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β’
43b85eb
1
Parent(s):
e8165e3
fix
Browse files
app.py
CHANGED
@@ -101,19 +101,11 @@ def get_benchmark_df(benchmark="Succeeded-1xA100-80GB"):
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merged_df.sort_values(by=SORTING_COLUMN, ascending=SORTING_ASCENDING, inplace=True)
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# drop duplicates
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merged_df.drop_duplicates(subset=NO_DUPLICATES_COLUMNS, inplace=True)
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merged_df = merged_df[[*list(ALL_COLUMNS_MAPPING.keys()), "generate.latency(s)"]]
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return merged_df
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def get_benchmark_table(bench_df):
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copy_df = bench_df.copy()
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# adding ** to quantized models score since we can't garantee the score is the same
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copy_df["best_score"] = copy_df.apply(
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lambda x: f"{x['best_score']}**"
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if x["backend.quantization_strategy"] in ["bnb", "gptq"]
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else x["best_score"],
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axis=1,
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)
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# filter
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copy_df = copy_df[list(ALL_COLUMNS_MAPPING.keys())]
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# rename
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@@ -123,7 +115,13 @@ def get_benchmark_table(bench_df):
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copy_df["Best Scored LLM π"] = copy_df["Best Scored LLM π"].apply(
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process_model_name
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)
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-
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return copy_df
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merged_df.sort_values(by=SORTING_COLUMN, ascending=SORTING_ASCENDING, inplace=True)
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# drop duplicates
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merged_df.drop_duplicates(subset=NO_DUPLICATES_COLUMNS, inplace=True)
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return merged_df
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def get_benchmark_table(bench_df):
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copy_df = bench_df.copy()
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# filter
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copy_df = copy_df[list(ALL_COLUMNS_MAPPING.keys())]
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# rename
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copy_df["Best Scored LLM π"] = copy_df["Best Scored LLM π"].apply(
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process_model_name
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)
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# process quantization
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copy_df["Best Score (%) β¬οΈ"] = copy_df.apply(
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lambda x: f"{x['Best Score (%) β¬οΈ']}**"
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if x["Quantization ποΈ"] in ["BnB.4bit", "GPTQ.4bit"]
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else x["Best Score (%) β¬οΈ"],
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axis=1,
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)
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return copy_df
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