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Clémentine
commited on
Commit
•
2a5f9fb
1
Parent(s):
e3aaf53
refacto part 1
Browse files- app.py +44 -254
- model_info_cache.pkl +0 -3
- model_size_cache.pkl +0 -3
- scripts/create_request_file.py +8 -6
- src/assets/hardcoded_evals.py +0 -43
- src/assets/scale-hf-logo.png +0 -3
- src/{assets/text_content.py → display/about.py} +2 -2
- src/{assets → display}/css_html_js.py +0 -0
- src/{get_model_info/utils.py → display/formatting.py} +0 -61
- src/display/utils.py +141 -0
- src/envs.py +28 -0
- src/get_model_info/apply_metadata_to_df.py +0 -95
- src/get_model_info/get_metadata_from_hub.py +0 -19
- src/get_model_info/hardocded_metadata/types.py +0 -555
- src/{get_model_info/hardocded_metadata/flags.py → leaderboard/filter_models.py} +32 -0
- src/leaderboard/read_evals.py +197 -0
- src/plots/read_results.py +0 -158
- src/{load_from_hub.py → populate.py} +7 -40
- src/{filters.py → submission/check_validity.py} +46 -2
- src/submission/submit.py +128 -0
- src/{manage_collections.py → tools/collections.py} +28 -20
- models_backlinks.py → src/tools/model_backlinks.py +0 -0
- src/{plots/plot_results.py → tools/plots.py} +2 -2
app.py
CHANGED
@@ -1,14 +1,24 @@
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import json
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import os
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from datetime import datetime, timezone
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import gradio as gr
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import pandas as pd
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from apscheduler.schedulers.background import BackgroundScheduler
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from huggingface_hub import
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from src.
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-
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CITATION_BUTTON_LABEL,
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CITATION_BUTTON_TEXT,
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EVALUATION_QUEUE_TEXT,
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@@ -16,102 +26,44 @@ from src.assets.text_content import (
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LLM_BENCHMARKS_TEXT,
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TITLE,
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)
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from src.plots
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create_metric_plot_obj,
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create_scores_df,
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create_plot_df,
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join_model_info_with_results,
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HUMAN_BASELINES,
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)
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from src.
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from src.
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from src.
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from src.
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AutoEvalColumn,
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EvalQueueColumn,
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fields,
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styled_error,
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styled_message,
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styled_warning,
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)
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from src.manage_collections import update_collections
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from src.load_from_hub import get_all_requested_models, get_evaluation_queue_df, get_leaderboard_df
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from src.filters import is_model_on_hub, user_submission_permission
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pd.set_option("display.precision", 1)
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# clone / pull the lmeh eval data
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H4_TOKEN = os.environ.get("H4_TOKEN", None)
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QUEUE_REPO = "open-llm-leaderboard/requests"
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RESULTS_REPO = "open-llm-leaderboard/results"
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PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests"
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PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results"
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IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
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EVAL_REQUESTS_PATH = "eval-queue"
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EVAL_RESULTS_PATH = "eval-results"
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EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
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EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"
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api = HfApi(token=H4_TOKEN)
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def restart_space():
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# Rate limit variables
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RATE_LIMIT_PERIOD = 7
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RATE_LIMIT_QUOTA = 5
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# Column selection
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COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
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TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
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COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
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TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
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if not IS_PUBLIC:
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COLS.insert(2, AutoEvalColumn.precision.name)
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TYPES.insert(2, AutoEvalColumn.precision.type)
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EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
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EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
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BENCHMARK_COLS = [
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c.name
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for c in [
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AutoEvalColumn.arc,
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AutoEvalColumn.hellaswag,
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AutoEvalColumn.mmlu,
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AutoEvalColumn.truthfulqa,
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AutoEvalColumn.winogrande,
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AutoEvalColumn.gsm8k,
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AutoEvalColumn.drop
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]
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]
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try:
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snapshot_download(
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except Exception:
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restart_space()
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try:
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snapshot_download(
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except Exception:
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restart_space()
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requested_models, users_to_submission_dates = get_all_requested_models(EVAL_REQUESTS_PATH)
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original_df = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS)
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update_collections(original_df.copy())
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leaderboard_df = original_df.copy()
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models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard
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#plot_df = create_plot_df(create_scores_df(join_model_info_with_results(original_df)))
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to_be_dumped = f"models = {repr(models)}\n"
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(
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finished_eval_queue_df,
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) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
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## INTERACTION FUNCTIONS
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def add_new_eval(
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model: str,
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base_model: str,
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revision: str,
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precision: str,
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private: bool,
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weight_type: str,
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model_type: str,
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):
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precision = precision.split(" ")[0]
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current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
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if model_type is None or model_type == "":
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return styled_error("Please select a model type.")
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# Is the user rate limited?
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user_can_submit, error_msg = user_submission_permission(model, users_to_submission_dates, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA)
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if not user_can_submit:
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return styled_error(error_msg)
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# Did the model authors forbid its submission to the leaderboard?
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if model in DO_NOT_SUBMIT_MODELS or base_model in DO_NOT_SUBMIT_MODELS:
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return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.")
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# Does the model actually exist?
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if revision == "":
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revision = "main"
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if weight_type in ["Delta", "Adapter"]:
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base_model_on_hub, error = is_model_on_hub(base_model, revision, H4_TOKEN)
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if not base_model_on_hub:
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return styled_error(f'Base model "{base_model}" {error}')
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if not weight_type == "Adapter":
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model_on_hub, error = is_model_on_hub(model, revision)
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if not model_on_hub:
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return styled_error(f'Model "{model}" {error}')
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try:
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model_info = api.model_info(repo_id=model, revision=revision)
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except Exception:
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return styled_error("Could not get your model information. Please fill it up properly.")
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model_size = get_model_size(model_info=model_info , precision= precision)
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# Were the model card and license filled?
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try:
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license = model_info.cardData["license"]
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except Exception:
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return styled_error("Please select a license for your model")
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modelcard_OK, error_msg = check_model_card(model)
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if not modelcard_OK:
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return styled_error(error_msg)
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# Seems good, creating the eval
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print("Adding new eval")
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eval_entry = {
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"model": model,
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"base_model": base_model,
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"revision": revision,
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"private": private,
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"precision": precision,
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"weight_type": weight_type,
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"status": "PENDING",
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"submitted_time": current_time,
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"model_type": model_type,
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"likes": model_info.likes,
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"params": model_size,
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"license": license,
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}
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user_name = ""
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model_path = model
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if "/" in model:
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user_name = model.split("/")[0]
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model_path = model.split("/")[1]
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print("Creating eval file")
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OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
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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}_{precision}_{weight_type}.json"
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# Check for duplicate submission
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if f"{model}_{revision}_{precision}" in requested_models:
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return styled_warning("This model has been already submitted.")
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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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print("Uploading eval file")
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api.upload_file(
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path_or_fileobj=out_path,
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path_in_repo=out_path.split("eval-queue/")[1],
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repo_id=QUEUE_REPO,
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repo_type="dataset",
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commit_message=f"Add {model} to eval queue",
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)
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# Remove the local file
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os.remove(out_path)
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return styled_message(
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"Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
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)
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# Basics
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def change_tab(query_param: str):
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query_param = query_param.replace("'", '"')
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return filtered_df
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NUMERIC_INTERVALS = {
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"?": pd.Interval(-1, 0, closed="right"),
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"~1.5": pd.Interval(0, 2, closed="right"),
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"~3": pd.Interval(2, 4, closed="right"),
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"~7": pd.Interval(4, 9, closed="right"),
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"~13": pd.Interval(9, 20, closed="right"),
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"~35": pd.Interval(20, 45, closed="right"),
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"~60": pd.Interval(45, 70, closed="right"),
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"70+": pd.Interval(70, 10000, closed="right"),
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}
