Commit
•
47f3547
1
Parent(s):
6d60fea
adding hyperlinks to models
Browse files
app.py
CHANGED
@@ -166,9 +166,15 @@ with demo:
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label="Model Types",
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elem_id="model_types",
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value=["pretrained","fine-tuned","chat-models","merges/moerges"])
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-
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initial_df = pd.read_csv("./status/leaderboard_status_041624.csv")
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-
initial_df["Model"] =
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def update_df(hw_selected, platform_selected, affiliation_selected, size_selected, precision_selected, type_selected):
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filtered_df = filter_benchmarks_table(df=initial_df, hw_selected=hw_selected, platform_selected=platform_selected,
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@@ -176,6 +182,7 @@ with demo:
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precision_selected=precision_selected, type_selected=type_selected)
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return filtered_df
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initial_filtered_df = update_df(["Gaudi","Xeon","GPU Max","Arc GPU","Core Ultra"],
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["Intel Developer Cloud","AWS","Azure","Google Cloud Platform","Local"],
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["No Affiliation","Intel Innovator","Student Ambassador","Intel Liftoff", "Intel Engineering", "Other"],
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@@ -183,10 +190,12 @@ with demo:
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["fp32","fp16","bf16","int8","fp8", "int4"],
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["pretrained","fine-tuned","chat-models","merges/moerges"])
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gradio_df_display = gr.Dataframe(value=initial_filtered_df, headers=["Model","Average","Hardware","Model Type","Precision",
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"Size","Infrastructure","ARC","HellaSwag","MMLU",
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"TruthfulQA","Winogrande","Affiliation"],
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-
datatype=["
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filter_hw.change(fn=update_df,
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inputs=[filter_hw, filter_platform, filter_affiliation, filter_size, filter_precision, filter_type],
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label="Model Types",
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elem_id="model_types",
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value=["pretrained","fine-tuned","chat-models","merges/moerges"])
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+
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color = '#5750DC'
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def make_clickable(row):
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return f'<a href="https://huggingface.co/{row["Model"]}" target="_blank" style="color: {color}; text-decoration: underline;">{row["Model"]}</a>'
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initial_df = pd.read_csv("./status/leaderboard_status_041624.csv")
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initial_df["Model"] = initial_df.apply(make_clickable, axis=1)
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def update_df(hw_selected, platform_selected, affiliation_selected, size_selected, precision_selected, type_selected):
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filtered_df = filter_benchmarks_table(df=initial_df, hw_selected=hw_selected, platform_selected=platform_selected,
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precision_selected=precision_selected, type_selected=type_selected)
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return filtered_df
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+
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initial_filtered_df = update_df(["Gaudi","Xeon","GPU Max","Arc GPU","Core Ultra"],
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["Intel Developer Cloud","AWS","Azure","Google Cloud Platform","Local"],
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["No Affiliation","Intel Innovator","Student Ambassador","Intel Liftoff", "Intel Engineering", "Other"],
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["fp32","fp16","bf16","int8","fp8", "int4"],
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["pretrained","fine-tuned","chat-models","merges/moerges"])
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+
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gradio_df_display = gr.Dataframe(value=initial_filtered_df, headers=["Model","Average","Hardware","Model Type","Precision",
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"Size","Infrastructure","ARC","HellaSwag","MMLU",
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"TruthfulQA","Winogrande","Affiliation"],
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datatype=["html","str","str","str","str","str","str","str","str","str","str","str","str"],
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interactive=False)
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filter_hw.change(fn=update_df,
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inputs=[filter_hw, filter_platform, filter_affiliation, filter_size, filter_precision, filter_type],
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