make a leaderboard

.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
@@ -33,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+scale-hf-logo.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,20 @@
+auto_evals/
+venv/
+__pycache__/
+.env
+.ipynb_checkpoints
+*ipynb
+.vscode/
+
+gpt_4_evals/
+human_evals/
+eval-queue/
+eval-results/
+eval-queue-private/
+eval-results-private/
+auto_evals/
+
+src/assets/model_counts.html
+
+**/.DS_Store
+.venv

.pre-commit-config.yaml

@@ -0,0 +1,53 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+default_language_version:
+  python: python3
+
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_schedule: quarterly
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: check-yaml
+      - id: check-case-conflict
+      - id: detect-private-key
+      - id: check-added-large-files
+        args: ['--maxkb=1000']
+      - id: requirements-txt-fixer
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        name: Format imports
+
+  - repo: https://github.com/psf/black
+    rev: 22.12.0
+    hooks:
+      - id: black
+        name: Format code
+        additional_dependencies: ['click==8.0.2']
+
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    # Ruff version.
+    rev: 'v0.0.267'
+    hooks:
+      - id: ruff

Makefile

@@ -0,0 +1,13 @@
+.PHONY: style format
+
+
+style:
+	python -m black --line-length 119 .
+	python -m isort .
+	ruff check --fix .
+
+
+quality:
+	python -m black --check --line-length 119 .
+	python -m isort --check-only .
+	ruff check .