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def filter_queries(query: str, filtered_df: pd.DataFrame):
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"""Added by Abishek"""
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final_df = []
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if show_deleted:
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filtered_df = df
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else: # Show only still on the hub models
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filtered_df = df[df[AutoEvalColumn.still_on_hub.name]
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type_emoji = [t[0] for t in type_query]
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filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
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)
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with gr.Row():
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shown_columns = gr.CheckboxGroup(
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choices=[
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for c in COLS
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if c
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not in [
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AutoEvalColumn.dummy.name,
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AutoEvalColumn.model.name,
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AutoEvalColumn.model_type_symbol.name,
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AutoEvalColumn.still_on_hub.name,
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]
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],
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value=[
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c
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for c in COLS_LITE
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if c
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not in [
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AutoEvalColumn.dummy.name,
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AutoEvalColumn.model.name,
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AutoEvalColumn.model_type_symbol.name,
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AutoEvalColumn.still_on_hub.name,
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]
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],
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label="Select columns to show",
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elem_id="column-select",
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interactive=True,
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)
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with gr.Row():
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deleted_models_visibility = gr.Checkbox(
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value=
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)
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with gr.Column(min_width=320):
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with gr.Box(elem_id="box-filter"):
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filter_columns_type = gr.CheckboxGroup(
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label="Model types",
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choices=[
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-
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ModelType.FT.to_str(),
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ModelType.IFT.to_str(),
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ModelType.RL.to_str(),
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ModelType.Unknown.to_str(),
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],
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value=[
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ModelType.PT.to_str(),
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ModelType.FT.to_str(),
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ModelType.IFT.to_str(),
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ModelType.RL.to_str(),
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ModelType.Unknown.to_str(),
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],
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interactive=True,
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elem_id="filter-columns-type",
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)
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@@ -410,16 +209,11 @@ with demo:
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leaderboard_table = gr.components.Dataframe(
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value=leaderboard_df[
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-
[
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+ shown_columns.value
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+ [AutoEvalColumn.dummy.name]
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],
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-
headers=[
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AutoEvalColumn.model_type_symbol.name,
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AutoEvalColumn.model.name,
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-
]
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-
+ shown_columns.value
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+ [AutoEvalColumn.dummy.name],
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datatype=TYPES,
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max_rows=None,
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elem_id="leaderboard-table",
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@@ -429,7 +223,7 @@ with demo:
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# Dummy leaderboard for handling the case when the user uses backspace key
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hidden_leaderboard_table_for_search = gr.components.Dataframe(
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value=original_df,
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headers=COLS,
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datatype=TYPES,
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max_rows=None,
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@@ -519,7 +313,8 @@ with demo:
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queue=True,
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)
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521 |
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522 |
-
# with gr.TabItem("📈
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# with gr.Row():
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# with gr.Column():
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# chart = create_metric_plot_obj(
|
@@ -589,12 +384,7 @@ with demo:
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revision_name_textbox = gr.Textbox(label="revision", placeholder="main")
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private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
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model_type = gr.Dropdown(
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-
choices=[
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ModelType.PT.to_str(" : "),
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ModelType.FT.to_str(" : "),
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ModelType.IFT.to_str(" : "),
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ModelType.RL.to_str(" : "),
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-
],
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label="Model type",
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multiselect=False,
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value=None,
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|
1 |
import json
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2 |
import os
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3 |
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4 |
import gradio as gr
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5 |
import pandas as pd
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6 |
from apscheduler.schedulers.background import BackgroundScheduler
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7 |
+
from huggingface_hub import snapshot_download
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8 |
+
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9 |
+
from src.display.utils import (
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10 |
+
COLS,
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11 |
+
TYPES,
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12 |
+
BENCHMARK_COLS,
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13 |
+
EVAL_COLS,
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14 |
+
EVAL_TYPES,
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15 |
+
AutoEvalColumn,
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16 |
+
ModelType,
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17 |
+
NUMERIC_INTERVALS,
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18 |
+
fields,
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19 |
+
)
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20 |
+
from src.display.css_html_js import custom_css, get_window_url_params
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21 |
+
from src.display.about import (
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22 |
CITATION_BUTTON_LABEL,
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23 |
CITATION_BUTTON_TEXT,
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24 |
EVALUATION_QUEUE_TEXT,
|
|
|
26 |
LLM_BENCHMARKS_TEXT,
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27 |
TITLE,
|
28 |
)
|
29 |
+
from src.tools.plots import (
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30 |
create_metric_plot_obj,
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31 |
create_scores_df,
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32 |
create_plot_df,
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33 |
join_model_info_with_results,
|
34 |
HUMAN_BASELINES,
|
35 |
)
|
36 |
+
from src.tools.collections import update_collections
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37 |
+
from src.populate import get_evaluation_queue_df, get_leaderboard_df
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38 |
+
from src.envs import H4_TOKEN, QUEUE_REPO, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, RESULTS_REPO, API, REPO_ID, IS_PUBLIC
|
39 |
+
from src.submission.submit import add_new_eval
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40 |
|
41 |
|
42 |
def restart_space():
|
43 |
+
API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
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44 |
+
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|
45 |
|
46 |
try:
|
47 |
+
snapshot_download(
|
48 |
+
repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
|
49 |
+
)
|
50 |
except Exception:
|
51 |
restart_space()
|
52 |
try:
|
53 |
+
snapshot_download(
|
54 |
+
repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30
|
55 |
+
)
|
56 |
except Exception:
|
57 |
restart_space()
|
58 |
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|
59 |
|
60 |
original_df = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, BENCHMARK_COLS)
|
61 |
+
#update_collections(original_df.copy())
|
62 |
leaderboard_df = original_df.copy()
|
63 |
|
64 |
+
#models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard
|
65 |
+
# plot_df = create_plot_df(create_scores_df(join_model_info_with_results(original_df)))
|
66 |
+
#to_be_dumped = f"models = {repr(models)}\n"
|
67 |
|
68 |
(
|
69 |
finished_eval_queue_df,
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|
72 |
) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
|
73 |
|
74 |
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|
75 |
# Basics
|
76 |
def change_tab(query_param: str):
|
77 |
query_param = query_param.replace("'", '"')
|
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|
115 |
return filtered_df
|
116 |
|
117 |
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|
118 |
def filter_queries(query: str, filtered_df: pd.DataFrame):
|
119 |
"""Added by Abishek"""
|
120 |
final_df = []
|
|
|
142 |
if show_deleted:
|
143 |
filtered_df = df
|
144 |
else: # Show only still on the hub models
|
145 |
+
filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
|
146 |
|
147 |
type_emoji = [t[0] for t in type_query]
|
148 |
filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
|
|
|
173 |
)
|
174 |
with gr.Row():
|
175 |
shown_columns = gr.CheckboxGroup(
|
176 |
+
choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden and not c.dummy],
|
177 |
+
value=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden and not c.never_hidden],
|
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|
178 |
label="Select columns to show",
|
179 |
elem_id="column-select",
|
180 |
interactive=True,
|
181 |
)
|
182 |
with gr.Row():
|
183 |
deleted_models_visibility = gr.Checkbox(
|
184 |
+
value=False, label="Show gated/private/deleted models", interactive=True
|
185 |
)
|
186 |
with gr.Column(min_width=320):
|
187 |
with gr.Box(elem_id="box-filter"):
|
188 |
filter_columns_type = gr.CheckboxGroup(
|
189 |
label="Model types",
|
190 |
+
choices=[t.to_str() for t in ModelType],
|
191 |
+
value=[t.to_str() for t in ModelType],
|
|
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|
192 |
interactive=True,
|
193 |
elem_id="filter-columns-type",
|
194 |
)
|
|
|
209 |
|
210 |
leaderboard_table = gr.components.Dataframe(
|
211 |
value=leaderboard_df[
|
212 |
+
[c.name for c in fields(AutoEvalColumn) if c.never_hidden]
|
213 |
+ shown_columns.value
|
214 |
+ [AutoEvalColumn.dummy.name]
|
215 |
],
|
216 |
+
headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
|
|
|
|
|
|
|
|
|
|
|
217 |
datatype=TYPES,
|
218 |
max_rows=None,
|
219 |
elem_id="leaderboard-table",
|
|
|
223 |
|
224 |
# Dummy leaderboard for handling the case when the user uses backspace key
|
225 |
hidden_leaderboard_table_for_search = gr.components.Dataframe(
|
226 |
+
value=original_df[COLS],
|
227 |
headers=COLS,
|
228 |
datatype=TYPES,
|
229 |
max_rows=None,
|
|
|
313 |
queue=True,
|
314 |
)
|
315 |
|
316 |
+
# with gr.TabItem("📈
|
317 |
+
# evolution through time", elem_id="llm-benchmark-tab-table", id=4):
|
318 |
# with gr.Row():
|
319 |
# with gr.Column():
|
320 |
# chart = create_metric_plot_obj(
|
|
|
384 |
revision_name_textbox = gr.Textbox(label="revision", placeholder="main")
|
385 |
private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
|
386 |
model_type = gr.Dropdown(
|
387 |
+
choices=[t.to_str(" : ") for t in ModelType],
|
|
|
|
|
|
|
|
|
|
|
388 |
label="Model type",
|
389 |
multiselect=False,
|
390 |
value=None,
|
model_info_cache.pkl
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:15ee9a3cdd3ffdfa4d46497b829fbb43ea5a66222a17d34dfef5ad1111a8eb18
|
3 |
-
size 3789941
|
|
|
|
|
|
|
|
model_size_cache.pkl
DELETED
@@ -1,3 +0,0 @@
|
|
1 |
-
version https://git-lfs.github.com/spec/v1
|
2 |
-
oid sha256:ace7167a258f711fa7ffeaadddc6ebef8ccb92651dce8b805228c2f18c988958
|
3 |
-
size 75324
|
|
|
|
|
|
|
|
scripts/create_request_file.py
CHANGED
@@ -10,10 +10,11 @@ import pprint
|
|
10 |
EVAL_REQUESTS_PATH = "eval-queue"
|
11 |
QUEUE_REPO = "open-llm-leaderboard/requests"
|
12 |
|
13 |
-
precisions =("float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)", "GPTQ")
|
14 |
model_types = ("pretrained", "fine-tuned", "RL-tuned", "instruction-tuned")
|
15 |
weight_types = ("Original", "Delta", "Adapter")
|
16 |
|
|
|
17 |
def get_model_size(model_info, precision: str):
|
18 |
size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
|
19 |
try:
|
@@ -24,12 +25,13 @@ def get_model_size(model_info, precision: str):
|
|
24 |
model_size = size_match.group(0)
|
25 |
model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
|
26 |
except AttributeError:
|
27 |
-
return 0
|
28 |
|
29 |
size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
|
30 |
model_size = size_factor * model_size
|
31 |
return model_size
|
32 |
|
|
|
33 |
def main():
|
34 |
api = HfApi()
|
35 |
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
|
@@ -49,7 +51,7 @@ def main():
|
|
49 |
print(f"{Fore.RED}Could not find model info for {model_name} on the Hub\n{e}{Fore.RESET}")
|
50 |
return 1
|
51 |
|
52 |
-
model_size = get_model_size(model_info=model_info
|
53 |
|
54 |
try:
|
55 |
license = model_info.cardData["license"]
|
@@ -98,7 +100,7 @@ def main():
|
|
98 |
)
|
99 |
else:
|
100 |
click.echo("aborting...")
|
101 |
-
|
102 |
|
103 |
-
|
104 |
-
|
|
|
|
10 |
EVAL_REQUESTS_PATH = "eval-queue"
|
11 |
QUEUE_REPO = "open-llm-leaderboard/requests"
|
12 |
|
13 |
+
precisions = ("float16", "bfloat16", "8bit (LLM.int8)", "4bit (QLoRA / FP4)", "GPTQ")
|
14 |
model_types = ("pretrained", "fine-tuned", "RL-tuned", "instruction-tuned")
|
15 |
weight_types = ("Original", "Delta", "Adapter")
|
16 |
|
17 |
+
|
18 |
def get_model_size(model_info, precision: str):
|
19 |
size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
|
20 |
try:
|
|
|
25 |
model_size = size_match.group(0)
|
26 |
model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
|
27 |
except AttributeError:
|
28 |
+
return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
|
29 |
|
30 |
size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
|
31 |
model_size = size_factor * model_size
|
32 |
