.5-Mistral (7B),night,3.330963373184204,179.5,28.0
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RedPajama-INCITE Chat (7B),night,1.3475391864776611,34.5,5.0
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Snorkel Mistral PairRM DPO (7B),night,4.399647831916809,404.5,60.5
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TinyLlama/TinyLlama-1.1B-Chat-v1.0,early morning,2.0273348593711855,372.13,62.53
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TinyLlama/TinyLlama-1.1B-Chat-v1.0,morning,1.9041210174560548,372.05,62.6
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TinyLlama/TinyLlama-1.1B-Chat-v1.0,afternoon,1.8381905496120452,308.795,51.08
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TinyLlama/TinyLlama-1.1B-Chat-v1.0,late afternoon,1.7547113946505954,285.17857142857144,46.9
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TinyLlama/TinyLlama-1.1B-Chat-v1.0,evening,1.7984187936782836,313.99,51.96
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TinyLlama/TinyLlama-1.1B-Chat-v1.0,late evening,1.5875422928068372,192.78333333333333,31.261111111111113
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TinyLlama/TinyLlama-1.1B-Chat-v1.0,midnight,1.6341248273849487,210.2,34.2
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TinyLlama/TinyLlama-1.1B-Chat-v1.0,night,2.0128010153770446,372.05,62.6
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WizardLM v1.2 (13B),night,3.350038528442383,162.0,26.0
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chat-bison,midnight,3.890243631601334,398.075,49.0
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33 |
+
chat-bison (PaLM 2),early morning,2.4665334616388592,381.9214285714286,60.892857142857146
|
34 |
+
chat-bison (PaLM 2),morning,2.488477897644043,381.73,60.88
|
35 |
+
chat-bison (PaLM 2),afternoon,2.6460144804074215,376.28076923076924,55.965384615384615
|
36 |
+
chat-bison (PaLM 2),late afternoon,3.0387172081253744,381.1363636363636,53.35454545454545
|
37 |
+
chat-bison (PaLM 2),evening,2.688272579908371,367.07,55.89
|
38 |
+
chat-bison (PaLM 2),late evening,2.7250528037548065,382.725,52.95
|
39 |
+
chat-bison (PaLM 2),midnight,2.468383938074112,381.95,60.9
|
40 |
+
chat-bison (PaLM 2),night,2.460119960308075,381.92,60.94
|
41 |
+
chat-bison-32k,midnight,4.128177767992019,389.925,48.025
|
42 |
+
chat-bison-32k (PaLM 2 32K),early morning,9.141417106560299,335.75,53.85
|
43 |
+
chat-bison-32k (PaLM 2 32K),morning,7.7035503840446475,335.75,53.85
|
44 |
+
chat-bison-32k (PaLM 2 32K),afternoon,5.014458654477046,339.3692307692308,49.323076923076925
|
45 |
+
chat-bison-32k (PaLM 2 32K),late afternoon,4.5047362284226855,348.65909090909093,48.35454545454545
|
46 |
+
chat-bison-32k (PaLM 2 32K),evening,7.7332194912433625,329.46,49.5
|
47 |
+
chat-bison-32k (PaLM 2 32K),late evening,7.796841062307358,349.95,47.805
|
48 |
+
chat-bison-32k (PaLM 2 32K),midnight,7.7498266498247785,335.75,53.85
|
49 |
+
chat-bison-32k (PaLM 2 32K),night,6.491292915344238,335.75,53.85
|
50 |
+
gemini-pro,early morning,2.7453590130460435,381.2214285714286,60.964285714285715
|
51 |
+
gemini-pro,morning,2.497767536007628,371.93,59.39
|
52 |
+
gemini-pro,afternoon,2.816922114008949,360.62307692307695,53.25769230769231
|
53 |
+
gemini-pro,late afternoon,2.9268629640903114,364.57272727272726,50.1
|
54 |
+
gemini-pro,evening,2.86901999375759,366.4,55.045
|
55 |
+
gemini-pro,late evening,3.7189874940246117,390.9142857142857,51.35
|
56 |
+
gemini-pro,midnight,3.338477972348531,369.825,55.9125
|
57 |
+
gemini-pro,night,2.8375814715210272,374.8,60.04
