Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
reject duplicate submission
Browse files
app.py
CHANGED
@@ -15,7 +15,21 @@ H4_TOKEN = os.environ.get("H4_TOKEN", None)
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LMEH_REPO = "HuggingFaceH4/lmeh_evaluations"
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IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", None))
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repo = None
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if H4_TOKEN:
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print("pulling repo")
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# try:
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@@ -31,6 +45,9 @@ if H4_TOKEN:
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)
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repo.git_pull()
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# parse the results
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BENCHMARKS = ["arc_challenge", "hellaswag", "hendrycks", "truthfulqa_mc"]
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@@ -110,7 +127,7 @@ def get_leaderboard():
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dataframe = pd.DataFrame.from_records(all_data)
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dataframe = dataframe.sort_values(by=["Average ⬆️"], ascending=False)
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print(dataframe)
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dataframe = dataframe[COLS]
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return dataframe
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@@ -187,12 +204,12 @@ def add_new_eval(
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if is_delta_weight and not is_model_on_hub(base_model, revision):
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error_message = f'Base model "{base_model}" was not found on hub!'
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print(error_message)
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return f"<p style='color: red; font-size:
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if not is_model_on_hub(model, revision):
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error_message = f'Model "{model}"was not found on hub!'
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print(error_message)
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return f"<p style='color: red; font-size:
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print("adding new eval")
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@@ -216,6 +233,11 @@ def add_new_eval(
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os.makedirs(OUT_DIR, exist_ok=True)
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out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{is_8_bit_eval}_{is_delta_weight}.json"
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with open(out_path, "w") as f:
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f.write(json.dumps(eval_entry))
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LMEH_REPO = "HuggingFaceH4/lmeh_evaluations"
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@@ -230,7 +252,7 @@ def add_new_eval(
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)
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success_message = "Your request has been submitted to the evaluation queue!"
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return f"<p style='color: green; font-size:
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def refresh():
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LMEH_REPO = "HuggingFaceH4/lmeh_evaluations"
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IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", None))
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def get_all_requested_models(requested_models_dir):
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depth = 1
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file_names = []
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for root, dirs, files in os.walk(requested_models_dir):
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current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
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if current_depth == depth:
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file_names.extend([os.path.join(root, file) for file in files])
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return set([file_name.lower().split("./evals/")[1] for file_name in file_names])
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repo = None
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requested_models = None
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if H4_TOKEN:
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print("pulling repo")
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# try:
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)
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repo.git_pull()
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requested_models_dir = "./evals/eval_requests"
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requested_models = get_all_requested_models(requested_models_dir)
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# parse the results
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BENCHMARKS = ["arc_challenge", "hellaswag", "hendrycks", "truthfulqa_mc"]
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dataframe = pd.DataFrame.from_records(all_data)
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dataframe = dataframe.sort_values(by=["Average ⬆️"], ascending=False)
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# print(dataframe)
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dataframe = dataframe[COLS]
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return dataframe
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if is_delta_weight and not is_model_on_hub(base_model, revision):
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error_message = f'Base model "{base_model}" was not found on hub!'
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print(error_message)
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return f"<p style='color: red; font-size: 20px; text-align: center;'>{error_message}</p>"
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if not is_model_on_hub(model, revision):
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error_message = f'Model "{model}"was not found on hub!'
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print(error_message)
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return f"<p style='color: red; font-size: 20px; text-align: center;'>{error_message}</p>"
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print("adding new eval")
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os.makedirs(OUT_DIR, exist_ok=True)
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out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{is_8_bit_eval}_{is_delta_weight}.json"
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# Check for duplicate submission
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if out_path.lower() in requested_models:
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duplicate_request_message = "This model has been already submitted."
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return f"<p style='color: orange; font-size: 20px; text-align: center;'>{duplicate_request_message}</p>"
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with open(out_path, "w") as f:
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f.write(json.dumps(eval_entry))
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LMEH_REPO = "HuggingFaceH4/lmeh_evaluations"
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)
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success_message = "Your request has been submitted to the evaluation queue!"
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return f"<p style='color: green; font-size: 20px; text-align: center;'>{success_message}</p>"
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def refresh():
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utils.py
CHANGED
@@ -133,4 +133,4 @@ def get_eval_results_dicts(is_public=True) -> List[Dict]:
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return [e.to_dict() for e in eval_results]
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eval_results_dict = get_eval_results_dicts()
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print(eval_results_dict)
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return [e.to_dict() for e in eval_results]
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eval_results_dict = get_eval_results_dicts()
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# print(eval_results_dict)
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