return model_size
|
33 |
|
34 |
+
|
35 |
def main():
|
36 |
api = HfApi()
|
37 |
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
|
|
|
51 |
print(f"{Fore.RED}Could not find model info for {model_name} on the Hub\n{e}{Fore.RESET}")
|
52 |
return 1
|
53 |
|
54 |
+
model_size = get_model_size(model_info=model_info, precision=precision)
|
55 |
|
56 |
try:
|
57 |
license = model_info.cardData["license"]
|
|
|
100 |
)
|
101 |
else:
|
102 |
click.echo("aborting...")
|
|
|
103 |
|
104 |
+
|
105 |
+
if __name__ == "__main__":
|
106 |
+
main()
|
src/assets/hardcoded_evals.py
DELETED
@@ -1,43 +0,0 @@
|
|
1 |
-
from src.get_model_info.utils import AutoEvalColumn, model_hyperlink
|
2 |
-
|
3 |
-
gpt4_values = {
|
4 |
-
AutoEvalColumn.model.name: model_hyperlink("https://arxiv.org/abs/2303.08774", "gpt4"),
|
5 |
-
AutoEvalColumn.revision.name: "tech report",
|
6 |
-
AutoEvalColumn.precision.name: None,
|
7 |
-
AutoEvalColumn.average.name: 84.3,
|
8 |
-
AutoEvalColumn.arc.name: 96.3,
|
9 |
-
AutoEvalColumn.hellaswag.name: 95.3,
|
10 |
-
AutoEvalColumn.mmlu.name: 86.4,
|
11 |
-
AutoEvalColumn.truthfulqa.name: 59.0,
|
12 |
-
AutoEvalColumn.dummy.name: "GPT-4",
|
13 |
-
AutoEvalColumn.model_type.name: "",
|
14 |
-
}
|
15 |
-
|
16 |
-
gpt35_values = {
|
17 |
-
AutoEvalColumn.model.name: model_hyperlink("https://arxiv.org/abs/2303.08774", "gpt3.5"),
|
18 |
-
AutoEvalColumn.revision.name: "tech report",
|
19 |
-
AutoEvalColumn.precision.name: None,
|
20 |
-
AutoEvalColumn.average.name: 71.9,
|
21 |
-
AutoEvalColumn.arc.name: 85.2,
|
22 |
-
AutoEvalColumn.hellaswag.name: 85.5,
|
23 |
-
AutoEvalColumn.mmlu.name: 70.0,
|
24 |
-
AutoEvalColumn.truthfulqa.name: 47.0,
|
25 |
-
AutoEvalColumn.dummy.name: "GPT-3.5",
|
26 |
-
AutoEvalColumn.model_type.name: "",
|
27 |
-
}
|
28 |
-
|
29 |
-
baseline = {
|
30 |
-
AutoEvalColumn.model.name: "<p>Baseline</p>",
|
31 |
-
AutoEvalColumn.revision.name: "N/A",
|
32 |
-
AutoEvalColumn.precision.name: None,
|
33 |
-
AutoEvalColumn.average.name: 25.0,
|
34 |
-
AutoEvalColumn.arc.name: 25.0,
|
35 |
-
AutoEvalColumn.hellaswag.name: 25.0,
|
36 |
-
AutoEvalColumn.mmlu.name: 25.0,
|
37 |
-
AutoEvalColumn.truthfulqa.name: 25.0,
|
38 |
-
AutoEvalColumn.winogrande.name: 50.0,
|
39 |
-
AutoEvalColumn.gsm8k.name: 0.21,
|
40 |
-
AutoEvalColumn.drop.name: 0.47,
|
41 |
-
AutoEvalColumn.dummy.name: "baseline",
|
42 |
-
AutoEvalColumn.model_type.name: "",
|
43 |
-
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
src/assets/scale-hf-logo.png
DELETED
Git LFS Details
|
src/{assets/text_content.py → display/about.py}
RENAMED
@@ -1,4 +1,4 @@
|
|
1 |
-
from src.
|
2 |
|
3 |
TITLE = """<h1 align="center" id="space-title">🤗 Open LLM Leaderboard</h1>"""
|
4 |
|
@@ -42,7 +42,7 @@ We chose these benchmarks as they test a variety of reasoning and general knowle
|
|
42 |
## Details and logs
|
43 |
You can find:
|
44 |
- detailed numerical results in the `results` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/results
|
45 |
-
- details on the input/outputs for the models in the `details`
|
46 |
- community queries and running status in the `requests` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/requests
|
47 |
|
48 |
## Reproducibility
|
|
|
1 |
+
from src.display.utils import ModelType
|
2 |
|
3 |
TITLE = """<h1 align="center" id="space-title">🤗 Open LLM Leaderboard</h1>"""
|
4 |
|
|
|
42 |
## Details and logs
|
43 |
You can find:
|
44 |
- detailed numerical results in the `results` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/results
|
45 |
+
- details on the input/outputs for the models in the `details` of each model, that you can access by clicking the 📄 emoji after the model name
|
46 |
- community queries and running status in the `requests` Hugging Face dataset: https://huggingface.co/datasets/open-llm-leaderboard/requests
|
47 |
|
48 |
## Reproducibility
|
src/{assets → display}/css_html_js.py
RENAMED
File without changes
|
src/{get_model_info/utils.py → display/formatting.py}
RENAMED
@@ -1,68 +1,8 @@
|
|
1 |
import os
|
2 |
-
from dataclasses import dataclass
|
3 |
-
|
4 |
from huggingface_hub import HfApi
|
5 |
|
6 |
API = HfApi()
|
7 |
|
8 |
-
|
9 |
-
# These classes are for user facing column names, to avoid having to change them
|
10 |
-
# all around the code when a modif is needed
|
11 |
-
@dataclass
|
12 |
-
class ColumnContent:
|
13 |
-
name: str
|
14 |
-
type: str
|
15 |
-
displayed_by_default: bool
|
16 |
-
hidden: bool = False
|
17 |
-
|
18 |
-
|
19 |
-
def fields(raw_class):
|
20 |
-
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
|
21 |
-
|
22 |
-
|
23 |
-
@dataclass(frozen=True)
|
24 |
-
class AutoEvalColumn: # Auto evals column
|
25 |
-
model_type_symbol = ColumnContent("T", "str", True)
|
26 |
-
model = ColumnContent("Model", "markdown", True)
|
27 |
-
average = ColumnContent("Average ⬆️", "number", True)
|
28 |
-
arc = ColumnContent("ARC", "number", True)
|
29 |
-
hellaswag = ColumnContent("HellaSwag", "number", True)
|
30 |
-
mmlu = ColumnContent("MMLU", "number", True)
|
31 |
-
truthfulqa = ColumnContent("TruthfulQA", "number", True)
|
32 |
-
winogrande = ColumnContent("Winogrande", "number", True)
|
33 |
-
gsm8k = ColumnContent("GSM8K", "number", True)
|
34 |
-
drop = ColumnContent("DROP", "number", True)
|
35 |
-
model_type = ColumnContent("Type", "str", False)
|
36 |
-
precision = ColumnContent("Precision", "str", False) # , True)
|
37 |
-
license = ColumnContent("Hub License", "str", False)
|
38 |
-
params = ColumnContent("#Params (B)", "number", False)
|
39 |
-
likes = ColumnContent("Hub ❤️", "number", False)
|
40 |
-
still_on_hub = ColumnContent("Available on the hub", "bool", False)
|
41 |
-
revision = ColumnContent("Model sha", "str", False, False)
|
42 |
-
dummy = ColumnContent(
|
43 |
-
"model_name_for_query", "str", True
|
44 |
-
) # dummy col to implement search bar (hidden by custom CSS)
|
45 |
-
|
46 |
-
|
47 |
-
@dataclass(frozen=True)
|
48 |
-
class EloEvalColumn: # Elo evals column
|
49 |
-
model = ColumnContent("Model", "markdown", True)
|
50 |
-
gpt4 = ColumnContent("GPT-4 (all)", "number", True)
|
51 |
-
human_all = ColumnContent("Human (all)", "number", True)
|
52 |
-
human_instruct = ColumnContent("Human (instruct)", "number", True)
|
53 |
-
human_code_instruct = ColumnContent("Human (code-instruct)", "number", True)
|
54 |
-
|
55 |
-
|
56 |
-
@dataclass(frozen=True)
|
57 |
-
class EvalQueueColumn: # Queue column
|
58 |
-
model = ColumnContent("model", "markdown", True)
|
59 |
-
revision = ColumnContent("revision", "str", True)
|
60 |
-
private = ColumnContent("private", "bool", True)
|
61 |
-
precision = ColumnContent("precision", "str", True)
|
62 |
-
weight_type = ColumnContent("weight_type", "str", "Original")
|
63 |
-
status = ColumnContent("status", "str", True)
|
64 |
-
|
65 |
-
|
66 |
LLAMAS = [
|
67 |
"huggingface/llama-7b",
|
68 |
"huggingface/llama-13b",
|
@@ -70,7 +10,6 @@ LLAMAS = [
|
|
70 |
"huggingface/llama-65b",
|
71 |
]
|
72 |
|
73 |
-
|
74 |
KOALA_LINK = "https://huggingface.co/TheBloke/koala-13B-HF"
|
75 |
VICUNA_LINK = "https://huggingface.co/lmsys/vicuna-13b-delta-v1.1"
|
76 |
OASST_LINK = "https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
|
|
|
1 |
import os
|
|
|
|
|
2 |
from huggingface_hub import HfApi
|
3 |
|
4 |
API = HfApi()
|
5 |
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
|
|
|
|
6 |
LLAMAS = [
|
7 |
"huggingface/llama-7b",
|
8 |
"huggingface/llama-13b",
|
|
|
10 |
"huggingface/llama-65b",
|
11 |
]
|
12 |
|
|
|
13 |
KOALA_LINK = "https://huggingface.co/TheBloke/koala-13B-HF"
|
14 |
VICUNA_LINK = "https://huggingface.co/lmsys/vicuna-13b-delta-v1.1"
|
15 |
OASST_LINK = "https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
|
src/display/utils.py
ADDED
@@ -0,0 +1,141 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
from dataclasses import dataclass
|
2 |
+
import pandas as pd
|
3 |
+
from enum import Enum
|
4 |
+
|
5 |
+
|
6 |
+
# These classes are for user facing column names,
|
7 |
+
# to avoid having to change them all around the code
|
8 |
+
# when a modif is needed
|
9 |
+
@dataclass
|
10 |
+
class ColumnContent:
|
11 |
+
name: str
|
12 |
+
type: str
|
13 |
+
displayed_by_default: bool
|
14 |
+
hidden: bool = False
|
15 |
+
never_hidden: bool = False
|
16 |
+
dummy: bool = False
|
17 |
+
|
18 |
+
|
19 |
+
def fields(raw_class):
|
20 |
+
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
|
21 |
+
|
22 |
+
|
23 |
+
@dataclass(frozen=True)
|
24 |
+
class AutoEvalColumn: # Auto evals column
|
25 |
+
model_type_symbol = ColumnContent("T", "str", True, never_hidden=True)
|
26 |
+
model = ColumnContent("Model", "markdown", True, never_hidden=True)
|
27 |
+
average = ColumnContent("Average ⬆️", "number", True)
|
28 |
+
arc = ColumnContent("ARC", "number", True)
|
29 |
+
hellaswag = ColumnContent("HellaSwag", "number", True)
|
30 |
+
mmlu = ColumnContent("MMLU", "number", True)
|
31 |
+
truthfulqa = ColumnContent("TruthfulQA", "number", True)
|
32 |
+
winogrande = ColumnContent("Winogrande", "number", True)
|
33 |
+
gsm8k = ColumnContent("GSM8K", "number", True)
|
34 |
+
drop = ColumnContent("DROP", "number", True)
|
35 |
+
model_type = ColumnContent("Type", "str", False)
|
36 |
+
weight_type = ColumnContent("Weight type", "str", False, True)
|
37 |
+
precision = ColumnContent("Precision", "str", False) # , True)
|
38 |
+
license = ColumnContent("Hub License", "str", False)
|
39 |
+
params = ColumnContent("#Params (B)", "number", False)
|
40 |
+
likes = ColumnContent("Hub ❤️", "number", False)
|
41 |
+
still_on_hub = ColumnContent("Available on the hub", "bool", False)
|
42 |
+
revision = ColumnContent("Model sha", "str", False, False)
|
43 |
+
dummy = ColumnContent(
|
44 |
+
"model_name_for_query", "str", False, dummy=True
|
45 |
+
) # dummy col to implement search bar (hidden by custom CSS)
|
46 |
+
|
47 |
+
|
48 |
+
@dataclass(frozen=True)
|
49 |
+
class EvalQueueColumn: # Queue column
|
50 |
+
model = ColumnContent("model", "markdown", True)
|
51 |
+
revision = ColumnContent("revision", "str", True)
|
52 |
+
private = ColumnContent("private", "bool", True)
|
53 |
+
precision = ColumnContent("precision", "str", True)
|
54 |
+
weight_type = ColumnContent("weight_type", "str", "Original")
|
55 |
+
status = ColumnContent("status", "str", True)
|
56 |
+
|
57 |
+
|
58 |
+
baseline_row = {
|
59 |
+
AutoEvalColumn.model.name: "<p>Baseline</p>",
|
60 |
+
AutoEvalColumn.revision.name: "N/A",
|
61 |
+
AutoEvalColumn.precision.name: None,
|
62 |
+
AutoEvalColumn.average.name: 25.0,
|
63 |
+
AutoEvalColumn.arc.name: 25.0,
|
64 |
+
AutoEvalColumn.hellaswag.name: 25.0,
|
65 |
+
AutoEvalColumn.mmlu.name: 25.0,
|
66 |
+
AutoEvalColumn.truthfulqa.name: 25.0,
|
67 |
+
AutoEvalColumn.winogrande.name: 50.0,
|
68 |
+
AutoEvalColumn.gsm8k.name: 0.21,
|
69 |
+
AutoEvalColumn.drop.name: 0.47,
|
70 |
+
AutoEvalColumn.dummy.name: "baseline",
|
71 |
+
AutoEvalColumn.model_type.name: "",
|
72 |
+
}
|
73 |
+
|
74 |
+
|
75 |
+
@dataclass
|
76 |
+
class ModelInfo:
|
77 |
+
name: str
|
78 |
+
symbol: str # emoji
|
79 |
+
|
80 |
+
|
81 |
+
class ModelType(Enum):
|
82 |
+
PT = ModelInfo(name="pretrained", symbol="🟢")
|
83 |
+
FT = ModelInfo(name="fine-tuned", symbol="🔶")
|
84 |
+
IFT = ModelInfo(name="instruction-tuned", symbol="⭕")
|
85 |
+
RL = ModelInfo(name="RL-tuned", symbol="🟦")
|
86 |
+
Unknown = ModelInfo(name="", symbol="?")
|
87 |
+
|
88 |
+
def to_str(self, separator=" "):
|
89 |
+
return f"{self.value.symbol}{separator}{self.value.name}"
|
90 |
+
|
91 |
+
@staticmethod
|
92 |
+
def from_str(type):
|
93 |
+
if "fine-tuned" in type or "🔶" in type:
|
94 |
+
return ModelType.FT
|
95 |
+
if "pretrained" in type or "🟢" in type:
|
96 |
+
return ModelType.PT
|
97 |
+
if "RL-tuned" in type or "🟦" in type:
|
98 |
+
return ModelType.RL
|
99 |
+
if "instruction-tuned" in type or "⭕" in type:
|
100 |
+
return ModelType.IFT
|
101 |
+
return ModelType.Unknown
|
102 |
+
|
103 |
+
|
104 |
+
@dataclass
|
105 |
+
class Task:
|
106 |
+
benchmark: str
|
107 |
+
metric: str
|
108 |
+
col_name: str
|
109 |
+
|
110 |
+
|
111 |
+
class Tasks(Enum):
|
112 |
+
arc = Task("arc:challenge", "acc_norm", AutoEvalColumn.arc.name)
|
113 |
+
hellaswag = Task("hellaswag", "acc_norm", AutoEvalColumn.hellaswag.name)
|
114 |
+
mmlu = Task("hendrycksTest", "acc", AutoEvalColumn.mmlu.name)
|
115 |
+
truthfulqa = Task("truthfulqa:mc", "mc2", AutoEvalColumn.truthfulqa.name)
|
116 |
+
winogrande = Task("winogrande", "acc", AutoEvalColumn.winogrande.name)
|
117 |
+
gsm8k = Task("gsm8k", "acc", AutoEvalColumn.gsm8k.name)
|
118 |
+
drop = Task("drop", "f1", AutoEvalColumn.drop.name)
|
119 |
+
|
120 |
+
|
121 |
+
# Column selection
|
122 |
+
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
|
123 |
+
TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
|
124 |
+
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
|
125 |
+
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
|
126 |
+
|
127 |
+
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
|
128 |
+
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
|
129 |
+
|
130 |
+
BENCHMARK_COLS = [t.value.col_name for t in Tasks if t.value.col_name in fields(AutoEvalColumn)]
|
131 |
+
|
132 |
+
NUMERIC_INTERVALS = {
|
133 |
+
"?": pd.Interval(-1, 0, closed="right"),
|
134 |
+
"~1.5": pd.Interval(0, 2, closed="right"),
|
135 |
+
"~3": pd.Interval(2, 4, closed="right"),
|
136 |
+
"~7": pd.Interval(4, 9, closed="right"),
|
137 |
+
"~13": pd.Interval(9, 20, closed="right"),
|
138 |
+
"~35": pd.Interval(20, 45, closed="right"),
|
139 |
+
"~60": pd.Interval(45, 70, closed="right"),
|
140 |
+
"70+": pd.Interval(70, 10000, closed="right"),
|
141 |
+
}
|
src/envs.py
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
from huggingface_hub import HfApi
|
3 |
+
|
4 |
+
# clone / pull the lmeh eval data
|
5 |
+
H4_TOKEN = os.environ.get("H4_TOKEN", None)
|
6 |
+
|
7 |
+
REPO_ID = "HuggingFaceH4/open_llm_leaderboard"
|
8 |
+
QUEUE_REPO = "open-llm-leaderboard/requests"
|
9 |
+
RESULTS_REPO = "open-llm-leaderboard/results"
|
10 |
+
|
11 |
+
PRIVATE_QUEUE_REPO = "open-llm-leaderboard/private-requests"
|
12 |
+
PRIVATE_RESULTS_REPO = "open-llm-leaderboard/private-results"
|
13 |
+
|
14 |
+
IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
|
15 |
+
|
16 |
+
EVAL_REQUESTS_PATH = "eval-queue"
|
17 |
+
EVAL_RESULTS_PATH = "eval-results"
|
18 |
+
|
19 |
+
EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
|
20 |
+
EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"
|
21 |
+
|
22 |
+
PATH_TO_COLLECTION = "open-llm-leaderboard/llm-leaderboard-best-models-652d6c7965a4619fb5c27a03"
|
23 |
+
|
24 |
+
# Rate limit variables
|
25 |
+
RATE_LIMIT_PERIOD = 7
|
26 |
+
RATE_LIMIT_QUOTA = 5
|
27 |
+
|
28 |
+
API = HfApi(token=H4_TOKEN)
|
src/get_model_info/apply_metadata_to_df.py
DELETED
@@ -1,95 +0,0 @@
|
|
1 |
-
import glob
|
2 |
-
import json
|
3 |
-
import os
|
4 |
-
from typing import List
|
5 |
-
|
6 |
-
from huggingface_hub import HfApi
|
7 |
-
from tqdm import tqdm
|
8 |
-
|
9 |
-
from src.get_model_info.hardocded_metadata.flags import DO_NOT_SUBMIT_MODELS, FLAGGED_MODELS
|
10 |
-
from src.get_model_info.hardocded_metadata.types import MODEL_TYPE_METADATA, ModelType, model_type_from_str
|
11 |
-
from src.get_model_info.utils import AutoEvalColumn, model_hyperlink
|
12 |
-
|
13 |
-
api = HfApi(token=os.environ.get("H4_TOKEN", None))
|
14 |
-
|
15 |
-
|
16 |
-
def get_model_metadata(leaderboard_data: List[dict]):
|
17 |
-
for model_data in tqdm(leaderboard_data):
|
18 |
-
request_files = os.path.join(
|
19 |
-
"eval-queue",
|
20 |
-
model_data["model_name_for_query"] + "_eval_request_*" + ".json",
|
21 |
-
)
|
22 |
-
request_files = glob.glob(request_files)
|
23 |
-
|
24 |
-
# Select correct request file (precision)
|
25 |
-
request_file = ""
|
26 |
-
if len(request_files) == 1:
|
27 |
-
request_file = request_files[0]
|
28 |
-
elif len(request_files) > 1:
|
29 |
-
request_files = sorted(request_files, reverse=True)
|
30 |
-
for tmp_request_file in request_files:
|
31 |
-
with open(tmp_request_file, "r") as f:
|
32 |
-
req_content = json.load(f)
|
33 |
-
if (
|
34 |
-
req_content["status"] in ["FINISHED", "PENDING_NEW_EVAL"]
|
35 |
-
and req_content["precision"] == model_data["Precision"].split(".")[-1]
|
36 |
-
):
|
37 |
-
request_file = tmp_request_file
|
38 |
-
|
39 |
-
try:
|
40 |
-
with open(request_file, "r") as f:
|
41 |
-
request = json.load(f)
|
42 |
-
model_type = model_type_from_str(request.get("model_type", ""))
|
43 |
-
model_data[AutoEvalColumn.model_type.name] = model_type.value.name
|
44 |
-
model_data[AutoEvalColumn.model_type_symbol.name] = model_type.value.symbol # + ("🔺" if is_delta else "")
|
45 |
-
model_data[AutoEvalColumn.license.name] = request.get("license", "?")
|
46 |
-
model_data[AutoEvalColumn.likes.name] = request.get("likes", 0)
|
47 |
-
model_data[AutoEvalColumn.params.name] = request.get("params", 0)
|
48 |
-
except Exception:
|
49 |
-
print(f"Could not find request file for {model_data['model_name_for_query']}")
|
50 |
-
|
51 |
-
if model_data["model_name_for_query"] in MODEL_TYPE_METADATA:
|
52 |
-
model_data[AutoEvalColumn.model_type.name] = MODEL_TYPE_METADATA[
|
53 |
-
model_data["model_name_for_query"]
|
54 |
-
].value.name
|
55 |
-
model_data[AutoEvalColumn.model_type_symbol.name] = MODEL_TYPE_METADATA[
|
56 |
-
model_data["model_name_for_query"]
|
57 |
-
].value.symbol # + ("🔺" if is_delta else "")
|
58 |
-
else:
|
59 |
-
model_data[AutoEvalColumn.model_type.name] = ModelType.Unknown.value.name
|
60 |
-
model_data[AutoEvalColumn.model_type_symbol.name] = ModelType.Unknown.value.symbol
|
61 |
-
|
62 |
-
# if we cannot find a request file, set license and likes to unknown
|
63 |
-
model_data[AutoEvalColumn.license.name] = "?"