|
58 |
+
gpt-3.5-turbo,early morning,3.787998208734724,403.74444444444447,47.34444444444444
|
59 |
+
gpt-3.5-turbo,morning,3.126271222697364,389.9888888888889,50.93888888888889
|
60 |
+
gpt-3.5-turbo,afternoon,3.9458200880459375,381.4428571428571,43.76190476190476
|
61 |
+
gpt-3.5-turbo,late afternoon,4.384064777692159,396.4357142857143,43.28333333333333
|
62 |
+
gpt-3.5-turbo,evening,3.5153889304115657,385.18095238095236,46.01428571428571
|
63 |
+
gpt-3.5-turbo,late evening,5.110168156187617,422.73510971786834,45.358934169278996
|
64 |
+
gpt-3.5-turbo,midnight,3.822115447632102,426.71923076923076,49.05
|
65 |
+
gpt-3.5-turbo,night,6.062970260473398,418.6192307692308,44.926923076923075
|
66 |
+
gpt-4,early morning,14.348626694414351,323.5388888888889,40.544444444444444
|
67 |
+
gpt-4,morning,12.759107512468733,338.18333333333334,46.85
|
68 |
+
gpt-4,afternoon,16.002364798386893,318.3095238095238,38.77142857142857
|
69 |
+
gpt-4,late afternoon,16.80607506932254,313.3595238095238,37.49285714285714
|
70 |
+
gpt-4,evening,13.841120740345547,318.0809523809524,40.19285714285714
|
71 |
+
gpt-4,late evening,14.298642643005493,314.336,37.012
|
72 |
+
gpt-4,midnight,12.3578163115329,334.075,41.35
|
73 |
+
gpt-4,night,12.813134506115546,316.93461538461537,37.93076923076923
|
74 |
+
gpt-4-turbo,early morning,11.555620827939775,357.65555555555557,47.21666666666667
|
75 |
+
gpt-4-turbo,morning,13.686854598257277,381.8888888888889,55.02777777777778
|
76 |
+
gpt-4-turbo,afternoon,13.997754749229976,351.01190476190476,46.03333333333333
|
77 |
+
gpt-4-turbo,late afternoon,22.320911452883767,381.65714285714284,47.35476190476191
|
78 |
+
gpt-4-turbo,evening,16.550320884159632,382.31666666666666,48.45
|
79 |
+
gpt-4-turbo,late evening,14.592236209392548,413.61,44.8
|
80 |
+
gpt-4-turbo,midnight,13.770663784850727,382.7613636363636,47.61818181818182
|
81 |
+
gpt-4-turbo,night,14.254795966698573,352.54615384615386,46.37692307692308
|
82 |
+
llama-2-70b-chat,early morning,2.8660141522424265,289.6642857142857,44.614285714285714
|
83 |
+
llama-2-70b-chat,morning,2.872361832027194,283.35,43.45
|
84 |
+
llama-2-70b-chat,afternoon,4.234376892130426,375.44615384615383,55.238461538461536
|
85 |
+
llama-2-70b-chat,late afternoon,3.6833307104881365,433.6636363636364,59.445454545454545
|
86 |
+
llama-2-70b-chat,evening,2.9706250462084185,317.245,47.4
|
87 |
+
llama-2-70b-chat,late evening,4.719581684340602,572.1689655172414,79.83103448275862
|
88 |
+
llama-2-70b-chat,midnight,3.249819871626402,346.8875,52.06875
|
89 |
+
llama-2-70b-chat,night,2.8264514451677147,313.71,48.42
|
90 |
+
zephyr-7b-beta,early morning,3.937663261095683,273.03333333333336,43.45
|
91 |
+
zephyr-7b-beta,morning,4.056525647640228,386.3,63.35
|
92 |
+
zephyr-7b-beta,afternoon,3.5789777278900146,277.6,44.016666666666666
|
93 |
+
zephyr-7b-beta,late afternoon,3.4592524923459447,248.59,38.9
|
94 |
+
zephyr-7b-beta,evening,3.5673056403795878,273.3666666666667,43.5
|
95 |
+
zephyr-7b-beta,late evening,3.45343524068594,176.23125,27.21875
|
96 |
+
zephyr-7b-beta,midnight,3.7253047794103624,217.82083333333333,33.67916666666667
|
97 |
+
zephyr-7b-beta,night,3.6954557319482166,216.55833333333334,33.46666666666667
|
data/2024-02-14 10:19:15.390578_time_of_day_comparison.csv
ADDED
@@ -0,0 +1,101 @@
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|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
1 |
+
model,time_of_day,execution_time,characters_count,words_count
|
2 |
+
Chronos Hermes (13B),night,4.168874144554138,360.0,58.5
|
3 |