app.py

@@ -0,0 +1,597 @@
+import json
+import os
+from datetime import datetime, timezone
+import re
+from distutils.util import strtobool
+
+import gradio as gr
+import pandas as pd
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import HfApi
+
+from src.assets.css_html_js import custom_css, get_window_url_params
+from src.assets.text_content import (
+    CITATION_BUTTON_LABEL,
+    CITATION_BUTTON_TEXT,
+    EVALUATION_QUEUE_TEXT,
+    INTRODUCTION_TEXT,
+    LLM_BENCHMARKS_TEXT,
+    TITLE,
+    BOTTOM_LOGO,
+)
+from src.display_models.get_model_metadata import DO_NOT_SUBMIT_MODELS, ModelType
+from src.display_models.utils import (
+    AutoEvalColumn,
+    EvalQueueColumn,
+    fields,
+    styled_error,
+    styled_message,
+    styled_warning,
+)
+from src.load_from_hub import get_evaluation_queue_df, get_leaderboard_df, is_model_on_hub, load_all_info_from_hub
+from src.rate_limiting import user_submission_permission
+
+pd.set_option("display.precision", 1)
+
+# clone / pull the lmeh eval data
+H4_TOKEN = os.environ.get("H4_TOKEN", None)
+
+QUEUE_REPO = "open-ko-llm-leaderboard/requests"
+RESULTS_REPO = "open-ko-llm-leaderboard/results"
+
+PRIVATE_QUEUE_REPO = "open-ko-llm-leaderboard/private-requests"
+PRIVATE_RESULTS_REPO = "open-ko-llm-leaderboard/private-results"
+
+IS_PUBLIC = bool(strtobool(os.environ.get("IS_PUBLIC", "True")))
+
+EVAL_REQUESTS_PATH = "eval-queue"
+EVAL_RESULTS_PATH = "eval-results"
+
+EVAL_REQUESTS_PATH_PRIVATE = "eval-queue-private"
+EVAL_RESULTS_PATH_PRIVATE = "eval-results-private"
+
+api = HfApi(token=H4_TOKEN)
+
+
+def restart_space():
+    api.restart_space(repo_id="upstage/open-ko-llm-leaderboard", token=H4_TOKEN)
+
+# Rate limit variables
+RATE_LIMIT_PERIOD = 7
+RATE_LIMIT_QUOTA = 5
+
+# Column selection
+COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
+TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]
+COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
+TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
+
+if not IS_PUBLIC:
+    COLS.insert(2, AutoEvalColumn.precision.name)
+    TYPES.insert(2, AutoEvalColumn.precision.type)
+
+EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
+EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
+
+BENCHMARK_COLS = [
+    c.name
+    for c in [
+        AutoEvalColumn.arc,
+        AutoEvalColumn.hellaswag,
+        AutoEvalColumn.mmlu,
+        AutoEvalColumn.truthfulqa,
+        AutoEvalColumn.commongen_v2,
+        # TODO: Uncomment when we have results for these
+        # AutoEvalColumn.ethicalverification,
+    ]
+]
+
+## LOAD INFO FROM HUB
+eval_queue, requested_models, eval_results, users_to_submission_dates = load_all_info_from_hub(
+    QUEUE_REPO, RESULTS_REPO, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH
+)
+
+if not IS_PUBLIC:
+    (eval_queue_private, requested_models_private, eval_results_private, _) = load_all_info_from_hub(
+        PRIVATE_QUEUE_REPO,
+        PRIVATE_RESULTS_REPO,
+        EVAL_REQUESTS_PATH_PRIVATE,
+        EVAL_RESULTS_PATH_PRIVATE,
+    )
+else:
+    eval_queue_private, eval_results_private = None, None
+
+original_df = get_leaderboard_df(eval_results, eval_results_private, COLS, BENCHMARK_COLS)
+models = original_df["model_name_for_query"].tolist() # needed for model backlinks in their to the leaderboard
+
+# Commented out because it causes infinite restart loops in local
+# to_be_dumped = f"models = {repr(models)}\n"
+
+# with open("models_backlinks.py", "w") as f:
+#     f.write(to_be_dumped)
+
+# print(to_be_dumped)
+
+leaderboard_df = original_df.copy()
+(
+    finished_eval_queue_df,
+    running_eval_queue_df,
+    pending_eval_queue_df,
+) = get_evaluation_queue_df(eval_queue, eval_queue_private, EVAL_REQUESTS_PATH, EVAL_COLS)
+
+## INTERACTION FUNCTIONS
+def add_new_eval(
+    model: str,
+    base_model: str,
+    revision: str,
+    precision: str,
+    private: bool,
+    weight_type: str,
+    model_type: str,
+):
+    precision = precision.split(" ")[0]
+    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+    num_models_submitted_in_period = user_submission_permission(model, users_to_submission_dates, RATE_LIMIT_PERIOD)
+    if num_models_submitted_in_period > RATE_LIMIT_QUOTA:
+        error_msg = f"Organisation or user `{model.split('/')[0]}`"
+        error_msg += f"already has {num_models_submitted_in_period} model requests submitted to the leaderboard "
+        error_msg += f"in the last {RATE_LIMIT_PERIOD} days.\n"
+        error_msg += "Please wait a couple of days before resubmitting, so that everybody can enjoy using the leaderboard 🤗"
+        return styled_error(error_msg)
+
+    if model_type is None or model_type == "":
+        return styled_error("Please select a model type.")
+
+    # check the model actually exists before adding the eval
+    if revision == "":
+        revision = "main"
+
+    if weight_type in ["Delta", "Adapter"]:
+        base_model_on_hub, error = is_model_on_hub(base_model, revision)
+        if not base_model_on_hub:
+            return styled_error(f'Base model "{base_model}" {error}')
+
+    if not weight_type == "Adapter":
+        model_on_hub, error = is_model_on_hub(model, revision)
+        if not model_on_hub:
+            return styled_error(f'Model "{model}" {error}')
+
+    print("adding new eval")
+
+    eval_entry = {
+        "model": model,
+        "base_model": base_model,
+        "revision": revision,
+        "private": private,
+        "precision": precision,
+        "weight_type": weight_type,
+        "status": "PENDING",
+        "submitted_time": current_time,
+        "model_type": model_type,
+    }
+
+    user_name = ""
+    model_path = model
+    if "/" in model:
+        user_name = model.split("/")[0]
+        model_path = model.split("/")[1]
+
+    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
+    os.makedirs(OUT_DIR, exist_ok=True)
+    out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json"
+
+    # Check if the model has been forbidden:
+    if out_path.split("eval-queue/")[1] in DO_NOT_SUBMIT_MODELS:
+        return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.")
+
+    # Check for duplicate submission
+    if f"{model}_{revision}_{precision}" in requested_models:
+        return styled_warning("This model has been already submitted.")
+
+    with open(out_path, "w") as f:
+        f.write(json.dumps(eval_entry))
+
+    api.upload_file(
+        path_or_fileobj=out_path,
+        path_in_repo=out_path.split("eval-queue/")[1],
+        repo_id=QUEUE_REPO,
+        repo_type="dataset",
+        commit_message=f"Add {model} to eval queue",
+    )
+
+    # remove the local file
+    os.remove(out_path)
+
+    return styled_message(
+        "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."
+    )
+
+
+# Basics
+def change_tab(query_param: str):
+    query_param = query_param.replace("'", '"')
+    query_param = json.loads(query_param)
+
+    if isinstance(query_param, dict) and "tab" in query_param and query_param["tab"] == "evaluation":
+        return gr.Tabs.update(selected=1)
+    else:
+        return gr.Tabs.update(selected=0)
+
+
+# Searching and filtering
+def update_table(hidden_df: pd.DataFrame, current_columns_df: pd.DataFrame, columns: list, type_query: list, precision_query: str, size_query: list, show_deleted: bool, query: str):
+    filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
+    if query != "":
+        filtered_df = search_table(filtered_df, query)
+    df = select_columns(filtered_df, columns)
+
+    return df
+
+def search_table(df: pd.DataFrame, query: str) -> pd.DataFrame:
+    return df[(df[AutoEvalColumn.dummy.name].str.contains(query, case=False))]
+
+def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
+    always_here_cols = [
+        AutoEvalColumn.model_type_symbol.name,
+        AutoEvalColumn.model.name,
+    ]
+    # We use COLS to maintain sorting
+    filtered_df = df[
+        always_here_cols + [c for c in COLS if c in df.columns and c in columns] + [AutoEvalColumn.dummy.name]
+    ]
+    return filtered_df
+
+NUMERIC_INTERVALS = {
+    "Unknown": pd.Interval(-1, 0, closed="right"),
+    "0~3B": pd.Interval(0, 3, closed="right"),
+    "3~7B": pd.Interval(3, 7, closed="right"),
+    "7~13B": pd.Interval(7, 13, closed="right"),
+    "13~35B": pd.Interval(13, 35, closed="right"),
+    "35~60B": pd.Interval(35, 60, closed="right"),
+    "60B+": pd.Interval(60, 10000, closed="right"),
+}
+
+def filter_models(
+    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
+) -> pd.DataFrame:
+    # Show all models
+    if show_deleted:
+        filtered_df = df
+    else:  # Show only still on the hub models
+        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
+
+    type_emoji = [t[0] for t in type_query]
+    filtered_df = filtered_df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = filtered_df[df[AutoEvalColumn.precision.name].isin(precision_query)]
+
+    numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
+    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
+    mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
+    filtered_df = filtered_df.loc[mask]
+
+    return filtered_df
+
+
+demo = gr.Blocks(css=custom_css)
+with demo:
+    gr.HTML(TITLE)
+    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+    with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
+            with gr.Row():
+                with gr.Column():
+                    with gr.Row():
+                        search_bar = gr.Textbox(
+                            placeholder=" 🔍 Search for your model and press ENTER...",
+                            show_label=False,
+                            elem_id="search-bar",
+                        )
+                    with gr.Row():
+                        shown_columns = gr.CheckboxGroup(
+                            choices=[
+                                c
+                                for c in COLS
+                                if c
+                                not in [
+                                    AutoEvalColumn.dummy.name,
+                                    AutoEvalColumn.model.name,
+                                    AutoEvalColumn.model_type_symbol.name,
+                                    AutoEvalColumn.still_on_hub.name,
+                                ]
+                            ],
+                            value=[
+                                c