|
64 |
-
model_data[AutoEvalColumn.likes.name] = 0
|
65 |
-
model_data[AutoEvalColumn.params.name] = 0
|
66 |
-
|
67 |
-
|
68 |
-
def flag_models(leaderboard_data: List[dict]):
|
69 |
-
for model_data in leaderboard_data:
|
70 |
-
if model_data["model_name_for_query"] in FLAGGED_MODELS:
|
71 |
-
issue_num = FLAGGED_MODELS[model_data["model_name_for_query"]].split("/")[-1]
|
72 |
-
issue_link = model_hyperlink(
|
73 |
-
FLAGGED_MODELS[model_data["model_name_for_query"]],
|
74 |
-
f"See discussion #{issue_num}",
|
75 |
-
)
|
76 |
-
model_data[
|
77 |
-
AutoEvalColumn.model.name
|
78 |
-
] = f"{model_data[AutoEvalColumn.model.name]} has been flagged! {issue_link}"
|
79 |
-
|
80 |
-
|
81 |
-
def remove_forbidden_models(leaderboard_data: List[dict]):
|
82 |
-
indices_to_remove = []
|
83 |
-
for ix, model in enumerate(leaderboard_data):
|
84 |
-
if model["model_name_for_query"] in DO_NOT_SUBMIT_MODELS:
|
85 |
-
indices_to_remove.append(ix)
|
86 |
-
|
87 |
-
for ix in reversed(indices_to_remove):
|
88 |
-
leaderboard_data.pop(ix)
|
89 |
-
return leaderboard_data
|
90 |
-
|
91 |
-
|
92 |
-
def apply_metadata(leaderboard_data: List[dict]):
|
93 |
-
leaderboard_data = remove_forbidden_models(leaderboard_data)
|
94 |
-
get_model_metadata(leaderboard_data)
|
95 |
-
flag_models(leaderboard_data)
|
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src/get_model_info/get_metadata_from_hub.py
DELETED
@@ -1,19 +0,0 @@
|
|
1 |
-
import re
|
2 |
-
from huggingface_hub.hf_api import ModelInfo
|
3 |
-
|
4 |
-
|
5 |
-
def get_model_size(model_info: ModelInfo, precision: str):
|
6 |
-
size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
|
7 |
-
try:
|
8 |
-
model_size = round(model_info.safetensors["total"] / 1e9, 3)
|
9 |
-
except AttributeError:
|
10 |
-
try:
|
11 |
-
size_match = re.search(size_pattern, model_info.modelId.lower())
|
12 |
-
model_size = size_match.group(0)
|
13 |
-
model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
|
14 |
-
except AttributeError:
|
15 |
-
return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
|
16 |
-
|
17 |
-
size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
|
18 |
-
model_size = size_factor * model_size
|
19 |
-
return model_size
|
|
|
|
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src/get_model_info/hardocded_metadata/types.py
DELETED
@@ -1,555 +0,0 @@
|
|
1 |
-
from dataclasses import dataclass
|
2 |
-
from enum import Enum
|
3 |
-
from typing import Dict
|
4 |
-
|
5 |
-
|
6 |
-
@dataclass
|
7 |
-
class ModelInfo:
|
8 |
-
name: str
|
9 |
-
symbol: str # emoji
|
10 |
-
|
11 |
-
|
12 |
-
class ModelType(Enum):
|
13 |
-
PT = ModelInfo(name="pretrained", symbol="🟢")
|
14 |
-
FT = ModelInfo(name="fine-tuned", symbol="🔶")
|
15 |
-
IFT = ModelInfo(name="instruction-tuned", symbol="⭕")
|
16 |
-
RL = ModelInfo(name="RL-tuned", symbol="🟦")
|
17 |
-
Unknown = ModelInfo(name="", symbol="?")
|
18 |
-
|
19 |
-
def to_str(self, separator=" "):
|
20 |
-
return f"{self.value.symbol}{separator}{self.value.name}"
|
21 |
-
|
22 |
-
|
23 |
-
MODEL_TYPE_METADATA: Dict[str, ModelType] = {
|
24 |
-
"tiiuae/falcon-180B": ModelType.PT,
|
25 |
-
"tiiuae/falcon-180B-chat": ModelType.RL,
|
26 |
-
"microsoft/phi-1_5": ModelType.PT,
|
27 |
-
"Qwen/Qwen-7B": ModelType.PT,
|
28 |
-
"Qwen/Qwen-7B-Chat": ModelType.RL,
|
29 |
-
"notstoic/PygmalionCoT-7b": ModelType.IFT,
|
30 |
-
"aisquared/dlite-v1-355m": ModelType.IFT,
|
31 |
-
"aisquared/dlite-v1-1_5b": ModelType.IFT,
|
32 |
-
"aisquared/dlite-v1-774m": ModelType.IFT,
|
33 |
-
"aisquared/dlite-v1-124m": ModelType.IFT,
|
34 |
-
"aisquared/chopt-2_7b": ModelType.IFT,
|
35 |
-
"aisquared/dlite-v2-124m": ModelType.IFT,
|
36 |
-
"aisquared/dlite-v2-774m": ModelType.IFT,
|
37 |
-
"aisquared/dlite-v2-1_5b": ModelType.IFT,
|
38 |
-
"aisquared/chopt-1_3b": ModelType.IFT,
|
39 |
-
"aisquared/dlite-v2-355m": ModelType.IFT,
|
40 |
-
"augtoma/qCammel-13": ModelType.IFT,
|
41 |
-
"Aspik101/Llama-2-7b-hf-instruct-pl-lora_unload": ModelType.IFT,
|
42 |
-
"Aspik101/vicuna-7b-v1.3-instruct-pl-lora_unload": ModelType.IFT,
|
43 |
-
"TheBloke/alpaca-lora-65B-HF": ModelType.FT,
|
44 |
-
"TheBloke/tulu-7B-fp16": ModelType.IFT,
|
45 |
-
"TheBloke/guanaco-7B-HF": ModelType.FT,
|
46 |
-
"TheBloke/koala-7B-HF": ModelType.FT,
|
47 |
-
"TheBloke/wizardLM-7B-HF": ModelType.IFT,
|
48 |
-
"TheBloke/airoboros-13B-HF": ModelType.IFT,
|
49 |
-
"TheBloke/koala-13B-HF": ModelType.FT,
|
50 |
-
"TheBloke/Wizard-Vicuna-7B-Uncensored-HF": ModelType.FT,
|
51 |
-
"TheBloke/dromedary-65b-lora-HF": ModelType.IFT,
|
52 |
-
"TheBloke/wizardLM-13B-1.0-fp16": ModelType.IFT,
|
53 |
-
"TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-fp16": ModelType.FT,
|
54 |
-
"TheBloke/Wizard-Vicuna-30B-Uncensored-fp16": ModelType.FT,
|
55 |
-
"TheBloke/wizard-vicuna-13B-HF": ModelType.IFT,
|
56 |
-
"TheBloke/UltraLM-13B-fp16": ModelType.IFT,
|
57 |
-
"TheBloke/OpenAssistant-FT-7-Llama-30B-HF": ModelType.FT,
|
58 |
-
"TheBloke/vicuna-13B-1.1-HF": ModelType.IFT,
|
59 |
-
"TheBloke/guanaco-13B-HF": ModelType.FT,
|
60 |
-
"TheBloke/guanaco-65B-HF": ModelType.FT,
|
61 |
-
"TheBloke/airoboros-7b-gpt4-fp16": ModelType.IFT,
|
62 |
-
"TheBloke/llama-30b-supercot-SuperHOT-8K-fp16": ModelType.IFT,
|
63 |
-
"TheBloke/Llama-2-13B-fp16": ModelType.PT,
|
64 |
-
"TheBloke/llama-2-70b-Guanaco-QLoRA-fp16": ModelType.FT,
|
65 |
-
"TheBloke/landmark-attention-llama7b-fp16": ModelType.IFT,
|
66 |
-
"TheBloke/Planner-7B-fp16": ModelType.IFT,
|
67 |
-
"TheBloke/Wizard-Vicuna-13B-Uncensored-HF": ModelType.FT,
|
68 |
-
"TheBloke/gpt4-alpaca-lora-13B-HF": ModelType.IFT,
|
69 |
-
"TheBloke/gpt4-x-vicuna-13B-HF": ModelType.IFT,
|
70 |
-
"TheBloke/gpt4-alpaca-lora_mlp-65B-HF": ModelType.IFT,
|
71 |
-
"TheBloke/tulu-13B-fp16": ModelType.IFT,
|
72 |
-
"TheBloke/VicUnlocked-alpaca-65B-QLoRA-fp16": ModelType.IFT,
|
73 |
-
"TheBloke/Llama-2-70B-fp16": ModelType.IFT,
|
74 |
-
"TheBloke/WizardLM-30B-fp16": ModelType.IFT,
|
75 |
-
"TheBloke/robin-13B-v2-fp16": ModelType.FT,
|
76 |
-
"TheBloke/robin-33B-v2-fp16": ModelType.FT,
|
77 |
-
"TheBloke/Vicuna-13B-CoT-fp16": ModelType.IFT,
|
78 |
-
"TheBloke/Vicuna-33B-1-3-SuperHOT-8K-fp16": ModelType.IFT,
|
79 |
-
"TheBloke/Wizard-Vicuna-30B-Superhot-8K-fp16": ModelType.FT,
|
80 |
-
"TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16": ModelType.IFT,
|
81 |
-
"TheBloke/GPlatty-30B-SuperHOT-8K-fp16": ModelType.FT,
|
82 |
-
"TheBloke/CAMEL-33B-Combined-Data-SuperHOT-8K-fp16": ModelType.IFT,
|
83 |
-
"TheBloke/Chinese-Alpaca-33B-SuperHOT-8K-fp16": ModelType.IFT,
|
84 |
-
"jphme/orca_mini_v2_ger_7b": ModelType.IFT,
|
85 |
-
"Ejafa/vicuna_7B_vanilla_1.1": ModelType.FT,
|
86 |
-
"kevinpro/Vicuna-13B-CoT": ModelType.IFT,
|
87 |
-
"AlekseyKorshuk/pygmalion-6b-vicuna-chatml": ModelType.FT,
|
88 |
-
"AlekseyKorshuk/chatml-pyg-v1": ModelType.FT,
|
89 |
-
"concedo/Vicuzard-30B-Uncensored": ModelType.FT,
|
90 |
-
"concedo/OPT-19M-ChatSalad": ModelType.FT,
|
91 |
-
"concedo/Pythia-70M-ChatSalad": ModelType.FT,
|
92 |
-
"digitous/13B-HyperMantis": ModelType.IFT,
|
93 |
-
"digitous/Adventien-GPTJ": ModelType.FT,
|
94 |
-
"digitous/Alpacino13b": ModelType.IFT,
|
95 |
-
"digitous/GPT-R": ModelType.IFT,
|
96 |
-
"digitous/Javelin-R": ModelType.IFT,
|
97 |
-
"digitous/Javalion-GPTJ": ModelType.IFT,
|
98 |
-
"digitous/Javalion-R": ModelType.IFT,
|
99 |
-
"digitous/Skegma-GPTJ": ModelType.FT,
|
100 |
-
"digitous/Alpacino30b": ModelType.IFT,
|
101 |
-
"digitous/Janin-GPTJ": ModelType.FT,
|
102 |
-
"digitous/Janin-R": ModelType.FT,
|
103 |
-
"digitous/Javelin-GPTJ": ModelType.FT,
|
104 |
-
"SaylorTwift/gpt2_test": ModelType.PT,
|
105 |
-
"anton-l/gpt-j-tiny-random": ModelType.FT,
|
106 |
-
"Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca": ModelType.FT,
|
107 |
-
"Lazycuber/pyg-instruct-wizardlm": ModelType.FT,
|
108 |
-
"Lazycuber/Janemalion-6B": ModelType.FT,
|
109 |
-
"IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1": ModelType.FT,
|
110 |
-
"IDEA-CCNL/Ziya-LLaMA-13B-v1": ModelType.IFT,
|
111 |
-
"dsvv-cair/alpaca-cleaned-llama-30b-bf16": ModelType.FT,
|
112 |
-
"gpt2-medium": ModelType.PT,
|
113 |
-
"camel-ai/CAMEL-13B-Combined-Data": ModelType.IFT,
|
114 |
-
"camel-ai/CAMEL-13B-Role-Playing-Data": ModelType.FT,
|
115 |
-
"camel-ai/CAMEL-33B-Combined-Data": ModelType.IFT,
|
116 |
-
"PygmalionAI/pygmalion-6b": ModelType.FT,
|
117 |
-
"PygmalionAI/metharme-1.3b": ModelType.IFT,
|
118 |
-
"PygmalionAI/pygmalion-1.3b": ModelType.FT,
|
119 |
-
"PygmalionAI/pygmalion-350m": ModelType.FT,
|
120 |
-
"PygmalionAI/pygmalion-2.7b": ModelType.FT,
|
121 |
-
"medalpaca/medalpaca-7b": ModelType.FT,
|
122 |
-
"lilloukas/Platypus-30B": ModelType.IFT,
|
123 |
-
"lilloukas/GPlatty-30B": ModelType.FT,
|
124 |
-
"mncai/chatdoctor": ModelType.FT,
|
125 |
-
"chaoyi-wu/MedLLaMA_13B": ModelType.FT,
|
126 |
-
"LoupGarou/WizardCoder-Guanaco-15B-V1.0": ModelType.IFT,
|
127 |
-
"LoupGarou/WizardCoder-Guanaco-15B-V1.1": ModelType.FT,
|
128 |
-
"hakurei/instruct-12b": ModelType.IFT,
|
129 |
-
"hakurei/lotus-12B": ModelType.FT,
|
130 |
-
"shibing624/chinese-llama-plus-13b-hf": ModelType.IFT,
|
131 |
-
"shibing624/chinese-alpaca-plus-7b-hf": ModelType.IFT,
|
132 |
-
"shibing624/chinese-alpaca-plus-13b-hf": ModelType.IFT,
|
133 |
-
"mosaicml/mpt-7b-instruct": ModelType.IFT,
|
134 |
-
"mosaicml/mpt-30b-chat": ModelType.IFT,
|
135 |
-
"mosaicml/mpt-7b-storywriter": ModelType.FT,
|
136 |
-
"mosaicml/mpt-30b-instruct": ModelType.IFT,
|
137 |
-
"mosaicml/mpt-7b-chat": ModelType.IFT,
|
138 |
-
"mosaicml/mpt-30b": ModelType.PT,
|
139 |
-
"Corianas/111m": ModelType.IFT,
|
140 |
-
"Corianas/Quokka_1.3b": ModelType.IFT,
|
141 |
-
"Corianas/256_5epoch": ModelType.FT,
|
142 |
-
"Corianas/Quokka_256m": ModelType.IFT,
|
143 |
-
"Corianas/Quokka_590m": ModelType.IFT,
|
144 |
-
"Corianas/gpt-j-6B-Dolly": ModelType.FT,
|
145 |
-
"Corianas/Quokka_2.7b": ModelType.IFT,
|
146 |
-
"cyberagent/open-calm-7b": ModelType.FT,
|
147 |
-
"Aspik101/Nous-Hermes-13b-pl-lora_unload": ModelType.IFT,
|
148 |
-
"THUDM/chatglm2-6b": ModelType.IFT,
|
149 |
-
"MetaIX/GPT4-X-Alpasta-30b": ModelType.IFT,
|
150 |
-
"NYTK/PULI-GPTrio": ModelType.PT,
|
151 |
-
"EleutherAI/pythia-1.3b": ModelType.PT,
|
152 |
-
"EleutherAI/pythia-2.8b-deduped": ModelType.PT,
|
153 |
-
"EleutherAI/gpt-neo-125m": ModelType.PT,
|
154 |
-
"EleutherAI/pythia-160m": ModelType.PT,
|
155 |
-
"EleutherAI/gpt-neo-2.7B": ModelType.PT,
|
156 |
-
"EleutherAI/pythia-1b-deduped": ModelType.PT,
|
157 |
-
"EleutherAI/pythia-6.7b": ModelType.PT,
|
158 |
-
"EleutherAI/pythia-70m-deduped": ModelType.PT,
|
159 |
-
"EleutherAI/gpt-neox-20b": ModelType.PT,
|
160 |
-
"EleutherAI/pythia-1.4b-deduped": ModelType.PT,
|
161 |
-
"EleutherAI/pythia-2.7b": ModelType.PT,
|
162 |
-
"EleutherAI/pythia-6.9b-deduped": ModelType.PT,
|
163 |
-
"EleutherAI/pythia-70m": ModelType.PT,
|
164 |
-
"EleutherAI/gpt-j-6b": ModelType.PT,
|
165 |
-
"EleutherAI/pythia-12b-deduped": ModelType.PT,
|
166 |
-
"EleutherAI/gpt-neo-1.3B": ModelType.PT,
|
167 |
-
"EleutherAI/pythia-410m-deduped": ModelType.PT,
|
168 |
-
"EleutherAI/pythia-160m-deduped": ModelType.PT,
|
169 |
-
"EleutherAI/polyglot-ko-12.8b": ModelType.PT,
|
170 |
-
"EleutherAI/pythia-12b": ModelType.PT,
|
171 |
-
"roneneldan/TinyStories-33M": ModelType.PT,
|
172 |
-
"roneneldan/TinyStories-28M": ModelType.PT,
|
173 |
-
"roneneldan/TinyStories-1M": ModelType.PT,
|
174 |
-
"roneneldan/TinyStories-8M": ModelType.PT,
|
175 |
-
"roneneldan/TinyStories-3M": ModelType.PT,
|
176 |
-
"jerryjalapeno/nart-100k-7b": ModelType.FT,
|
177 |
-
"lmsys/vicuna-13b-v1.3": ModelType.IFT,
|
178 |
-
"lmsys/vicuna-7b-v1.3": ModelType.IFT,
|
179 |
-
"lmsys/vicuna-13b-v1.1": ModelType.IFT,
|
180 |
-
"lmsys/vicuna-13b-delta-v1.1": ModelType.IFT,
|
181 |
-
"lmsys/vicuna-7b-delta-v1.1": ModelType.IFT,
|
182 |
-
"abhiramtirumala/DialoGPT-sarcastic-medium": ModelType.FT,
|
183 |
-
"haonan-li/bactrian-x-llama-13b-merged": ModelType.IFT,
|
184 |
-
"Gryphe/MythoLogic-13b": ModelType.IFT,
|
185 |
-
"Gryphe/MythoBoros-13b": ModelType.IFT,
|
186 |
-
"pillowtalks-ai/delta13b": ModelType.FT,
|
187 |
-
"wannaphong/openthaigpt-0.1.0-beta-full-model_for_open_llm_leaderboard": ModelType.FT,
|
188 |
-
"bigscience/bloom-7b1": ModelType.PT,
|
189 |
-
"bigcode/tiny_starcoder_py": ModelType.PT,
|
190 |
-
"bigcode/starcoderplus": ModelType.FT,
|
191 |
-
"bigcode/gpt_bigcode-santacoder": ModelType.PT,
|
192 |
-
"bigcode/starcoder": ModelType.PT,
|
193 |
-
"Open-Orca/OpenOrca-Preview1-13B": ModelType.IFT,
|
194 |
-
"microsoft/DialoGPT-large": ModelType.FT,
|
195 |
-
"microsoft/DialoGPT-small": ModelType.FT,
|
196 |
-
"microsoft/DialoGPT-medium": ModelType.FT,