+
Falcon Instruct (7B),night,1.0781378746032715,42.0,7.5
|
4 |
+
LLaMA-2 Chat (7B),night,1.686687707901001,377.5,61.5
|
5 |
+
Mistral (7B) Instruct v0.2 (Together AI),morning,1.8144107659657795,253.75,39.0
|
6 |
+
Mistral (7B) Instruct v0.2 (Together AI),afternoon,3.4312500613076344,541.5,58.875
|
7 |
+
Mistral (7B) Instruct v0.2 (Together AI),late afternoon,3.4016824527220293,563.8333333333334,62.375
|
8 |
+
Mistral (7B) Instruct v0.2 (Together AI),evening,11.989823137010847,581.5,69.14285714285714
|
9 |
+
Mistral (7B) Instruct v0.2 (Together AI),night,1.7709604501724243,311.0,48.0
|
10 |
+
Mistral-7B-Instruct-v0.2,morning,3.734026002883911,362.9,58.35
|
11 |
+
Mistral-7B-Instruct-v0.2,afternoon,3.228973722457886,222.0625,32.25
|
12 |
+
Mistral-7B-Instruct-v0.2,late afternoon,3.2048643112182615,219.8625,31.8
|
13 |
+
Mistral-7B-Instruct-v0.2,evening,3.397640073299408,261.18333333333334,40.1
|
14 |
+
Mistral-7B-Instruct-v0.2,late evening,3.389284573495388,175.79375,25.68125
|
15 |
+
Mistral-7B-Instruct-v0.2,midnight,1.9149879813194275,37.8,2.95
|
16 |
+
Mixtral-8x7B-Instruct-v0.1,early morning,4.526968242530536,285.045,41.86
|
17 |
+
Mixtral-8x7B-Instruct-v0.1,morning,3.9661054956285575,304.82,47.28
|
18 |
+
Mixtral-8x7B-Instruct-v0.1,afternoon,5.362903979589355,369.3192307692308,54.353846153846156
|
19 |
+
Mixtral-8x7B-Instruct-v0.1,late afternoon,5.80184749175942,347.9681818181818,47.27272727272727
|
20 |
+
Mixtral-8x7B-Instruct-v0.1,evening,3.6435119574237023,326.69,48.545
|
21 |
+
Mixtral-8x7B-Instruct-v0.1,late evening,5.62397656769588,395.15714285714284,49.02857142857143
|
22 |
+
Mixtral-8x7B-Instruct-v0.1,midnight,4.639010797279158,323.0394736842105,42.69210526315789
|
23 |
+
Mixtral-8x7B-Instruct-v0.1,night,4.009439338194697,301.24545454545455,42.21818181818182
|
24 |
+
OpenHermes-2.5-Mistral (7B),night,3.330963373184204,179.5,28.0
|
25 |
+
RedPajama-INCITE Chat (7B),night,1.3475391864776611,34.5,5.0
|
26 |
+
Snorkel Mistral PairRM DPO (7B),night,4.399647831916809,404.5,60.5
|
27 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,early morning,2.0273348593711855,372.13,62.53
|
28 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,morning,1.9041210174560548,372.05,62.6
|
29 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,afternoon,1.8381905496120452,308.795,51.08
|
30 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,late afternoon,1.7547113946505954,285.17857142857144,46.9
|
31 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,evening,1.7984187936782836,313.99,51.96
|
32 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,late evening,1.5875422928068372,192.78333333333333,31.261111111111113
|
33 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,midnight,1.6341248273849487,210.2,34.2
|
34 |
+
TinyLlama/TinyLlama-1.1B-Chat-v1.0,night,2.0128010153770446,372.05,62.6
|
35 |
+
WizardLM v1.2 (13B),night,3.350038528442383,162.0,26.0
|
36 |
+
chat-bison,midnight,3.890243631601334,398.075,49.0
|
37 |
+
chat-bison (PaLM 2),early morning,2.4665334616388592,381.9214285714286,60.892857142857146
|
38 |
+
chat-bison (PaLM 2),morning,2.488477897644043,381.73,60.88
|
39 |
+
chat-bison (PaLM 2),afternoon,2.6460144804074215,376.28076923076924,55.965384615384615
|
40 |
+
chat-bison (PaLM 2),late afternoon,3.0387172081253744,381.1363636363636,53.35454545454545
|
41 |
+
chat-bison (PaLM 2),evening,2.688272579908371,367.07,55.89
|
42 |
+
chat-bison (PaLM 2),late evening,2.7250528037548065,382.725,52.95
|
43 |
+
chat-bison (PaLM 2),midnight,2.468383938074112,381.95,60.9
|
44 |
+