+                                for c in COLS_LITE
+                                if c
+                                not in [
+                                    AutoEvalColumn.dummy.name,
+                                    AutoEvalColumn.model.name,
+                                    AutoEvalColumn.model_type_symbol.name,
+                                    AutoEvalColumn.still_on_hub.name,
+                                ]
+                            ],
+                            label="Select columns to show",
+                            elem_id="column-select",
+                            interactive=True,
+                        )
+                    with gr.Row():
+                        deleted_models_visibility = gr.Checkbox(
+                            value=True, label="👀 Show gated/private/deleted models", interactive=True
+                        )
+                with gr.Column(min_width=320):
+                    with gr.Box(elem_id="box-filter"):
+                        filter_columns_type = gr.CheckboxGroup(
+                            label="Model types",
+                            choices=[
+                                ModelType.PT.to_str(),
+                                # ModelType.FT.to_str(),
+                                ModelType.IFT.to_str(),
+                                ModelType.RL.to_str(),
+                            ],
+                            value=[
+                                ModelType.PT.to_str(),
+                                # ModelType.FT.to_str(),
+                                ModelType.IFT.to_str(),
+                                ModelType.RL.to_str(),
+                            ],
+                            interactive=True,
+                            elem_id="filter-columns-type",
+                        )
+                        filter_columns_precision = gr.CheckboxGroup(
+                            label="Precision",
+                            choices=["torch.float16"], #, "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
+                            value=["torch.float16"], #, "torch.bfloat16", "torch.float32", "8bit", "4bit", "GPTQ"],
+                            interactive=False,
+                            elem_id="filter-columns-precision",
+                        )
+                        filter_columns_size = gr.CheckboxGroup(
+                            label="Model sizes",
+                            choices=list(NUMERIC_INTERVALS.keys()),
+                            value=list(NUMERIC_INTERVALS.keys()),
+                            interactive=True,
+                            elem_id="filter-columns-size",
+                        )
+
+            leaderboard_table = gr.components.Dataframe(
+                value=leaderboard_df[
+                    [AutoEvalColumn.model_type_symbol.name, AutoEvalColumn.model.name]
+                    + shown_columns.value
+                    + [AutoEvalColumn.dummy.name]
+                ],
+                headers=[
+                    AutoEvalColumn.model_type_symbol.name,
+                    AutoEvalColumn.model.name,
+                ]
+                + shown_columns.value
+                + [AutoEvalColumn.dummy.name],
+                datatype=TYPES,
+                max_rows=None,
+                elem_id="leaderboard-table",
+                interactive=False,
+                visible=True,
+            )
+
+            # Dummy leaderboard for handling the case when the user uses backspace key
+            hidden_leaderboard_table_for_search = gr.components.Dataframe(
+                value=original_df,
+                headers=COLS,
+                datatype=TYPES,
+                max_rows=None,
+                visible=False,
+            )
+            search_bar.submit(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    leaderboard_table,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    deleted_models_visibility,
+                    search_bar,
+                ],
+                leaderboard_table,
+            )
+            shown_columns.change(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    leaderboard_table,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    deleted_models_visibility,
+                    search_bar,
+                ],
+                leaderboard_table,
+                queue=True,
+            )
+            filter_columns_type.change(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    leaderboard_table,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    deleted_models_visibility,
+                    search_bar,
+                ],
+                leaderboard_table,
+                queue=True,
+            )
+            filter_columns_precision.change(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    leaderboard_table,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    deleted_models_visibility,
+                    search_bar,
+                ],
+                leaderboard_table,
+                queue=True,
+            )
+            filter_columns_size.change(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    leaderboard_table,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    deleted_models_visibility,
+                    search_bar,
+                ],
+                leaderboard_table,
+                queue=True,
+            )
+            deleted_models_visibility.change(
+                update_table,
+                [
+                    hidden_leaderboard_table_for_search,
+                    leaderboard_table,
+                    shown_columns,
+                    filter_columns_type,
+                    filter_columns_precision,
+                    filter_columns_size,
+                    deleted_models_visibility,
+                    search_bar,
+                ],
+                leaderboard_table,
+                queue=True,
+            )
+        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
+            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
+            with gr.Column():
+                with gr.Row():
+                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+
+                with gr.Column():
+                    with gr.Accordion(
+                        f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            finished_eval_table = gr.components.Dataframe(
+                                value=finished_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                max_rows=5,
+                            )
+                    with gr.Accordion(
+                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            running_eval_table = gr.components.Dataframe(
+                                value=running_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                max_rows=5,
+                            )
+
+                    with gr.Accordion(
+                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
+                        open=False,
+                    ):
+                        with gr.Row():
+                            pending_eval_table = gr.components.Dataframe(
+                                value=pending_eval_queue_df,
+                                headers=EVAL_COLS,
+                                datatype=EVAL_TYPES,
+                                max_rows=5,
+                            )
+            with gr.Row():
+                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
+
+            with gr.Row():
+                with gr.Column():
+                    model_name_textbox = gr.Textbox(label="Model name")
+                    revision_name_textbox = gr.Textbox(label="Revision", placeholder="main")
+                    private = gr.Checkbox(False, label="Private", visible=not IS_PUBLIC)
+                    model_type = gr.Dropdown(
+                        choices=[
+                            ModelType.PT.to_str(" : "),
+                            # ModelType.FT.to_str(" : "),
+                            ModelType.IFT.to_str(" : "),
+                            ModelType.RL.to_str(" : "),
+                        ],
+                        label="Model type",
+                        multiselect=False,
+                        value=None,
+                        interactive=True,
+                    )
+
+                with gr.Column():
+                    precision = gr.Dropdown(
+                        choices=[
+                            "float16",
+                            # "bfloat16",
+                            # "8bit (LLM.int8)",
+                            # "4bit (QLoRA / FP4)",
+                            # "GPTQ"
+                        ],
+                        label="Precision",
+                        multiselect=False,
+                        value="float16",
+                        interactive=True,
+                    )
+                    weight_type = gr.Dropdown(
+                        choices=["Original", "Delta", "Adapter"],
+                        label="Weights type",
+                        multiselect=False,
+                        value="Original",
+                        interactive=True,
+                    )
+                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
+
+            submit_button = gr.Button("Submit Evalulation!")
+            submission_result = gr.Markdown()
+            submit_button.click(
+                add_new_eval,
+                [
+                    model_name_textbox,
+                    base_model_name_textbox,
+                    revision_name_textbox,
+                    precision,
+                    private,
+                    weight_type,
+                    model_type,
+                ],
+                submission_result,
+            )
+
+    with gr.Row():
+        with gr.Accordion("📙 Citation", open=False):
+            citation_button = gr.Textbox(
+                value=CITATION_BUTTON_TEXT,
+                label=CITATION_BUTTON_LABEL,
+                elem_id="citation-button",
+            ).style(show_copy_button=True)
+
+    gr.HTML(BOTTOM_LOGO)
+
+    dummy = gr.Textbox(visible=False)
+    demo.load(
+        change_tab,
+        dummy,
+        tabs,
+        _js=get_window_url_params,
+    )
+
+scheduler = BackgroundScheduler()
+scheduler.add_job(restart_space, "interval", seconds=1800)
+scheduler.start()
+demo.queue(concurrency_count=40).launch()