|
197 |
-
"microsoft/CodeGPT-small-py": ModelType.FT,
|
198 |
-
"Tincando/fiction_story_generator": ModelType.FT,
|
199 |
-
"Pirr/pythia-13b-deduped-green_devil": ModelType.FT,
|
200 |
-
"Aeala/GPT4-x-AlpacaDente2-30b": ModelType.FT,
|
201 |
-
"Aeala/GPT4-x-AlpacaDente-30b": ModelType.FT,
|
202 |
-
"Aeala/GPT4-x-Alpasta-13b": ModelType.FT,
|
203 |
-
"Aeala/VicUnlocked-alpaca-30b": ModelType.IFT,
|
204 |
-
"Tap-M/Luna-AI-Llama2-Uncensored": ModelType.FT,
|
205 |
-
"illuin/test-custom-llama": ModelType.FT,
|
206 |
-
"dvruette/oasst-llama-13b-2-epochs": ModelType.FT,
|
207 |
-
"dvruette/oasst-gpt-neox-20b-1000-steps": ModelType.FT,
|
208 |
-
"dvruette/llama-13b-pretrained-dropout": ModelType.PT,
|
209 |
-
"dvruette/llama-13b-pretrained": ModelType.PT,
|
210 |
-
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213 |
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214 |
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223 |
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224 |
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225 |
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229 |
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230 |
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231 |
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232 |
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233 |
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234 |
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236 |
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238 |
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252 |
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266 |
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267 |
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269 |
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271 |
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273 |
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274 |
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275 |
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276 |
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280 |
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281 |
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282 |
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283 |
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286 |
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287 |
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288 |
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289 |
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291 |
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292 |
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293 |
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294 |
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295 |
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296 |
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297 |
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298 |
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299 |
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303 |
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304 |
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305 |
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306 |
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307 |
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308 |
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309 |
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310 |
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311 |
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314 |
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315 |
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316 |
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317 |
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321 |
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322 |
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323 |
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324 |
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325 |
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326 |
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327 |
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328 |
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329 |
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330 |
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331 |
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332 |
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333 |
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334 |
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335 |
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336 |
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337 |
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338 |
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339 |
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340 |
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341 |
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342 |
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|
343 |
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344 |
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345 |
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346 |
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|
347 |
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|
348 |
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|
349 |
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350 |
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|
351 |
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352 |
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353 |
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|
354 |
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|
355 |
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356 |
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|
357 |
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|
358 |
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|
359 |
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|
360 |
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|
361 |
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|
362 |
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363 |
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|
364 |
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|
365 |
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366 |
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367 |
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368 |
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369 |
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370 |
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371 |
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372 |
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373 |
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374 |
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|
375 |
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|
376 |
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377 |
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378 |
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379 |
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380 |
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381 |
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382 |
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383 |
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|
384 |
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|
385 |
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|
386 |
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|
387 |
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388 |
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|
389 |
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|
390 |
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|
391 |
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|
392 |
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|
393 |
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|
394 |
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|
395 |
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396 |
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397 |
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398 |
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|
399 |
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|
400 |
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|
401 |
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|
402 |
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|
403 |
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|
404 |
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405 |
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|
406 |
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|
407 |
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|
408 |
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409 |
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410 |
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411 |
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412 |
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|
413 |
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|
414 |
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|
415 |
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|
416 |
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|
417 |
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|
418 |
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|
419 |
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|
420 |
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|
421 |
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|
422 |
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|
423 |
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|
424 |
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|
425 |
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|
426 |
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|
427 |
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428 |
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|
429 |
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|
430 |
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|
431 |
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|
432 |
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|
433 |
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|
434 |
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|
435 |
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|
436 |
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|
437 |
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|
438 |
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439 |
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|
440 |
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|
441 |
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|
442 |
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|
443 |
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|
444 |
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|
445 |
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|
446 |
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|
447 |
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|
448 |
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|
449 |
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|
450 |
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|
451 |
-
"cerebras/Cerebras-GPT-1.3B": ModelType.PT,
|
452 |
-
"cerebras/Cerebras-GPT-13B": ModelType.PT,
|
453 |
-
"cerebras/Cerebras-GPT-2.7B": ModelType.PT,
|
454 |
-
"cerebras/Cerebras-GPT-111M": ModelType.PT,
|
455 |
-
"cerebras/Cerebras-GPT-6.7B": ModelType.PT,
|
456 |
-
"Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf": ModelType.RL,
|
457 |
-
"Yhyu13/llama-30B-hf-openassitant": ModelType.FT,
|
458 |
-
"NousResearch/Nous-Hermes-Llama2-13b": ModelType.IFT,
|
459 |
-
"NousResearch/Nous-Hermes-llama-2-7b": ModelType.IFT,
|
460 |
-
"NousResearch/Redmond-Puffin-13B": ModelType.IFT,
|
461 |
-
"NousResearch/Nous-Hermes-13b": ModelType.IFT,
|
462 |
-
"project-baize/baize-v2-7b": ModelType.IFT,
|
463 |
-
"project-baize/baize-v2-13b": ModelType.IFT,
|
464 |
-
"LLMs/WizardLM-13B-V1.0": ModelType.FT,
|
465 |
-