chat-bison (PaLM 2),night,2.460119960308075,381.92,60.94
|
45 |
+
chat-bison-32k,midnight,4.128177767992019,389.925,48.025
|
46 |
+
chat-bison-32k (PaLM 2 32K),early morning,9.141417106560299,335.75,53.85
|
47 |
+
chat-bison-32k (PaLM 2 32K),morning,7.7035503840446475,335.75,53.85
|
48 |
+
chat-bison-32k (PaLM 2 32K),afternoon,5.014458654477046,339.3692307692308,49.323076923076925
|
49 |
+
chat-bison-32k (PaLM 2 32K),late afternoon,4.5047362284226855,348.65909090909093,48.35454545454545
|
50 |
+
chat-bison-32k (PaLM 2 32K),evening,7.7332194912433625,329.46,49.5
|
51 |
+
chat-bison-32k (PaLM 2 32K),late evening,7.796841062307358,349.95,47.805
|
52 |
+
chat-bison-32k (PaLM 2 32K),midnight,7.7498266498247785,335.75,53.85
|
53 |
+
chat-bison-32k (PaLM 2 32K),night,6.491292915344238,335.75,53.85
|
54 |
+
gemini-pro,early morning,2.7453590130460435,381.2214285714286,60.964285714285715
|
55 |
+
gemini-pro,morning,2.497767536007628,371.93,59.39
|
56 |
+
gemini-pro,afternoon,2.816922114008949,360.62307692307695,53.25769230769231
|
57 |
+
gemini-pro,late afternoon,2.9268629640903114,364.57272727272726,50.1
|
58 |
+
gemini-pro,evening,2.86901999375759,366.4,55.045
|
59 |
+
gemini-pro,late evening,3.7189874940246117,390.9142857142857,51.35
|
60 |
+
gemini-pro,midnight,3.338477972348531,369.825,55.9125
|
61 |
+
gemini-pro,night,2.8375814715210272,374.8,60.04
|
62 |
+
gpt-3.5-turbo,early morning,3.787998208734724,403.74444444444447,47.34444444444444
|
63 |
+
gpt-3.5-turbo,morning,3.126271222697364,389.9888888888889,50.93888888888889
|
64 |
+
gpt-3.5-turbo,afternoon,3.9458200880459375,381.4428571428571,43.76190476190476
|
65 |
+
gpt-3.5-turbo,late afternoon,4.384064777692159,396.4357142857143,43.28333333333333
|
66 |
+
gpt-3.5-turbo,evening,3.5153889304115657,385.18095238095236,46.01428571428571
|
67 |
+
gpt-3.5-turbo,late evening,5.110168156187617,422.73510971786834,45.358934169278996
|
68 |
+
gpt-3.5-turbo,midnight,3.822115447632102,426.71923076923076,49.05
|
69 |
+
gpt-3.5-turbo,night,6.062970260473398,418.6192307692308,44.926923076923075
|
70 |
+
gpt-4,early morning,14.348626694414351,323.5388888888889,40.544444444444444
|
71 |
+
gpt-4,morning,12.759107512468733,338.18333333333334,46.85
|
72 |
+
gpt-4,afternoon,16.002364798386893,318.3095238095238,38.77142857142857
|
73 |
+
gpt-4,late afternoon,16.80607506932254,313.3595238095238,37.49285714285714
|
74 |
+
gpt-4,evening,13.841120740345547,318.0809523809524,40.19285714285714
|
75 |
+
gpt-4,late evening,14.298642643005493,314.336,37.012
|
76 |
+
gpt-4,midnight,12.3578163115329,334.075,41.35
|
77 |
+
gpt-4,night,12.813134506115546,316.93461538461537,37.93076923076923
|
78 |
+
gpt-4-turbo,early morning,11.555620827939775,357.65555555555557,47.21666666666667
|
79 |
+
gpt-4-turbo,morning,13.686854598257277,381.8888888888889,55.02777777777778
|
80 |
+
gpt-4-turbo,afternoon,13.997754749229976,351.01190476190476,46.03333333333333
|
81 |
+
gpt-4-turbo,late afternoon,22.320911452883767,381.65714285714284,47.35476190476191
|
82 |
+
gpt-4-turbo,evening,16.550320884159632,382.31666666666666,48.45
|
83 |
+
gpt-4-turbo,late evening,14.592236209392548,413.61,44.8
|
84 |
+
gpt-4-turbo,midnight,13.770663784850727,382.7613636363636,47.61818181818182
|
85 |
+
gpt-4-turbo,night,14.254795966698573,352.54615384615386,46.37692307692308
|
86 |
+
llama-2-70b-chat,early morning,2.8660141522424265,289.6642857142857,44.614285714285714
|
87 |
+
llama-2-70b-chat,morning,2.872361832027194,283.35,43.45
|
88 |
+
llama-2-70b-chat,afternoon,4.234376892130426,375.44615384615383,55.238461538461536