model_info_cache.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6579e330063066b049d778cb4dbb548289e9fb570492ff444d42ef490234e379
+size 216682

model_size_cache.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:316daa2fa787ce5e521a0c39fc0f40b5ba7e084ec033b6e893b7712269ec11a3
+size 5559

models_backlinks.py

@@ -0,0 +1 @@
+models = ['upstage/Llama-2-70b-instruct-v2', 'upstage/Llama-2-70b-instruct', 'upstage/llama-65b-instruct', 'upstage/llama-65b-instruct', 'upstage/llama-30b-instruct-2048', 'upstage/llama-30b-instruct', 'baseline']

pyproject.toml

@@ -0,0 +1,13 @@
+[tool.ruff]
+# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
+select = ["E", "F"]
+ignore = ["E501"] # line too long (black is taking care of this)
+line-length = 119
+fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+
+[tool.isort]
+profile = "black"
+line_length = 119
+
+[tool.black]
+line-length = 119

requirements.txt

@@ -0,0 +1,71 @@
+accelerate==0.23.0
+aiofiles==23.1.0
+aiohttp==3.8.4
+aiosignal==1.3.1
+altair==4.2.2
+anyio==3.6.2
+APScheduler==3.10.1
+async-timeout==4.0.2
+attrs==23.1.0
+certifi==2022.12.7
+charset-normalizer==3.1.0
+click==8.1.3
+contourpy==1.0.7
+cycler==0.11.0
+datasets==2.12.0
+entrypoints==0.4
+fastapi==0.95.1
+ffmpy==0.3.0
+filelock==3.11.0
+fonttools==4.39.3
+frozenlist==1.3.3
+fsspec==2023.4.0
+gradio==3.43.2
+gradio-client==0.5.0
+h11==0.14.0
+httpcore==0.17.0
+httpx==0.24.0
+huggingface-hub==0.16.4
+idna==3.4
+Jinja2==3.1.2
+jsonschema==4.17.3
+kiwisolver==1.4.4
+linkify-it-py==2.0.0
+markdown-it-py==2.2.0
+MarkupSafe==2.1.2
+matplotlib==3.7.1
+mdit-py-plugins==0.3.3
+mdurl==0.1.2
+multidict==6.0.4
+numpy==1.24.2
+orjson==3.8.10
+packaging==23.1
+pandas==2.0.0
+Pillow==9.5.0
+plotly==5.14.1
+pyarrow==11.0.0
+pydantic==1.10.7
+pydub==0.25.1
+pyparsing==3.0.9
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2023.3
+pytz-deprecation-shim==0.1.0.post0
+PyYAML==6.0
+requests==2.28.2
+semantic-version==2.10.0
+six==1.16.0
+sniffio==1.3.0
+starlette==0.26.1
+toolz==0.12.0
+tqdm==4.65.0
+transformers==4.34.0
+typing_extensions==4.5.0
+tzdata==2023.3
+tzlocal==4.3
+uc-micro-py==1.0.1
+urllib3==1.26.15
+uvicorn==0.21.1
+websockets==11.0.1
+yarl==1.8.2