"LLMs/AlpacaGPT4-7B-elina": ModelType.FT,
|
466 |
-
"wenge-research/yayi-7b": ModelType.FT,
|
467 |
-
"wenge-research/yayi-7b-llama2": ModelType.FT,
|
468 |
-
"wenge-research/yayi-13b-llama2": ModelType.FT,
|
469 |
-
"yhyhy3/open_llama_7b_v2_med_instruct": ModelType.IFT,
|
470 |
-
"llama-anon/instruct-13b": ModelType.IFT,
|
471 |
-
"huggingtweets/jerma985": ModelType.FT,
|
472 |
-
"huggingtweets/gladosystem": ModelType.FT,
|
473 |
-
"huggingtweets/bladeecity-jerma985": ModelType.FT,
|
474 |
-
"huggyllama/llama-13b": ModelType.PT,
|
475 |
-
"huggyllama/llama-65b": ModelType.PT,
|
476 |
-
"FabbriSimo01/Facebook_opt_1.3b_Quantized": ModelType.PT,
|
477 |
-
"upstage/Llama-2-70b-instruct": ModelType.IFT,
|
478 |
-
"upstage/Llama-2-70b-instruct-1024": ModelType.IFT,
|
479 |
-
"upstage/llama-65b-instruct": ModelType.IFT,
|
480 |
-
"upstage/llama-30b-instruct-2048": ModelType.IFT,
|
481 |
-
"upstage/llama-30b-instruct": ModelType.IFT,
|
482 |
-
"WizardLM/WizardLM-13B-1.0": ModelType.IFT,
|
483 |
-
"WizardLM/WizardLM-13B-V1.1": ModelType.IFT,
|
484 |
-
"WizardLM/WizardLM-13B-V1.2": ModelType.IFT,
|
485 |
-
"WizardLM/WizardLM-30B-V1.0": ModelType.IFT,
|
486 |
-
"WizardLM/WizardCoder-15B-V1.0": ModelType.IFT,
|
487 |
-
"gpt2": ModelType.PT,
|
488 |
-
"keyfan/vicuna-chinese-replication-v1.1": ModelType.IFT,
|
489 |
-
"nthngdy/pythia-owt2-70m-100k": ModelType.FT,
|
490 |
-
"nthngdy/pythia-owt2-70m-50k": ModelType.FT,
|
491 |
-
"quantumaikr/KoreanLM-hf": ModelType.FT,
|
492 |
-
"quantumaikr/open_llama_7b_hf": ModelType.FT,
|
493 |
-
"quantumaikr/QuantumLM-70B-hf": ModelType.IFT,
|
494 |
-
"MayaPH/FinOPT-Lincoln": ModelType.FT,
|
495 |
-
"MayaPH/FinOPT-Franklin": ModelType.FT,
|
496 |
-
"MayaPH/GodziLLa-30B": ModelType.IFT,
|
497 |
-
"MayaPH/GodziLLa-30B-plus": ModelType.IFT,
|
498 |
-
"MayaPH/FinOPT-Washington": ModelType.FT,
|
499 |
-
"ogimgio/gpt-neo-125m-neurallinguisticpioneers": ModelType.FT,
|
500 |
-
"layoric/llama-2-13b-code-alpaca": ModelType.FT,
|
501 |
-
"CobraMamba/mamba-gpt-3b": ModelType.FT,
|
502 |
-
"CobraMamba/mamba-gpt-3b-v2": ModelType.FT,
|
503 |
-
"CobraMamba/mamba-gpt-3b-v3": ModelType.FT,
|
504 |
-
"timdettmers/guanaco-33b-merged": ModelType.FT,
|
505 |
-
"elinas/chronos-33b": ModelType.IFT,
|
506 |
-
"heegyu/RedTulu-Uncensored-3B-0719": ModelType.IFT,
|
507 |
-
"heegyu/WizardVicuna-Uncensored-3B-0719": ModelType.IFT,
|
508 |
-
"heegyu/WizardVicuna-3B-0719": ModelType.IFT,
|
509 |
-
"meta-llama/Llama-2-7b-chat-hf": ModelType.RL,
|
510 |
-
"meta-llama/Llama-2-7b-hf": ModelType.PT,
|
511 |
-
"meta-llama/Llama-2-13b-chat-hf": ModelType.RL,
|
512 |
-
"meta-llama/Llama-2-13b-hf": ModelType.PT,
|
513 |
-
"meta-llama/Llama-2-70b-chat-hf": ModelType.RL,
|
514 |
-
"meta-llama/Llama-2-70b-hf": ModelType.PT,
|
515 |
-
"xhyi/PT_GPTNEO350_ATG": ModelType.FT,
|
516 |
-
"h2oai/h2ogpt-gm-oasst1-en-1024-20b": ModelType.FT,
|
517 |
-
"h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt": ModelType.FT,
|
518 |
-
"h2oai/h2ogpt-oig-oasst1-512-6_9b": ModelType.IFT,
|
519 |
-
"h2oai/h2ogpt-oasst1-512-12b": ModelType.IFT,
|
520 |
-
"h2oai/h2ogpt-oig-oasst1-256-6_9b": ModelType.IFT,
|
521 |
-
"h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt": ModelType.FT,
|
522 |
-
"h2oai/h2ogpt-oasst1-512-20b": ModelType.IFT,
|
523 |
-
"h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2": ModelType.FT,
|
524 |
-
"h2oai/h2ogpt-gm-oasst1-en-1024-12b": ModelType.FT,
|
525 |
-
"h2oai/h2ogpt-gm-oasst1-multilang-1024-20b": ModelType.FT,
|
526 |
-
"bofenghuang/vigogne-13b-instruct": ModelType.IFT,
|
527 |
-
"bofenghuang/vigogne-13b-chat": ModelType.FT,
|
528 |
-
"bofenghuang/vigogne-2-7b-instruct": ModelType.IFT,
|
529 |
-
"bofenghuang/vigogne-7b-instruct": ModelType.IFT,
|
530 |
-
"bofenghuang/vigogne-7b-chat": ModelType.FT,
|
531 |
-
"Vmware/open-llama-7b-v2-open-instruct": ModelType.IFT,
|
532 |
-
"VMware/open-llama-0.7T-7B-open-instruct-v1.1": ModelType.IFT,
|
533 |
-
"ewof/koishi-instruct-3b": ModelType.IFT,
|
534 |
-
"gywy/llama2-13b-chinese-v1": ModelType.FT,
|
535 |
-
"GOAT-AI/GOAT-7B-Community": ModelType.FT,
|
536 |
-
"psyche/kollama2-7b": ModelType.FT,
|
537 |
-
"TheTravellingEngineer/llama2-7b-hf-guanaco": ModelType.FT,
|
538 |
-
"beaugogh/pythia-1.4b-deduped-sharegpt": ModelType.FT,
|
539 |
-
"augtoma/qCammel-70-x": ModelType.IFT,
|
540 |
-
"Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_unload": ModelType.IFT,
|
541 |
-
"anhnv125/pygmalion-6b-roleplay": ModelType.FT,
|
542 |
-
"64bits/LexPodLM-13B": ModelType.FT,
|
543 |
-
}
|
544 |
-
|
545 |
-
|
546 |
-
def model_type_from_str(type):
|
547 |
-
if "fine-tuned" in type or "🔶" in type:
|
548 |
-
return ModelType.FT
|
549 |
-
if "pretrained" in type or "🟢" in type:
|
550 |
-
return ModelType.PT
|
551 |
-
if "RL-tuned" in type or "🟦" in type:
|
552 |
-
return ModelType.RL
|
553 |
-
if "instruction-tuned" in type or "⭕" in type:
|
554 |
-
return ModelType.IFT
|
555 |
-
return ModelType.Unknown
|
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|
|
src/{get_model_info/hardocded_metadata/flags.py → leaderboard/filter_models.py}
RENAMED
@@ -1,3 +1,6 @@
|
|
|
|
|
|
|
|
1 |
# Models which have been flagged by users as being problematic for a reason or another
|
2 |
# (Model name to forum discussion link)
|
3 |
FLAGGED_MODELS = {
|
@@ -16,3 +19,32 @@ FLAGGED_MODELS = {
|
|
16 |
DO_NOT_SUBMIT_MODELS = [
|
17 |
"Voicelab/trurl-2-13b", # trained on MMLU
|
18 |
]
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from src.display.formatting import model_hyperlink
|
2 |
+
from src.display.utils import AutoEvalColumn
|
3 |
+
|
4 |
# Models which have been flagged by users as being problematic for a reason or another
|
5 |
# (Model name to forum discussion link)
|
6 |
FLAGGED_MODELS = {
|
|
|
19 |
DO_NOT_SUBMIT_MODELS = [
|
20 |
"Voicelab/trurl-2-13b", # trained on MMLU
|
21 |
]
|
22 |
+
|
23 |
+
|
24 |
+
def flag_models(leaderboard_data: list[dict]):
|
25 |
+
for model_data in leaderboard_data:
|
26 |
+
if model_data["model_name_for_query"] in FLAGGED_MODELS:
|
27 |
+
issue_num = FLAGGED_MODELS[model_data["model_name_for_query"]].split("/")[-1]
|
28 |
+
issue_link = model_hyperlink(
|
29 |
+
FLAGGED_MODELS[model_data["model_name_for_query"]],
|
30 |
+
f"See discussion #{issue_num}",
|
31 |
+
)
|
32 |
+
model_data[
|
33 |
+
AutoEvalColumn.model.name
|
34 |
+
] = f"{model_data[AutoEvalColumn.model.name]} has been flagged! {issue_link}"
|
35 |
+
|
36 |
+
|
37 |
+
def remove_forbidden_models(leaderboard_data: list[dict]):
|
38 |
+
indices_to_remove = []
|
39 |
+
for ix, model in enumerate(leaderboard_data):
|
40 |
+
if model["model_name_for_query"] in DO_NOT_SUBMIT_MODELS:
|
41 |
+
indices_to_remove.append(ix)
|
42 |
+
|
43 |
+
for ix in reversed(indices_to_remove):
|
44 |
+
leaderboard_data.pop(ix)
|
45 |
+
return leaderboard_data
|
46 |
+
|
47 |
+
|
48 |
+
def filter_models(leaderboard_data: list[dict]):
|
49 |
+
leaderboard_data = remove_forbidden_models(leaderboard_data)
|
50 |
+
flag_models(leaderboard_data)
|
src/leaderboard/read_evals.py
ADDED
@@ -0,0 +1,197 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
1 |
+
import json
|
2 |
+
import os
|
3 |
+
import math
|
4 |
+
import glob
|
5 |
+
from dataclasses import dataclass
|
6 |
+
from typing import Dict, List, Tuple
|
7 |
+
|
8 |
+
import dateutil
|
9 |
+
import numpy as np
|
10 |
+
|
11 |
+
from src.display.utils import AutoEvalColumn, ModelType, Tasks
|
12 |
+
from src.display.formatting import make_clickable_model
|
13 |
+
from src.submission.check_validity import is_model_on_hub
|
14 |
+
|
15 |
+
|
16 |
+
@dataclass
|
17 |
+
class EvalResult:
|
18 |
+
eval_name: str
|
19 |
+
full_model: str
|
20 |
+
org: str
|
21 |
+
model: str
|
22 |
+
revision: str
|
23 |
+
results: dict
|
24 |
+
precision: str = ""
|
25 |
+
model_type: ModelType = ModelType.Unknown
|
26 |
+
weight_type: str = "Original"
|
27 |
+
license: str = "?"
|
28 |
+
likes: int = 0
|
29 |
+
num_params: int = 0
|
30 |
+
date: str = ""
|
31 |
+
still_on_hub: bool = False
|
32 |
+
|
33 |
+
@classmethod
|
34 |
+
def init_from_json_file(self, json_filepath):
|
35 |
+
with open(json_filepath) as fp:
|
36 |
+
data = json.load(fp)
|
37 |
+
|
38 |
+
# We manage the legacy config format
|
39 |
+
config = data.get("config", data.get("config_general", None))
|
40 |
+
|
41 |
+
# Precision
|
42 |
+
precision = config.get("model_dtype")
|
43 |
+
if precision == "None":
|
44 |
+
precision = "GPTQ"
|
45 |
+
|
46 |
+
# Get model and org
|
47 |
+
org_and_model = config.get("model_name", config.get("model_args", None))
|
48 |
+
org_and_model = org_and_model.split("/", 1)
|
49 |
+
|
50 |
+
if len(org_and_model) == 1:
|
51 |
+
org = None
|
52 |
+
model = org_and_model[0]
|
53 |
+
result_key = f"{model}_{precision}"
|
54 |
+
else:
|
55 |
+
org = org_and_model[0]
|
56 |
+
model = org_and_model[1]
|
57 |
+
result_key = f"{org}_{model}_{precision}"
|
58 |
+
|
59 |
+
still_on_hub = is_model_on_hub("/".join(org_and_model), config.get("model_sha", "main"), trust_remote_code=True)[0]
|
60 |
+
|
61 |
+
# Extract results available in this file (some results are split in several files)
|
62 |
+
results = {}
|
63 |
+
for task in Tasks:
|
64 |
+
task = task.value
|
65 |
+
# We skip old mmlu entries
|
66 |
+
wrong_mmlu_version = False
|
67 |
+
if task.benchmark == "hendrycksTest":
|
68 |
+
for mmlu_k in ["harness|hendrycksTest-abstract_algebra|5", "hendrycksTest-abstract_algebra"]:
|
69 |
+
if mmlu_k in data["versions"] and data["versions"][mmlu_k] == 0:
|
70 |
+
wrong_mmlu_version = True
|
71 |
+
|
72 |
+
if wrong_mmlu_version:
|
73 |
+
continue
|
74 |
+
|
75 |
+
# Some truthfulQA values are NaNs
|
76 |
+
if task.benchmark == "truthfulqa:mc" and task.benchmark in data["results"]:
|
77 |
+
if math.isnan(float(data["results"][task.benchmark][task.metric])):
|
78 |
+
results[task.benchmark] = 0.0
|
79 |
+
continue
|
80 |
+
|
81 |
+
# We average all scores of a given metric (mostly for mmlu)
|
82 |
+
accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark in k])
|
83 |
+
if accs.size == 0 or any([acc is None for acc in accs]):
|
84 |
+
continue
|
85 |
+
|
86 |
+
mean_acc = np.mean(accs) * 100.0
|
87 |
+
results[task.benchmark] = mean_acc
|
88 |
+
|
89 |
+
return self(
|
90 |
+
eval_name=result_key,
|
91 |
+
full_model="/".join(org_and_model),
|
92 |
+
org=org,
|
93 |
+
model=model,
|
94 |
+
results=results,
|
95 |
+
precision=precision, # todo model_type=, weight_type=
|
96 |
+
revision=config.get("model_sha", ""),
|
97 |
+
date=config.get("submission_date", ""),
|
98 |
+
still_on_hub=still_on_hub,
|
99 |
+
)
|
100 |
+
|
101 |
+
def update_with_request_file(self):
|
102 |
+
request_file = get_request_file_for_model(self.full_model, self.precision)
|
103 |
+
|
104 |
+
try:
|
105 |
+
with open(request_file, "r") as f:
|
106 |
+
request = json.load(f)
|
107 |
+
self.model_type = ModelType.from_str(request.get("model_type", ""))
|
108 |
+
self.license = request.get("license", "?")
|
109 |
+
self.likes = request.get("likes", 0)
|
110 |
+
self.num_params = request.get("params", 0)
|
111 |
+
except Exception:
|
112 |
+
print(f"Could not find request file for {self.org}/{self.model}")
|
113 |
+
|
114 |
+
def to_dict(self):
|
115 |
+
average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)
|
116 |
+
data_dict = {
|
117 |
+
"eval_name": self.eval_name, # not a column, just a save name,
|
118 |
+
AutoEvalColumn.precision.name: self.precision,
|
119 |
+
AutoEvalColumn.model_type.name: self.model_type.value.name,
|
120 |
+
AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
|
121 |
+
AutoEvalColumn.weight_type.name: self.weight_type,
|
122 |
+
AutoEvalColumn.model.name: make_clickable_model(self.full_model),
|
123 |
+
AutoEvalColumn.dummy.name: self.full_model,
|
124 |
+
AutoEvalColumn.revision.name: self.revision,
|
125 |
+
AutoEvalColumn.average.name: average,
|
126 |
+
AutoEvalColumn.license.name: self.license,
|
127 |
+
AutoEvalColumn.likes.name: self.likes,
|
128 |
+
AutoEvalColumn.params.name: self.num_params,
|
129 |
+
AutoEvalColumn.still_on_hub.name: self.still_on_hub,
|
130 |
+
}
|
131 |
+
|
132 |
+
for task in Tasks:
|
133 |
+
data_dict[task.value.col_name] = self.results[task.value.benchmark]
|
134 |
+
|
135 |
+
return data_dict
|
136 |
+
|
137 |
+
|
138 |
+
def get_request_file_for_model(model_name, precision):
|
139 |
+
request_files = os.path.join(
|
140 |
+
"eval-queue",
|
141 |
+
f"{model_name}_eval_request_*.json",
|
142 |
+
)
|
143 |
+
request_files = glob.glob(request_files)
|
144 |
+
|
145 |
+
# Select correct request file (precision)
|
146 |
+
request_file = ""
|
147 |
+
request_files = sorted(request_files, reverse=True)