|
89 |
+
llama-2-70b-chat,late afternoon,3.6833307104881365,433.6636363636364,59.445454545454545
|
90 |
+
llama-2-70b-chat,evening,2.9706250462084185,317.245,47.4
|
91 |
+
llama-2-70b-chat,late evening,4.719581684340602,572.1689655172414,79.83103448275862
|
92 |
+
llama-2-70b-chat,midnight,3.249819871626402,346.8875,52.06875
|
93 |
+
llama-2-70b-chat,night,2.8264514451677147,313.71,48.42
|
94 |
+
zephyr-7b-beta,early morning,3.937663261095683,273.03333333333336,43.45
|
95 |
+
zephyr-7b-beta,morning,4.056525647640228,386.3,63.35
|
96 |
+
zephyr-7b-beta,afternoon,3.5789777278900146,277.6,44.016666666666666
|
97 |
+
zephyr-7b-beta,late afternoon,3.4592524923459447,248.59,38.9
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zephyr-7b-beta,evening,3.5673056403795878,273.3666666666667,43.5
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99 |
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zephyr-7b-beta,late evening,3.45343524068594,176.23125,27.21875
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100 |
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zephyr-7b-beta,midnight,3.7253047794103624,217.82083333333333,33.67916666666667
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zephyr-7b-beta,night,3.6954557319482166,216.55833333333334,33.46666666666667
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data/general_plots.csv
CHANGED
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data/output_plots.csv
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@@ -1,4 +1,4 @@
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plot_object,header,plot_json
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"Figure({
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"Figure({
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'data': [{'alignmentgroup': 'True',
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'hovertemplate': 'Model=%{x}<br>Words count=%{y}<extra></extra>',
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@@ -400,7 +414,21 @@ plot_object,header,plot_json
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'title': {'text': 'Model'}},
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"Figure({
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'data': [{'alignmentgroup': 'True',
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'hovertemplate': 'Model=%{x}<br>Chunks mean size (characters)=%{y}<extra></extra>',
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@@ -601,4 +629,18 @@ plot_object,header,plot_json
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'title': {'text': 'Model'}},
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11,llama-2-70b-chat,6.065365233652719
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216 |
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|
633 |
+
0,Mistral-7B-Instruct-v0.2,2.6941392346739055
|
634 |
+
1,Mixtral-8x7B-Instruct-v0.1,2.9569017240855695
|
635 |
+
2,TinyLlama/TinyLlama-1.1B-Chat-v1.0,3.146639511119026
|
636 |
+
3,chat-bison,61.46100879376141
|
637 |
+
4,chat-bison (PaLM 2),57.12081730998353
|
638 |
+
5,chat-bison-32k,62.01633272616879
|
639 |
+
6,chat-bison-32k (PaLM 2 32K),56.57639873825073
|
640 |
+
7,gemini-pro,142.5030152620613
|
641 |
+
8,gpt-3.5-turbo,3.0439941593593898
|
642 |
+
9,gpt-4,3.018755174356351
|
643 |
+
10,gpt-4-turbo,3.0126559039321035
|
644 |
+
11,llama-2-70b-chat,3.2003241836714382
|
645 |
+
12,zephyr-7b-beta,2.711003271075019
|
646 |
+