src/assets/css_html_js.py

@@ -0,0 +1,111 @@
+custom_css = """
+
+.markdown-text {
+    font-size: 16px !important;
+}
+
+#models-to-add-text {
+    font-size: 18px !important;
+}
+
+#citation-button span {
+    font-size: 16px !important;
+}
+
+#citation-button textarea {
+    font-size: 16px !important;
+}
+
+#citation-button > label > button {
+    margin: 6px;
+    transform: scale(1.3);
+}
+
+#leaderboard-table {
+    margin-top: 15px
+}
+
+#leaderboard-table-lite {
+    margin-top: 15px
+}
+
+#search-bar-table-box > div:first-child {
+    background: none;
+    border: none;
+}
+ 
+#search-bar {
+    padding: 0px;
+}
+
+/* Hides the final AutoEvalColumn */
+#llm-benchmark-tab-table table td:last-child,
+#llm-benchmark-tab-table table th:last-child {
+    display: none;
+}
+
+/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
+table td:first-child,
+table th:first-child {
+    max-width: 400px;
+    overflow: auto;
+    white-space: nowrap;
+}
+
+.tab-buttons button {
+    font-size: 20px;
+}
+
+#scale-logo {
+    border-style: none !important;
+    box-shadow: none;
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+    max-width: 600px;
+}
+
+#scale-logo .download {
+    display: none;
+}
+#filter_type{
+    border: 0;
+    padding-left: 0;
+    padding-top: 0;
+}
+#filter_type label {
+    display: flex;
+}
+#filter_type label > span{
+    margin-top: var(--spacing-lg);
+    margin-right: 0.5em;
+}
+#filter_type label > .wrap{
+    width: 103px;
+}
+#filter_type label > .wrap .wrap-inner{  
+    padding: 2px;
+}
+#filter_type label > .wrap .wrap-inner input{
+    width: 1px
+}
+#filter-columns-type{
+    border:0;
+    padding:0.5;
+}
+#filter-columns-size{
+    border:0;
+    padding:0.5;
+}
+#box-filter > .form{
+    border: 0
+}
+"""
+
+get_window_url_params = """
+    function(url_params) {
+        const params = new URLSearchParams(window.location.search);
+        url_params = Object.fromEntries(params);
+        return url_params;
+    }
+    """

src/assets/hardcoded_evals.py

@@ -0,0 +1,14 @@
+from src.display_models.utils import AutoEvalColumn, model_hyperlink
+
+baseline = {
+    AutoEvalColumn.model.name: "<p>Baseline</p>",
+    AutoEvalColumn.revision.name: "N/A",
+    AutoEvalColumn.precision.name: None,
+    AutoEvalColumn.average.name: 25.0,
+    AutoEvalColumn.arc.name: 25.0,
+    AutoEvalColumn.hellaswag.name: 25.0,
+    AutoEvalColumn.mmlu.name: 25.0,
+    AutoEvalColumn.truthfulqa.name: 25.0,
+    AutoEvalColumn.dummy.name: "baseline",
+    AutoEvalColumn.model_type.name: "",
+}