|
148 |
+
for tmp_request_file in request_files:
|
149 |
+
with open(tmp_request_file, "r") as f:
|
150 |
+
req_content = json.load(f)
|
151 |
+
if (
|
152 |
+
req_content["status"] in ["FINISHED", "PENDING_NEW_EVAL"]
|
153 |
+
and req_content["precision"] == precision.split(".")[-1]
|
154 |
+
):
|
155 |
+
request_file = tmp_request_file
|
156 |
+
return request_file
|
157 |
+
|
158 |
+
|
159 |
+
def get_eval_results(results_path: str) -> List[EvalResult]:
|
160 |
+
json_filepaths = []
|
161 |
+
|
162 |
+
for root, _, files in os.walk(results_path):
|
163 |
+
# We should only have json files in model results
|
164 |
+
if len(files) == 0 or any([not f.endswith(".json") for f in files]):
|
165 |
+
continue
|
166 |
+
|
167 |
+
# Sort the files by date
|
168 |
+
try:
|
169 |
+
files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
|
170 |
+
except dateutil.parser._parser.ParserError:
|
171 |
+
files = [files[-1]]
|
172 |
+
|
173 |
+
# up_to_date = files[-1]
|
174 |
+
for file in files:
|
175 |
+
json_filepaths.append(os.path.join(root, file))
|
176 |
+
|
177 |
+
eval_results = {}
|
178 |
+
for json_filepath in json_filepaths:
|
179 |
+
# Creation of result
|
180 |
+
eval_result = EvalResult.init_from_json_file(json_filepath)
|
181 |
+
eval_result.update_with_request_file()
|
182 |
+
|
183 |
+
# Store results of same eval together
|
184 |
+
eval_name = eval_result.eval_name
|
185 |
+
if eval_name in eval_results.keys():
|
186 |
+
eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
|
187 |
+
else:
|
188 |
+
eval_results[eval_name] = eval_result
|
189 |
+
|
190 |
+
results = []
|
191 |
+
for v in eval_results.values():
|
192 |
+
try:
|
193 |
+
results.append(v.to_dict())
|
194 |
+
except KeyError: # not all eval values present
|
195 |
+
continue
|
196 |
+
|
197 |
+
return results
|
src/plots/read_results.py
DELETED
@@ -1,158 +0,0 @@
|
|
1 |
-
import json
|
2 |
-
import os
|
3 |
-
from dataclasses import dataclass
|
4 |
-
from typing import Dict, List, Tuple
|
5 |
-
|
6 |
-
import dateutil
|
7 |
-
import numpy as np
|
8 |
-
|
9 |
-
from src.get_model_info.utils import AutoEvalColumn, make_clickable_model
|
10 |
-
|
11 |
-
METRICS = ["acc_norm", "acc_norm", "acc", "mc2", "acc", "acc", "f1"]
|
12 |
-
BENCHMARKS = ["arc:challenge", "hellaswag", "hendrycksTest", "truthfulqa:mc", "winogrande", "gsm8k", "drop"]
|
13 |
-
BENCH_TO_NAME = {
|
14 |
-
"arc:challenge": AutoEvalColumn.arc.name,
|
15 |
-
"hellaswag": AutoEvalColumn.hellaswag.name,
|
16 |
-
"hendrycksTest": AutoEvalColumn.mmlu.name,
|
17 |
-
"truthfulqa:mc": AutoEvalColumn.truthfulqa.name,
|
18 |
-
"winogrande": AutoEvalColumn.winogrande.name,
|
19 |
-
"gsm8k": AutoEvalColumn.gsm8k.name,
|
20 |
-
"drop": AutoEvalColumn.drop.name,
|
21 |
-
}
|
22 |
-
|
23 |
-
|
24 |
-
@dataclass
|
25 |
-
class EvalResult:
|
26 |
-
eval_name: str
|
27 |
-
org: str
|
28 |
-
model: str
|
29 |
-
revision: str
|
30 |
-
results: dict
|
31 |
-
precision: str = ""
|
32 |
-
model_type: str = ""
|
33 |
-
weight_type: str = "Original"
|
34 |
-
date: str = ""
|
35 |
-
|
36 |
-
def to_dict(self):
|
37 |
-
from src.filters import is_model_on_hub
|
38 |
-
|
39 |
-
if self.org is not None:
|
40 |
-
base_model = f"{self.org}/{self.model}"
|
41 |
-
else:
|
42 |
-
base_model = f"{self.model}"
|
43 |
-
data_dict = {}
|
44 |
-
|
45 |
-
data_dict["eval_name"] = self.eval_name # not a column, just a save name
|
46 |
-
data_dict["weight_type"] = self.weight_type # not a column, just a save name
|
47 |
-
data_dict[AutoEvalColumn.precision.name] = self.precision
|
48 |
-
data_dict[AutoEvalColumn.model_type.name] = self.model_type
|
49 |
-
data_dict[AutoEvalColumn.model.name] = make_clickable_model(base_model)
|
50 |
-
data_dict[AutoEvalColumn.dummy.name] = base_model
|
51 |
-
data_dict[AutoEvalColumn.revision.name] = self.revision
|
52 |
-
data_dict[AutoEvalColumn.average.name] = sum([v for k, v in self.results.items()]) / 7.0
|
53 |
-
data_dict[AutoEvalColumn.still_on_hub.name] = (
|
54 |
-
is_model_on_hub(base_model, self.revision)[0] or base_model == "baseline"
|
55 |
-
)
|
56 |
-
|
57 |
-
for benchmark in BENCHMARKS:
|
58 |
-
if benchmark not in self.results.keys():
|
59 |
-
self.results[benchmark] = None
|
60 |
-
|
61 |
-
for k, v in BENCH_TO_NAME.items():
|
62 |
-
data_dict[v] = self.results[k]
|
63 |
-
|
64 |
-
return data_dict
|
65 |
-
|
66 |
-
|
67 |
-
def parse_eval_result(json_filepath: str) -> Tuple[str, list[dict]]:
|
68 |
-
with open(json_filepath) as fp:
|
69 |
-
data = json.load(fp)
|
70 |
-
|
71 |
-
for mmlu_k in ["harness|hendrycksTest-abstract_algebra|5", "hendrycksTest-abstract_algebra"]:
|
72 |
-
if mmlu_k in data["versions"] and data["versions"][mmlu_k] == 0:
|
73 |
-
return None, [] # we skip models with the wrong version
|
74 |
-
|
75 |
-
try:
|
76 |
-
config = data["config"]
|
77 |
-
except KeyError:
|
78 |
-
config = data["config_general"]
|
79 |
-
model = config.get("model_name", None)
|
80 |
-
if model is None:
|
81 |
-
model = config.get("model_args", None)
|
82 |
-
|
83 |
-
model_sha = config.get("model_sha", "")
|
84 |
-
model_split = model.split("/", 1)
|
85 |
-
|
86 |
-
precision = config.get("model_dtype")
|
87 |
-
if precision == "None":
|
88 |
-
precision = "GPTQ"
|
89 |
-
|
90 |
-
model = model_split[-1]
|
91 |
-
|
92 |
-
if len(model_split) == 1:
|
93 |
-
org = None
|
94 |
-
model = model_split[0]
|
95 |
-
result_key = f"{model}_{precision}"
|
96 |
-
else:
|
97 |
-
org = model_split[0]
|
98 |
-
model = model_split[1]
|
99 |
-
result_key = f"{org}_{model}_{precision}"
|
100 |
-
|
101 |
-
eval_results = []
|
102 |
-
for benchmark, metric in zip(BENCHMARKS, METRICS):
|
103 |
-
accs = np.array([v.get(metric, None) for k, v in data["results"].items() if benchmark in k])
|
104 |
-
if accs.size == 0 or any([acc is None for acc in accs]):
|
105 |
-
continue
|
106 |
-
mean_acc = np.mean(accs) * 100.0
|
107 |
-
eval_results.append(
|
108 |
-
EvalResult(
|
109 |
-
eval_name=result_key,
|
110 |
-
org=org,
|
111 |
-
model=model,
|
112 |
-
revision=model_sha,
|
113 |
-
results={benchmark: mean_acc},
|
114 |
-
precision=precision, # todo model_type=, weight_type=
|
115 |
-
date=config.get("submission_date"),
|
116 |
-
)
|
117 |
-
)
|
118 |
-
|
119 |
-
return result_key, eval_results
|
120 |
-
|
121 |
-
|
122 |
-
def get_eval_results(results_path: str) -> List[EvalResult]:
|
123 |
-
json_filepaths = []
|
124 |
-
|
125 |
-
for root, dir, files in os.walk(results_path):
|
126 |
-
# We should only have json files in model results
|
127 |
-
if len(files) == 0 or any([not f.endswith(".json") for f in files]):
|
128 |
-
continue
|
129 |
-
|
130 |
-
# Sort the files by date
|
131 |
-
# store results by precision maybe?
|
132 |
-
try:
|
133 |
-
files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
|
134 |
-
except dateutil.parser._parser.ParserError:
|
135 |
-
files = [files[-1]]
|
136 |
-
|
137 |
-
# up_to_date = files[-1]
|
138 |
-
for file in files:
|
139 |
-
json_filepaths.append(os.path.join(root, file))
|
140 |
-
|
141 |
-
eval_results = {}
|
142 |
-
for json_filepath in json_filepaths:
|
143 |
-
result_key, results = parse_eval_result(json_filepath)
|
144 |
-
for eval_result in results:
|
145 |
-
if result_key in eval_results.keys():
|
146 |
-
eval_results[result_key].results.update(eval_result.results)
|
147 |
-
else:
|
148 |
-
eval_results[result_key] = eval_result
|
149 |
-
|
150 |
-
eval_results = [v for v in eval_results.values()]
|
151 |
-
|
152 |
-
return eval_results
|
153 |
-
|
154 |
-
|
155 |
-
def get_eval_results_dicts(results_path: str) -> List[Dict]:
|
156 |
-
eval_results = get_eval_results(results_path)
|
157 |
-
|
158 |
-
return [e.to_dict() for e in eval_results]
|
|
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|
src/{load_from_hub.py → populate.py}
RENAMED
@@ -1,50 +1,18 @@
|
|
1 |
import json
|
2 |
import os
|
3 |
-
from collections import defaultdict
|
4 |
|
5 |
import pandas as pd
|
6 |
|
7 |
-
from src.
|
8 |
-
from src.
|
9 |
-
from src.
|
10 |
-
from src.
|
11 |
-
|
12 |
-
IS_PUBLIC = bool(os.environ.get("IS_PUBLIC", True))
|
13 |
-
|
14 |
-
|
15 |
-
def get_all_requested_models(requested_models_dir: str) -> set[str]:
|
16 |
-
depth = 1
|
17 |
-
file_names = []
|
18 |
-
users_to_submission_dates = defaultdict(list)
|
19 |
-
|
20 |
-
for root, _, files in os.walk(requested_models_dir):
|
21 |
-
current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
|
22 |
-
if current_depth == depth:
|
23 |
-
for file in files:
|
24 |
-
if not file.endswith(".json"):
|
25 |
-
continue
|
26 |
-
with open(os.path.join(root, file), "r") as f:
|
27 |
-
info = json.load(f)
|
28 |
-
file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
|
29 |
-
|
30 |
-
# Select organisation
|
31 |
-
if info["model"].count("/") == 0 or "submitted_time" not in info:
|
32 |
-
continue
|
33 |
-
organisation, _ = info["model"].split("/")
|
34 |
-
users_to_submission_dates[organisation].append(info["submitted_time"])
|
35 |
-
|
36 |
-
return set(file_names), users_to_submission_dates
|
37 |
|
38 |
|
39 |
def get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
|
40 |
-
all_data =
|
41 |
-
|
42 |
-
|
43 |
-
all_data.append(gpt4_values)
|
44 |
-
all_data.append(gpt35_values)
|
45 |
-
|
46 |
-
all_data.append(baseline)
|
47 |
-
apply_metadata(all_data) # Populate model type based on known hardcoded values in `metadata.py`
|
48 |
|
49 |
df = pd.DataFrame.from_records(all_data)
|
50 |
df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
|
@@ -88,4 +56,3 @@ def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
|
|
88 |
df_running = pd.DataFrame.from_records(running_list, columns=cols)
|
89 |
df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
|
90 |
return df_finished[cols], df_running[cols], df_pending[cols]
|
91 |
-
|
|
|
1 |
import json
|
2 |
import os
|
|
|
3 |
|
4 |
import pandas as pd
|
5 |
|
6 |
+
from src.leaderboard.filter_models import filter_models
|
7 |
+
from src.leaderboard.read_evals import get_eval_results
|
8 |
+
from src.display.formatting import make_clickable_model, has_no_nan_values
|
9 |
+
from src.display.utils import AutoEvalColumn, EvalQueueColumn, baseline_row
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
10 |
|
11 |
|
12 |
def get_leaderboard_df(results_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
|
13 |
+
all_data = get_eval_results(results_path)
|
14 |
+
all_data.append(baseline_row)
|
15 |
+
filter_models(all_data)
|
|
|
|
|
|
|
|
|
|
|
16 |
|
17 |
df = pd.DataFrame.from_records(all_data)
|
18 |
df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
|
|
|
56 |
df_running = pd.DataFrame.from_records(running_list, columns=cols)
|
57 |
df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
|
58 |
return df_finished[cols], df_running[cols], df_pending[cols]
|
|
src/{filters.py → submission/check_validity.py}
RENAMED
@@ -1,5 +1,9 @@
|
|
1 |
import huggingface_hub
|
2 |
import os
|
|
|
|
|
|
|
|
|
3 |
from huggingface_hub import ModelCard
|
4 |
from transformers import AutoConfig
|
5 |
|
@@ -30,9 +34,9 @@ def check_model_card(repo_id: str) -> tuple[bool, str]:
|
|
30 |
return True, ""
|
31 |
|
32 |
|
33 |
-
def is_model_on_hub(model_name: str, revision: str, token: str = None) -> bool:
|
34 |
try:
|
35 |
-
AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=
|
36 |
return True, None
|
37 |
|
38 |
except ValueError:
|
@@ -45,6 +49,23 @@ def is_model_on_hub(model_name: str, revision: str, token: str = None) -> bool:
|
|
45 |
return False, "was not found on hub!"
|
46 |
|
47 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
48 |
def user_submission_permission(submission_name, users_to_submission_dates, rate_limit_period, rate_limit_quota):
|
49 |
org_or_user, _ = submission_name.split("/")
|
50 |
if org_or_user not in users_to_submission_dates:
|
@@ -65,3 +86,26 @@ def user_submission_permission(submission_name, users_to_submission_dates, rate_
|
|
65 |
return False, error_msg
|
66 |
return True, ""
|
67 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
import huggingface_hub
|
2 |
import os
|
3 |
+
import json
|
4 |
+
import re
|
5 |
+
from collections import defaultdict
|
6 |
+
from huggingface_hub.hf_api import ModelInfo
|
7 |
from huggingface_hub import ModelCard
|
8 |
from transformers import AutoConfig
|
9 |
|
|
|
34 |
return True, ""
|
35 |
|
36 |
|
37 |
+
def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False) -> tuple[bool, str]:
|
38 |
try:
|
39 |
+
AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
|
40 |
return True, None
|
41 |
|
42 |
except ValueError:
|
|
|
49 |
return False, "was not found on hub!"