","{""x"": ""model"", ""y"": ""chunk_sizes_mean"", ""color"": ""model"", ""labels"": {""model"": ""Model"", ""chunk_sizes_mean"": ""Chunks mean size (characters)""}, ""title"": ""Plot of chunks mean size (characters)""}"
|
data/time_of_day_plots.csv
CHANGED
The diff for this file is too large to render.
See raw diff
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pipeline/models.py
CHANGED
@@ -16,6 +16,7 @@ class Model(object):
|
|
16 |
cost_per_million_input_tokens: int = None
|
17 |
cost_per_million_output_tokens: int = None
|
18 |
input_size: int = None
|
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|
19 |
|
20 |
def __post_init__(self):
|
21 |
self.cost_per_million_input_tokens = self.cost_per_million_input_tokens or self.cost_per_million_tokens
|
@@ -144,10 +145,328 @@ MODELS = [
|
|
144 |
cost_per_million_input_tokens=0.25,
|
145 |
cost_per_million_output_tokens=0.5,
|
146 |
),
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|
147 |
]
|
148 |
-
|
149 |
-
"""
|
150 |
-
MODELS = [model for model in MODELS
|
151 |
-
if model.model_name=="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1"
|
152 |
-
or model.model_name=="huggingface/HuggingFaceH4/zephyr-7b-beta"]
|
153 |
-
"""
|
|
|
16 |
cost_per_million_input_tokens: int = None
|
17 |
cost_per_million_output_tokens: int = None
|
18 |
input_size: int = None
|
19 |
+
selected: bool = False
|
20 |
|
21 |
def __post_init__(self):
|
22 |
self.cost_per_million_input_tokens = self.cost_per_million_input_tokens or self.cost_per_million_tokens
|
|
|
145 |
cost_per_million_input_tokens=0.25,
|
146 |
cost_per_million_output_tokens=0.5,
|
147 |
),
|
148 |
+
# new models:
|
149 |
+
Model(
|
150 |
+
"01-ai Yi Chat (34B)",
|
151 |
+
"together_ai/zero-one-ai/Yi-34B-Chat",
|
152 |
+
None,
|
153 |
+
"Together AI",
|
154 |
+
input_size=4096,
|
155 |
+
# selected=True,
|
156 |
+
),
|
157 |
+
Model(
|
158 |
+
"Chronos Hermes (13B)",
|
159 |
+
"together_ai/Austism/chronos-hermes-13b",
|
160 |
+
None,
|
161 |
+
"Together AI",
|
162 |
+
input_size=2048,
|
163 |
+
# selected=True,
|
164 |
+
),
|
165 |
+
Model(
|
166 |
+
"Deepseek Coder Instruct (33B)",
|
167 |
+
"together_ai/deepseek-ai/deepseek-coder-33b-instruct",
|
168 |
+
None,
|
169 |
+
"Together AI",
|
170 |
+
input_size=16384,
|
171 |
+
),
|
172 |
+
Model(
|
173 |
+
"Platypus2 Instruct (70B)",
|
174 |
+
"together_ai/garage-bAInd/Platypus2-70B-instruct",
|
175 |
+
None,
|
176 |
+
"Together AI",
|
177 |
+
input_size=4096,
|
178 |
+
),
|
179 |
+
Model(
|
180 |
+
"MythoMax-L2 (13B)",
|
181 |
+
"together_ai/Gryphe/MythoMax-L2-13b",
|
182 |
+
None,
|
183 |
+
"Together AI",
|
184 |
+
input_size=4096,
|
185 |
+
),
|
186 |
+
Model(
|
187 |
+
"Vicuna v1.5 (13B)",
|
188 |
+
"together_ai/lmsys/vicuna-13b-v1.5",
|
189 |
+
None,
|
190 |
+
"Together AI",
|
191 |
+
input_size=4096,
|
192 |
+
),
|
193 |
+
Model("Vicuna v1.5 (7B)", "together_ai/lmsys/vicuna-7b-v1.5", None, "Together AI", input_size=4096, selected=True),
|
194 |
+
Model(
|
195 |
+
"Code Llama Instruct (13B)",
|
196 |
+
"together_ai/codellama/CodeLlama-13b-Instruct-hf",
|
197 |
+
None,
|
198 |
+
"Together AI",
|
199 |
+
input_size=16384,
|
200 |
+
),
|
201 |
+
Model(
|
202 |
+
"Code Llama Instruct (34B)",
|
203 |
+
"together_ai/codellama/CodeLlama-34b-Instruct-hf",
|
204 |
+
None,
|
205 |
+
"Together AI",
|
206 |
+
input_size=16384,
|
207 |
+
),
|
208 |
+
Model(
|
209 |
+
"Code Llama Instruct (70B)",
|
210 |
+
"together_ai/codellama/CodeLlama-70b-Instruct-hf",
|
211 |
+
None,
|
212 |
+
"Together AI",
|
213 |
+
input_size=4096,
|
214 |
+
),
|
215 |
+
Model(
|
216 |
+
"Code Llama Instruct (7B)",
|
217 |
+