src/assets/text_content.py

@@ -0,0 +1,159 @@
+from src.display_models.model_metadata_type import ModelType
+
+TITLE = """<img src="https://upstage-open-ko-llm-leaderboard-logos.s3.ap-northeast-2.amazonaws.com/header_logo.png" style="width:30%;display:block;margin-left:auto;margin-right:auto">"""
+BOTTOM_LOGO = """<img src="https://upstage-open-ko-llm-leaderboard-logos.s3.ap-northeast-2.amazonaws.com/footer_logo_1.png" style="width:50%;display:block;margin-left:auto;margin-right:auto">"""
+
+INTRODUCTION_TEXT = f"""
+🚀 The Open Ko-LLM Leaderboard 🇰🇷 objectively evaluates the performance of Korean Large Language Model (LLM).
+
+When you submit a model on the "Submit here!" page, it is automatically evaluated. The GPU used for evaluation is operated with the support of  __[KT](https://cloud.kt.com/)__.
+The data used for evaluation consists of datasets to assess reasoning, language understanding, hallucination, and commonsense. 
+The evaluation dataset is exclusively private and only available for evaluation process.
+More detailed information about the benchmark dataset is provided on the “About” page.
+
+This leaderboard is co-hosted by __[Upstage](https://www.upstage.ai)__, and __[NIA](https://www.nia.or.kr/site/nia_kor/main.do)__ that provides various Korean Data Sets through __[AI-Hub](https://aihub.or.kr)__, and operated by __[Upstage](https://www.upstage.ai)__.
+"""
+
+LLM_BENCHMARKS_TEXT = f"""
+# Context
+While outstanding LLM models are being released competitively, most of them are centered on English and are familiar with the English cultural sphere. We operate the Korean leaderboard, 🚀 Open Ko-LLM, to evaluate models that reflect the characteristics of the Korean language and Korean culture. Through this, we hope that users can conveniently use the leaderboard, participate, and contribute to the advancement of research in Korean.
+
+## Icons
+{ModelType.PT.to_str(" : ")} model
+{ModelType.FT.to_str(" : ")} model
+{ModelType.IFT.to_str(" : ")} model
+{ModelType.RL.to_str(" : ")} model
+If there is no icon, it indicates that there is insufficient information about the model.
+Please provide information about the model through an issue! 🤩
+
+🏴‍☠️ : This icon indicates that the model has been selected as a subject of caution by the community, implying that users should exercise restraint when using it. Clicking on the icon will take you to a discussion about that model.
+(Models that have used the evaluation set for training to achieve a high leaderboard ranking, among others, are selected as subjects of caution.)
+
+## How it works
+
+📈 We evaluate models using the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness), a unified framework to test generative language models on a large number of different evaluation tasks.
+
+We have set up a benchmark using datasets translated into Korean, and applied variations by human experts, from the four tasks (HellaSwag, MMLU, Arc, Truthful QA) operated by HuggingFace OpenLLM. We have also added a new dataset prepared from scratch.
+- Ko-HellaSwag (provided by __[Upstage](https://www.upstage.ai/)__, machine translation)
+- Ko-MMLU (provided by __[Upstage](https://www.upstage.ai/)__, human translation and variation)
+- Ko-Arc (provided by __[Upstage](https://www.upstage.ai/)__, human translation and variation)
+- Ko-Truthful QA (provided by __[Upstage](https://www.upstage.ai/)__, human translation and variation)
+- Ko-CommonGen V2 (provided by __[Korea University NLP&AI Lab](http://nlp.korea.ac.kr/)__, created from scratch)
+
+To provide an evaluation befitting the LLM era, we've selected benchmark datasets suitable for assessing these elements: expertise, inference, hallucination, and common sense. The final score is converted to the average score from each evaluation datasets.
+
+GPUs are provided by __[KT](https://cloud.kt.com/)__ for the evaluations.
+
+## Details and Logs
+- Detailed numerical results in the `results` Upstage dataset: https://huggingface.co/datasets/open-ko-llm-leaderboard/results
+- Community queries and running status in the `requests` Upstage dataset: https://huggingface.co/datasets/open-ko-llm-leaderboard/requests
+
+## More resources
+If you still have questions, you can check our FAQ [here](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard/discussions/1)!
+"""
+
+EVALUATION_QUEUE_TEXT = f"""
+# Evaluation Queue for the 🚀 Open Ko-LLM Leaderboard
+Models added here will be automatically evaluated on the KT GPU cluster.
+
+## <Some good practices before submitting a model>
+
+### 1️⃣ Make sure you can load your model and tokenizer using AutoClasses
+```python
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+config = AutoConfig.from_pretrained("your model name", revision=revision)
+model = AutoModel.from_pretrained("your model name", revision=revision)
+tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
+```
+
+If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
+
+⚠️ Make sure your model is public!
+
+⚠️ Maker sure your model runs with [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness)
+
+⚠️ If your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
+
+### 2️⃣ Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
+It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
+
+### 3️⃣ Make sure your model has an open license!
+This is a leaderboard for 🚀 Open Ko-LLMs, and we'd love for as many people as possible to know they can use your model
+
+### 4️⃣ Fill up your model card
+When we add extra information about models to the leaderboard, it will be automatically taken from the model card
+
+## In case of model failure
+If your model is displayed in the `FAILED` category, its execution stopped. Make sure you have followed the above steps first. If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
+"""
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results. Authors of open-ko-llm-leaderboard are ordered alphabetically."
+CITATION_BUTTON_TEXT = r"""
+@misc{open-ko-llm-leaderboard,
+  author = {Chanjun Park, Hwalsuk Lee, Hyunbyung Park, Hyeonwoo Kim, Sanghoon Kim, Seonghwan Cho, Sunghun Kim, Sukyung Lee},
+  title = {Open Ko-LLM Leaderboard},
+  year = {2023},
+  publisher = {Upstage, National Information Society Agency},
+  howpublished = "\url{https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard}"
+}
+@software{eval-harness,
+  author       = {Gao, Leo and
+                  Tow, Jonathan and
+                  Biderman, Stella and
+                  Black, Sid and
+                  DiPofi, Anthony and
+                  Foster, Charles and
+                  Golding, Laurence and
+                  Hsu, Jeffrey and
+                  McDonell, Kyle and
+                  Muennighoff, Niklas and
+                  Phang, Jason and
+                  Reynolds, Laria and
+                  Tang, Eric and
+                  Thite, Anish and
+                  Wang, Ben and
+                  Wang, Kevin and
+                  Zou, Andy},
+  title        = {A framework for few-shot language model evaluation},
+  month        = sep,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {v0.0.1},
+  doi          = {10.5281/zenodo.5371628},
+  url          = {https://doi.org/10.5281/zenodo.5371628}
+}
+@misc{seo2023kocommongen,
+      title={Korean Commonsense Reasoning Evaluation for Large Language Models},
+      author={Jaehyung Seo, Chanjun Park, Hyeonseok Moon, Sugyeong Eo, Aram So, Heuiseok Lim},
+      year={2023},
+      affilation={Korea University, NLP&AI},
+      booktitle={Proceedings of the 35th Annual Conference on Human & Cognitive Language Technology}}
+@misc{park2023koarc,
+      title={Ko-ARC},
+      original_title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
+      author={Hyunbyung Park, Chanjun Park},
+      original_author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
+      year={2023}
+}
+@misc{park2023kohellaswag,
+      title={Ko-HellaSwag},
+      original_title={HellaSwag: Can a Machine Really Finish Your Sentence?},
+      author={Hyunbyung Park, Chanjun Park},
+      original_author={Rowan Zellers and Ari Holtzman and Yonatan Bisk and Ali Farhadi and Yejin Choi},
+      year={2023}
+}
+@misc{park2023kommlu,
+      title={Ko-MMLU},
+      original_title={Measuring Massive Multitask Language Understanding},
+      author={Hyunbyung Park, Chanjun Park},
+      original_author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
+      year={2023}
+}
+@misc{park2023kotruthfulqa,
+      title={Ko-TruthfulQA},
+      original_title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
+      author={Hyunbyung Park, Chanjun Park},
+      original_author={Stephanie Lin and Jacob Hilton and Owain Evans},
+      year={2023}
+}
+"""