|
50 |
|
51 |
|
52 |
+
def get_model_size(model_info: ModelInfo, precision: str):
|
53 |
+
size_pattern = size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
|
54 |
+
try:
|
55 |
+
model_size = round(model_info.safetensors["total"] / 1e9, 3)
|
56 |
+
except AttributeError:
|
57 |
+
try:
|
58 |
+
size_match = re.search(size_pattern, model_info.modelId.lower())
|
59 |
+
model_size = size_match.group(0)
|
60 |
+
model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
|
61 |
+
except AttributeError:
|
62 |
+
return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
|
63 |
+
|
64 |
+
size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
|
65 |
+
model_size = size_factor * model_size
|
66 |
+
return model_size
|
67 |
+
|
68 |
+
|
69 |
def user_submission_permission(submission_name, users_to_submission_dates, rate_limit_period, rate_limit_quota):
|
70 |
org_or_user, _ = submission_name.split("/")
|
71 |
if org_or_user not in users_to_submission_dates:
|
|
|
86 |
return False, error_msg
|
87 |
return True, ""
|
88 |
|
89 |
+
|
90 |
+
def already_submitted_models(requested_models_dir: str) -> set[str]:
|
91 |
+
depth = 1
|
92 |
+
file_names = []
|
93 |
+
users_to_submission_dates = defaultdict(list)
|
94 |
+
|
95 |
+
for root, _, files in os.walk(requested_models_dir):
|
96 |
+
current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
|
97 |
+
if current_depth == depth:
|
98 |
+
for file in files:
|
99 |
+
if not file.endswith(".json"):
|
100 |
+
continue
|
101 |
+
with open(os.path.join(root, file), "r") as f:
|
102 |
+
info = json.load(f)
|
103 |
+
file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
|
104 |
+
|
105 |
+
# Select organisation
|
106 |
+
if info["model"].count("/") == 0 or "submitted_time" not in info:
|
107 |
+
continue
|
108 |
+
organisation, _ = info["model"].split("/")
|
109 |
+
users_to_submission_dates[organisation].append(info["submitted_time"])
|
110 |
+
|
111 |
+
return set(file_names), users_to_submission_dates
|
src/submission/submit.py
ADDED
@@ -0,0 +1,128 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os, json
|
2 |
+
|
3 |
+
from datetime import datetime, timezone
|
4 |
+
|
5 |
+
from src.display.formatting import styled_error, styled_warning, styled_message
|
6 |
+
from src.leaderboard.filter_models import DO_NOT_SUBMIT_MODELS
|
7 |
+
from src.submission.check_validity import (
|
8 |
+
user_submission_permission,
|
9 |
+
is_model_on_hub,
|
10 |
+
get_model_size,
|
11 |
+
check_model_card,
|
12 |
+
already_submitted_models,
|
13 |
+
)
|
14 |
+
from src.envs import RATE_LIMIT_QUOTA, RATE_LIMIT_PERIOD, H4_TOKEN, EVAL_REQUESTS_PATH, API, QUEUE_REPO
|
15 |
+
|
16 |
+
requested_models, users_to_submission_dates = already_submitted_models(EVAL_REQUESTS_PATH)
|
17 |
+
|
18 |
+
|
19 |
+
def add_new_eval(
|
20 |
+
model: str,
|
21 |
+
base_model: str,
|
22 |
+
revision: str,
|
23 |
+
precision: str,
|
24 |
+
private: bool,
|
25 |
+
weight_type: str,
|
26 |
+
model_type: str,
|
27 |
+
):
|
28 |
+
precision = precision.split(" ")[0]
|
29 |
+
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
|
30 |
+
|
31 |
+
if model_type is None or model_type == "":
|
32 |
+
return styled_error("Please select a model type.")
|
33 |
+
|
34 |
+
# Is the user rate limited?
|
35 |
+
user_can_submit, error_msg = user_submission_permission(
|
36 |
+
model, users_to_submission_dates, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA
|
37 |
+
)
|
38 |
+
if not user_can_submit:
|
39 |
+
return styled_error(error_msg)
|
40 |
+
|
41 |
+
# Did the model authors forbid its submission to the leaderboard?
|
42 |
+
if model in DO_NOT_SUBMIT_MODELS or base_model in DO_NOT_SUBMIT_MODELS:
|
43 |
+
return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.")
|
44 |
+
|
45 |
+
# Does the model actually exist?
|
46 |
+
if revision == "":
|
47 |
+
revision = "main"
|
48 |
+
|
49 |
+
# Is the model on the hub?
|
50 |
+
if weight_type in ["Delta", "Adapter"]:
|
51 |
+
base_model_on_hub, error = is_model_on_hub(base_model, revision, H4_TOKEN)
|
52 |
+
if not base_model_on_hub:
|
53 |
+
return styled_error(f'Base model "{base_model}" {error}')
|
54 |
+
|
55 |
+
if not weight_type == "Adapter":
|
56 |
+
model_on_hub, error = is_model_on_hub(model, revision)
|
57 |
+
if not model_on_hub:
|
58 |
+
return styled_error(f'Model "{model}" {error}')
|
59 |
+
|
60 |
+
# Is the model info correctly filled?
|
61 |
+
try:
|
62 |
+
model_info = API.model_info(repo_id=model, revision=revision)
|
63 |
+
except Exception:
|
64 |
+
return styled_error("Could not get your model information. Please fill it up properly.")
|
65 |
+
|
66 |
+
model_size = get_model_size(model_info=model_info, precision=precision)
|
67 |
+
|
68 |
+
# Were the model card and license filled?
|
69 |
+
try:
|
70 |
+
license = model_info.cardData["license"]
|
71 |
+
except Exception:
|
72 |
+
return styled_error("Please select a license for your model")
|
73 |
+
|
74 |
+
modelcard_OK, error_msg = check_model_card(model)
|
75 |
+
if not modelcard_OK:
|
76 |
+
return styled_error(error_msg)
|
77 |
+
|
78 |
+
# Seems good, creating the eval
|
79 |
+
print("Adding new eval")
|
80 |
+
|
81 |
+
eval_entry = {
|
82 |
+
"model": model,
|
83 |
+
"base_model": base_model,
|
84 |
+
"revision": revision,
|
85 |
+
"private": private,
|
86 |
+
"precision": precision,
|
87 |
+
"weight_type": weight_type,
|
88 |
+
"status": "PENDING",
|
89 |
+
"submitted_time": current_time,
|
90 |
+
"model_type": model_type,
|
91 |
+
"likes": model_info.likes,
|
92 |
+
"params": model_size,
|
93 |
+
"license": license,
|
94 |
+
}
|
95 |
+
|
96 |
+
user_name = ""
|
97 |
+
model_path = model
|
98 |
+
if "/" in model:
|
99 |
+
user_name = model.split("/")[0]
|
100 |
+
model_path = model.split("/")[1]
|
101 |
+
|
102 |
+
print("Creating eval file")
|
103 |
+
OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
|
104 |
+
os.makedirs(OUT_DIR, exist_ok=True)
|
105 |
+
out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json"
|
106 |
+
|
107 |
+
# Check for duplicate submission
|
108 |
+
if f"{model}_{revision}_{precision}" in requested_models:
|
109 |
+
return styled_warning("This model has been already submitted.")
|
110 |
+
|
111 |
+
with open(out_path, "w") as f:
|
112 |
+
f.write(json.dumps(eval_entry))
|
113 |
+
|
114 |
+
print("Uploading eval file")
|
115 |
+
API.upload_file(
|
116 |
+
path_or_fileobj=out_path,
|
117 |
+
path_in_repo=out_path.split("eval-queue/")[1],
|
118 |
+
repo_id=QUEUE_REPO,
|
119 |
+
repo_type="dataset",
|
120 |
+
commit_message=f"Add {model} to eval queue",
|
121 |
+
)
|
122 |
+
|
123 |
+
# Remove the local file
|
124 |
+
os.remove(out_path)
|
125 |
+
|
126 |
+
return styled_message(
|
127 |
+
"Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
|
128 |
+
)
|
src/{manage_collections.py → tools/collections.py}
RENAMED
@@ -4,33 +4,34 @@ from pandas import DataFrame
|
|
4 |
from huggingface_hub import get_collection, add_collection_item, update_collection_item, delete_collection_item
|
5 |
from huggingface_hub.utils._errors import HfHubHTTPError
|
6 |
|
7 |
-
from src.
|
8 |
-
from src.get_model_info.utils import AutoEvalColumn
|
9 |
|
10 |
-
|
11 |
|
12 |
-
|
13 |
intervals = {
|
14 |
"1B": pd.Interval(0, 1.5, closed="right"),
|
15 |
"3B": pd.Interval(2.5, 3.5, closed="neither"),
|
16 |
"7B": pd.Interval(6, 8, closed="neither"),
|
17 |
"13B": pd.Interval(10, 14, closed="neither"),
|
18 |
-
"30B":pd.Interval(25, 35, closed="neither"),
|
19 |
"65B": pd.Interval(60, 70, closed="neither"),
|
20 |
}
|
21 |
|
|
|
22 |
def update_collections(df: DataFrame):
|
23 |
-
"""This function updates the Open LLM Leaderboard model collection with the latest best models for
|
24 |
each size category and type.
|
25 |
"""
|
26 |
-
collection = get_collection(collection_slug=
|
27 |
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
|
28 |
|
29 |
cur_best_models = []
|
30 |
|
31 |
ix = 0
|
32 |
for type in ModelType:
|
33 |
-
if type.value.name == "":
|
|
|
34 |
for size in intervals:
|
35 |
# We filter the df to gather the relevant models
|
36 |
type_emoji = [t[0] for t in type.value.symbol]
|
@@ -40,7 +41,9 @@ def update_collections(df: DataFrame):
|
|
40 |
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
|
41 |
filtered_df = filtered_df.loc[mask]
|
42 |
|
43 |
-
best_models = list(
|
|
|
|
|
44 |
print(type.value.symbol, size, best_models[:10])
|
45 |
|
46 |
# We add them one by one to the leaderboard
|
@@ -49,27 +52,32 @@ def update_collections(df: DataFrame):
|
|
49 |
cur_len_collection = len(collection.items)
|
50 |
try:
|
51 |
collection = add_collection_item(
|
52 |
-
|
53 |
-
item_id=model,
|
54 |
-
item_type="model",
|
55 |
exists_ok=True,
|
56 |
-
note=f"Best {type.to_str(' ')} model of around {size} on the leaderboard today!",
|
57 |
-
token=H4_TOKEN
|
58 |
)
|
59 |
-
if
|
60 |
-
|
61 |
-
|
|
|
|
|
|
|
|
|
62 |
cur_len_collection = len(collection.items)
|
63 |
cur_best_models.append(model)
|
64 |
break
|
65 |
except HfHubHTTPError:
|
66 |
continue
|
67 |
|
68 |
-
collection = get_collection(
|
69 |
for item in collection.items:
|
70 |
if item.item_id not in cur_best_models:
|
71 |
try:
|
72 |
-
delete_collection_item(
|
|
|
|
|
73 |
except HfHubHTTPError:
|
74 |
continue
|
75 |
-
|
|
|
4 |
from huggingface_hub import get_collection, add_collection_item, update_collection_item, delete_collection_item
|
5 |
from huggingface_hub.utils._errors import HfHubHTTPError
|
6 |
|
7 |
+
from src.display.utils import AutoEvalColumn, ModelType
|
|
|
8 |
|
9 |
+
from src.envs import H4_TOKEN, PATH_TO_COLLECTION
|
10 |
|
11 |
+
# Specific intervals for the collections
|
12 |
intervals = {
|
13 |
"1B": pd.Interval(0, 1.5, closed="right"),
|
14 |
"3B": pd.Interval(2.5, 3.5, closed="neither"),
|
15 |
"7B": pd.Interval(6, 8, closed="neither"),
|
16 |
"13B": pd.Interval(10, 14, closed="neither"),
|
17 |
+
"30B": pd.Interval(25, 35, closed="neither"),
|
18 |
"65B": pd.Interval(60, 70, closed="neither"),
|
19 |
}
|
20 |
|
21 |
+
|
22 |
def update_collections(df: DataFrame):
|
23 |
+
"""This function updates the Open LLM Leaderboard model collection with the latest best models for
|
24 |
each size category and type.
|
25 |
"""
|
26 |
+
collection = get_collection(collection_slug=PATH_TO_COLLECTION, token=H4_TOKEN)
|
27 |
params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
|
28 |
|
29 |
cur_best_models = []
|
30 |
|
31 |
ix = 0
|
32 |
for type in ModelType:
|
33 |
+
if type.value.name == "":
|
34 |
+
continue
|
35 |
for size in intervals:
|
36 |
# We filter the df to gather the relevant models
|
37 |
type_emoji = [t[0] for t in type.value.symbol]
|
|
|
41 |
mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
|
42 |
filtered_df = filtered_df.loc[mask]
|
43 |
|
44 |
+
best_models = list(
|
45 |
+
filtered_df.sort_values(AutoEvalColumn.average.name, ascending=False)[AutoEvalColumn.dummy.name]
|
46 |
+
)
|
47 |
print(type.value.symbol, size, best_models[:10])
|
48 |
|
49 |
# We add them one by one to the leaderboard
|
|
|
52 |
cur_len_collection = len(collection.items)
|
53 |
try:
|
54 |
collection = add_collection_item(
|
55 |
+
PATH_TO_COLLECTION,
|
56 |
+
item_id=model,
|
57 |
+
item_type="model",
|
58 |
exists_ok=True,
|
59 |
+
note=f"Best {type.to_str(' ')} model of around {size} on the leaderboard today!",
|
60 |
+
token=H4_TOKEN,
|
61 |
)
|
62 |
+
if (
|
63 |
+
len(collection.items) > cur_len_collection
|
64 |
+
): # we added an item - we make sure its position is correct
|
65 |
+
item_object_id = collection.items[-1].item_object_id
|
66 |
+
update_collection_item(
|
67 |
+
collection_slug=PATH_TO_COLLECTION, item_object_id=item_object_id, position=ix
|
68 |
+
)
|
69 |
cur_len_collection = len(collection.items)
|
70 |
cur_best_models.append(model)
|
71 |
break
|
72 |
except HfHubHTTPError:
|
73 |
continue
|
74 |
|
75 |
+
collection = get_collection(PATH_TO_COLLECTION, token=H4_TOKEN)
|
76 |
for item in collection.items:
|
77 |
if item.item_id not in cur_best_models:
|
78 |
try:
|
79 |
+
delete_collection_item(
|
80 |
+
collection_slug=PATH_TO_COLLECTION, item_object_id=item.item_object_id, token=H4_TOKEN
|
81 |
+
)
|
82 |
except HfHubHTTPError:
|
83 |
continue
|
|
models_backlinks.py → src/tools/model_backlinks.py
RENAMED
File without changes
|
src/{plots/plot_results.py → tools/plots.py}
RENAMED
@@ -4,7 +4,7 @@ from plotly.graph_objs import Figure
|
|
4 |
import pickle
|
5 |
from datetime import datetime, timezone
|
6 |
from typing import List, Dict, Tuple, Any
|
7 |
-
from src.
|
8 |
|
9 |
# Average ⬆️ human baseline is 0.897 (source: averaging human baselines below)
|
10 |
# ARC human baseline is 0.80 (source: https://lab42.global/arc/)
|
@@ -220,4 +220,4 @@ def create_metric_plot_obj(
|
|
220 |
|
221 |
# Example Usage:
|
222 |
# human_baselines dictionary is defined.
|
223 |
-
# chart = create_metric_plot_obj(scores_df, ["ARC", "HellaSwag", "MMLU", "TruthfulQA"], human_baselines, "Graph Title")
|
|
|
4 |
import pickle
|
5 |
from datetime import datetime, timezone
|
6 |
from typing import List, Dict, Tuple, Any
|
7 |
+
from src.leaderboard.filter_models import FLAGGED_MODELS
|
8 |
|
9 |
# Average ⬆️ human baseline is 0.897 (source: averaging human baselines below)
|
10 |
# ARC human baseline is 0.80 (source: https://lab42.global/arc/)
|
|
|
220 |
|
221 |
# Example Usage:
|
222 |
# human_baselines dictionary is defined.
|
223 |
+
# chart = create_metric_plot_obj(scores_df, ["ARC", "HellaSwag", "MMLU", "TruthfulQA"], human_baselines, "Graph Title")
|