"together_ai/codellama/CodeLlama-7b-Instruct-hf",
|
218 |
+
None,
|
219 |
+
"Together AI",
|
220 |
+
input_size=16384,
|
221 |
+
),
|
222 |
+
Model(
|
223 |
+
"LLaMA-2 Chat (13B)",
|
224 |
+
"together_ai/togethercomputer/llama-2-13b-chat",
|
225 |
+
None,
|
226 |
+
"Together AI",
|
227 |
+
input_size=4096,
|
228 |
+
),
|
229 |
+
Model(
|
230 |
+
"LLaMA-2 Chat (70B)",
|
231 |
+
"together_ai/togethercomputer/llama-2-70b-chat",
|
232 |
+
None,
|
233 |
+
"Together AI",
|
234 |
+
input_size=4096,
|
235 |
+
),
|
236 |
+
Model(
|
237 |
+
"LLaMA-2 Chat (7B)",
|
238 |
+
"together_ai/togethercomputer/llama-2-7b-chat",
|
239 |
+
None,
|
240 |
+
"Together AI",
|
241 |
+
input_size=4096,
|
242 |
+
# selected=True,
|
243 |
+
),
|
244 |
+
Model(
|
245 |
+
"Mistral (7B) Instruct",
|
246 |
+
"together_ai/mistralai/Mistral-7B-Instruct-v0.1",
|
247 |
+
None,
|
248 |
+
"Together AI",
|
249 |
+
input_size=4096,
|
250 |
+
),
|
251 |
+
Model(
|
252 |
+
"Mistral (7B) Instruct v0.2 (Together AI)",
|
253 |
+
"together_ai/mistralai/Mistral-7B-Instruct-v0.2",
|
254 |
+
None,
|
255 |
+
"Together AI",
|
256 |
+
input_size=32768,
|
257 |
+
selected=True,
|
258 |
+
),
|
259 |
+
Model(
|
260 |
+
"Mixtral-8x7B Instruct (46.7B)",
|
261 |
+
"together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1",
|
262 |
+
None,
|
263 |
+
"Together AI",
|
264 |
+
input_size=32768,
|
265 |
+
),
|
266 |
+
Model(
|
267 |
+
"Nous Capybara v1.9 (7B)",
|
268 |
+
"together_ai/NousResearch/Nous-Capybara-7B-V1p9",
|
269 |
+
None,
|
270 |
+
"Together AI",
|
271 |
+
input_size=8192,
|
272 |
+
),
|
273 |
+
Model(
|
274 |
+
"Nous Hermes 2 - Mixtral 8x7B-DPO (46.7B)",
|
275 |
+
"together_ai/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
|
276 |
+
None,
|
277 |
+
"Together AI",
|
278 |
+
input_size=32768,
|
279 |
+
),
|
280 |
+
Model(
|
281 |
+
"Nous Hermes 2 - Mixtral 8x7B-SFT (46.7B)",
|
282 |
+
"together_ai/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT",
|
283 |
+
None,
|
284 |
+
"Together AI",
|
285 |
+
input_size=32768,
|
286 |
+
),
|
287 |
+
Model(
|
288 |
+
"Nous Hermes LLaMA-2 (7B)",
|
289 |
+
"together_ai/NousResearch/Nous-Hermes-llama-2-7b",
|
290 |
+
None,
|
291 |
+
"Together AI",
|
292 |
+
input_size=4096,
|
293 |
+
),
|
294 |
+
Model(
|
295 |
+
"Nous Hermes Llama-2 (13B)",
|
296 |
+
"together_ai/NousResearch/Nous-Hermes-Llama2-13b",
|
297 |
+
None,
|
298 |
+
"Together AI",
|
299 |
+
input_size=4096,
|
300 |
+
),
|
301 |
+
Model(
|
302 |
+
"Nous Hermes-2 Yi (34B)",
|
303 |
+
"together_ai/NousResearch/Nous-Hermes-2-Yi-34B",
|
304 |
+
None,
|
305 |
+
"Together AI",
|
306 |
+
input_size=4096,
|
307 |
+
),
|
308 |
+
Model(
|
309 |
+
"OpenChat 3.5 (7B)",
|
310 |
+
"together_ai/openchat/openchat-3.5-1210",
|
311 |
+
None,
|
312 |
+
"Together AI",
|
313 |
+
input_size=8192,
|
314 |
+
),
|
315 |
+
Model(
|
316 |
+
"OpenOrca Mistral (7B) 8K",
|
317 |
+
"together_ai/Open-Orca/Mistral-7B-OpenOrca",
|
318 |
+
None,
|
319 |
+
"Together AI",
|
320 |
+
input_size=8192,
|
321 |
+
),
|
322 |
+
Model(
|
323 |
+
"Qwen-Chat (7B)",
|
324 |
+
"together_ai/togethercomputer/Qwen-7B-Chat",
|
325 |
+
None,
|
326 |
+
"Together AI",
|
327 |
+
input_size=8192,
|
328 |
+
),
|
329 |
+
Model(
|
330 |
+
"Qwen 1.5 Chat (0.5B)",
|
331 |
+
"together_ai/Qwen/Qwen1.5-0.5B-Chat",
|
332 |
+
None,
|
333 |
+
"Together AI",
|
334 |
+
input_size=32768,
|
335 |
+
),
|
336 |
+
Model(
|
337 |
+
"Qwen 1.5 Chat (1.8B)",
|
338 |
+
"together_ai/Qwen/Qwen1.5-1.8B-Chat",
|
339 |
+
None,
|
340 |
+
"Together AI",
|
341 |
+
input_size=32768,
|
342 |
+
),
|
343 |
+
Model(
|
344 |
+
"Qwen 1.5 Chat (4B)",