src/display_models/get_model_metadata.py

@@ -0,0 +1,167 @@
+import glob
+import json
+import os
+import re
+import pickle
+from typing import List
+
+import huggingface_hub
+from huggingface_hub import HfApi
+from tqdm import tqdm
+from transformers import AutoModel, AutoConfig
+from accelerate import init_empty_weights
+
+from src.display_models.model_metadata_flags import DO_NOT_SUBMIT_MODELS, FLAGGED_MODELS
+from src.display_models.model_metadata_type import MODEL_TYPE_METADATA, ModelType, model_type_from_str
+from src.display_models.utils import AutoEvalColumn, model_hyperlink
+
+api = HfApi(token=os.environ.get("H4_TOKEN", None))
+
+
+def get_model_infos_from_hub(leaderboard_data: List[dict]):
+    # load cache from disk
+    try:
+        with open("model_info_cache.pkl", "rb") as f:
+            model_info_cache = pickle.load(f)
+    except (EOFError, FileNotFoundError):
+        model_info_cache = {}
+    try:
+        with open("model_size_cache.pkl", "rb") as f:
+            model_size_cache = pickle.load(f)
+    except (EOFError, FileNotFoundError):
+        model_size_cache = {}
+
+    for model_data in tqdm(leaderboard_data):
+        model_name = model_data["model_name_for_query"]
+
+        if model_name in model_info_cache:
+            model_info = model_info_cache[model_name]
+        else:
+            try:
+                model_info = api.model_info(model_name)
+                model_info_cache[model_name] = model_info
+            except huggingface_hub.utils._errors.RepositoryNotFoundError:
+                print("Repo not found!", model_name)
+                model_data[AutoEvalColumn.license.name] = None
+                model_data[AutoEvalColumn.likes.name] = None
+                if model_name not in model_size_cache:
+                    model_size_cache[model_name] = get_model_size(model_name, None)
+                model_data[AutoEvalColumn.params.name] = model_size_cache[model_name]
+
+        model_data[AutoEvalColumn.license.name] = get_model_license(model_info)
+        model_data[AutoEvalColumn.likes.name] = get_model_likes(model_info)
+        if model_name not in model_size_cache:
+            model_size_cache[model_name] = get_model_size(model_name, model_info)
+        model_data[AutoEvalColumn.params.name] = model_size_cache[model_name]
+    
+    # save cache to disk in pickle format
+    with open("model_info_cache.pkl", "wb") as f:
+        pickle.dump(model_info_cache, f)
+    with open("model_size_cache.pkl", "wb") as f:
+        pickle.dump(model_size_cache, f)
+
+
+def get_model_license(model_info):
+    try:
+        return model_info.cardData["license"]
+    except Exception:
+        return "?"
+
+
+def get_model_likes(model_info):
+    return model_info.likes
+
+
+size_pattern = re.compile(r"(\d\.)?\d+(b|m)")
+
+
+def get_model_size(model_name, model_info):
+    # In billions
+    try:
+        return round(model_info.safetensors["total"] / 1e9, 3)
+    except AttributeError:
+        try:
+            config = AutoConfig.from_pretrained(model_name, trust_remote_code=False)
+            with init_empty_weights():
+                model = AutoModel.from_config(config, trust_remote_code=False)
+            return round(sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e9, 3)
+        except (EnvironmentError, ValueError): # model config not found, likely private  
+            try:
+                size_match = re.search(size_pattern, model_name.lower())
+                size = size_match.group(0)
+                return round(float(size[:-1]) if size[-1] == "b" else float(size[:-1]) / 1e3, 3)
+            except AttributeError:
+                return 0
+
+
+def get_model_type(leaderboard_data: List[dict]):
+    for model_data in leaderboard_data:
+        request_files = os.path.join(
+            "eval-queue",
+            model_data["model_name_for_query"] + "_eval_request_*" + ".json",
+        )
+        request_files = glob.glob(request_files)
+
+        # Select correct request file (precision)
+        request_file = ""
+        if len(request_files) == 1:
+            request_file = request_files[0]
+        elif len(request_files) > 1:
+            request_files = sorted(request_files, reverse=True)
+            for tmp_request_file in request_files:
+                with open(tmp_request_file, "r") as f:
+                    req_content = json.load(f)
+                    if (
+                        req_content["status"] == "FINISHED"
+                        and req_content["precision"] == model_data["Precision"].split(".")[-1]
+                    ):
+                        request_file = tmp_request_file
+
+        try:
+            with open(request_file, "r") as f:
+                request = json.load(f)
+            model_type = model_type_from_str(request["model_type"])
+            model_data[AutoEvalColumn.model_type.name] = model_type.value.name
+            model_data[AutoEvalColumn.model_type_symbol.name] = model_type.value.symbol  # + ("🔺" if is_delta else "")
+        except Exception:
+            if model_data["model_name_for_query"] in MODEL_TYPE_METADATA:
+                model_data[AutoEvalColumn.model_type.name] = MODEL_TYPE_METADATA[
+                    model_data["model_name_for_query"]
+                ].value.name
+                model_data[AutoEvalColumn.model_type_symbol.name] = MODEL_TYPE_METADATA[
+                    model_data["model_name_for_query"]
+                ].value.symbol  # + ("🔺" if is_delta else "")
+            else:
+                model_data[AutoEvalColumn.model_type.name] = ModelType.Unknown.value.name
+                model_data[AutoEvalColumn.model_type_symbol.name] = ModelType.Unknown.value.symbol
+
+
+def flag_models(leaderboard_data: List[dict]):
+    for model_data in leaderboard_data:
+        if model_data["model_name_for_query"] in FLAGGED_MODELS:
+            issue_num = FLAGGED_MODELS[model_data["model_name_for_query"]].split("/")[-1]
+            issue_link = model_hyperlink(
+                FLAGGED_MODELS[model_data["model_name_for_query"]],
+                f"See discussion #{issue_num}",
+            )
+            model_data[
+                AutoEvalColumn.model.name
+            ] = f"{model_data[AutoEvalColumn.model.name]} has been flagged! {issue_link}"
+
+
+def remove_forbidden_models(leaderboard_data: List[dict]):
+    indices_to_remove = []
+    for ix, model in enumerate(leaderboard_data):
+        if model["model_name_for_query"] in DO_NOT_SUBMIT_MODELS:
+            indices_to_remove.append(ix)
+
+    for ix in reversed(indices_to_remove):
+        leaderboard_data.pop(ix)
+    return leaderboard_data
+
+
+def apply_metadata(leaderboard_data: List[dict]):
+    leaderboard_data = remove_forbidden_models(leaderboard_data)
+    get_model_type(leaderboard_data)
+    get_model_infos_from_hub(leaderboard_data)
+    flag_models(leaderboard_data)