|
345 |
+
"together_ai/Qwen/Qwen1.5-4B-Chat",
|
346 |
+
None,
|
347 |
+
"Together AI",
|
348 |
+
input_size=32768,
|
349 |
+
),
|
350 |
+
Model(
|
351 |
+
"Qwen 1.5 Chat (7B)", "together_ai/Qwen/Qwen1.5-7B-Chat", None, "Together AI", input_size=32768,
|
352 |
+
# selected=True
|
353 |
+
),
|
354 |
+
Model(
|
355 |
+
"Qwen 1.5 Chat (14B)",
|
356 |
+
"together_ai/Qwen/Qwen1.5-14B-Chat",
|
357 |
+
None,
|
358 |
+
"Together AI",
|
359 |
+
input_size=32768,
|
360 |
+
),
|
361 |
+
Model(
|
362 |
+
"Qwen 1.5 Chat (72B)",
|
363 |
+
"together_ai/Qwen/Qwen1.5-72B-Chat",
|
364 |
+
None,
|
365 |
+
"Together AI",
|
366 |
+
input_size=4096,
|
367 |
+
),
|
368 |
+
Model(
|
369 |
+
"Snorkel Mistral PairRM DPO (7B)",
|
370 |
+
"together_ai/snorkelai/Snorkel-Mistral-PairRM-DPO",
|
371 |
+
None,
|
372 |
+
"Together AI",
|
373 |
+
input_size=32768,
|
374 |
+
# selected=True,
|
375 |
+
),
|
376 |
+
Model(
|
377 |
+
"Alpaca (7B)",
|
378 |
+
"together_ai/togethercomputer/alpaca-7b",
|
379 |
+
None,
|
380 |
+
"Together AI",
|
381 |
+
input_size=2048,
|
382 |
+
),
|
383 |
+
Model(
|
384 |
+
"OpenHermes-2-Mistral (7B)",
|
385 |
+
"teknium/OpenHermes-2-Mistral-7B",
|
386 |
+
None,
|
387 |
+
"Together AI",
|
388 |
+
input_size=8192,
|
389 |
+
),
|
390 |
+
Model(
|
391 |
+
"OpenHermes-2.5-Mistral (7B)",
|
392 |
+
"together_ai/teknium/OpenHermes-2p5-Mistral-7B",
|
393 |
+
None,
|
394 |
+
"Together AI",
|
395 |
+
input_size=8192,
|
396 |
+
# selected=True,
|
397 |
+
),
|
398 |
+
Model(
|
399 |
+
"Falcon Instruct (40B)",
|
400 |
+
"together_ai/togethercomputer/falcon-40b-instruct",
|
401 |
+
None,
|
402 |
+
"Together AI",
|
403 |
+
input_size=2048,
|
404 |
+
),
|
405 |
+
Model(
|
406 |
+
"Falcon Instruct (7B)",
|
407 |
+
"together_ai/togethercomputer/falcon-7b-instruct",
|
408 |
+
None,
|
409 |
+
"Together AI",
|
410 |
+
input_size=2048,
|
411 |
+
# selected=True,
|
412 |
+
),
|
413 |
+
Model(
|
414 |
+
"LLaMA-2-7B-32K-Instruct (7B)",
|
415 |
+
"together_ai/togethercomputer/Llama-2-7B-32K-Instruct",
|
416 |
+
None,
|
417 |
+
"Together AI",
|
418 |
+
input_size=32768,
|
419 |
+
),
|
420 |
+
Model(
|
421 |
+
"RedPajama-INCITE Chat (3B)",
|
422 |
+
"together_ai/togethercomputer/RedPajama-INCITE-Chat-3B-v1",
|
423 |
+
None,
|
424 |
+
"Together AI",
|
425 |
+
input_size=2048,
|
426 |
+
),
|
427 |
+
Model(
|
428 |
+
"RedPajama-INCITE Chat (7B)",
|
429 |
+
"together_ai/togethercomputer/RedPajama-INCITE-7B-Chat",
|
430 |
+
None,
|
431 |
+
"Together AI",
|
432 |
+
input_size=2048,
|
433 |
+
# selected=True,
|
434 |
+
),
|
435 |
+
Model(
|
436 |
+
"StripedHyena Nous (7B)",
|
437 |
+
"together_ai/togethercomputer/StripedHyena-Nous-7B",
|
438 |
+
None,
|
439 |
+
"Together AI",
|
440 |
+
input_size=32768,
|
441 |
+
),
|
442 |
+
Model(
|
443 |
+
"ReMM SLERP L2 (13B)",
|
444 |
+
"together_ai/Undi95/ReMM-SLERP-L2-13B",
|
445 |
+
None,
|
446 |
+
"Together AI",
|
447 |
+
input_size=4096,
|
448 |
+
),
|
449 |
+
Model(
|
450 |
+
"Toppy M (7B)",
|
451 |
+
"together_ai/Undi95/Toppy-M-7B",
|
452 |
+
None,
|
453 |
+
"Together AI",
|
454 |
+
input_size=4096,
|
455 |
+
),
|
456 |
+
Model(
|
457 |
+
"WizardLM v1.2 (13B)",
|
458 |
+
"together_ai/WizardLM/WizardLM-13B-V1.2",
|
459 |
+
None,
|
460 |
+
"Together AI",
|
461 |
+
input_size=4096,
|
462 |
+
# selected=True,
|
463 |
+
),
|
464 |
+
Model(
|
465 |
+
"Upstage SOLAR Instruct v1 (11B)",
|
466 |
+
"together_ai/upstage/SOLAR-10.7B-Instruct-v1.0",
|
467 |
+
None,
|
468 |
+
"Together AI",
|
469 |
+
input_size=4096,
|
470 |
+
# selected=True,
|
471 |
+
),
|
472 |
]
|
|
|
|
|
|
|
|
|
|
|
|