src/display_models/model_metadata_flags.py

@@ -0,0 +1,8 @@
+# Models which have been flagged by users as being problematic for a reason or another
+# (Model name to forum discussion link)
+FLAGGED_MODELS = {
+}
+
+# Models which have been requested by orgs to not be submitted on the leaderboard
+DO_NOT_SUBMIT_MODELS = [
+]

src/display_models/model_metadata_type.py

@@ -0,0 +1,553 @@
+from dataclasses import dataclass
+from enum import Enum
+from typing import Dict
+
+
+@dataclass
+class ModelInfo:
+    name: str
+    symbol: str  # emoji
+
+
+class ModelType(Enum):
+    PT = ModelInfo(name="pretrained", symbol="🟢")
+    FT = ModelInfo(name="fine-tuned", symbol="🔶")
+    IFT = ModelInfo(name="instruction-tuned", symbol="⭕")
+    RL = ModelInfo(name="RL-tuned", symbol="🟦")
+    Unknown = ModelInfo(name="Unknown, add type to request file!", symbol="?")
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
+
+MODEL_TYPE_METADATA: Dict[str, ModelType] = {
+    "tiiuae/falcon-180B": ModelType.PT,
+    "Qwen/Qwen-7B": ModelType.PT,
+    "Qwen/Qwen-7B-Chat": ModelType.RL,
+    "notstoic/PygmalionCoT-7b": ModelType.IFT,
+    "aisquared/dlite-v1-355m": ModelType.IFT,
+    "aisquared/dlite-v1-1_5b": ModelType.IFT,
+    "aisquared/dlite-v1-774m": ModelType.IFT,
+    "aisquared/dlite-v1-124m": ModelType.IFT,
+    "aisquared/chopt-2_7b": ModelType.IFT,
+    "aisquared/dlite-v2-124m": ModelType.IFT,
+    "aisquared/dlite-v2-774m": ModelType.IFT,
+    "aisquared/dlite-v2-1_5b": ModelType.IFT,
+    "aisquared/chopt-1_3b": ModelType.IFT,
+    "aisquared/dlite-v2-355m": ModelType.IFT,
+    "augtoma/qCammel-13": ModelType.IFT,
+    "Aspik101/Llama-2-7b-hf-instruct-pl-lora_unload": ModelType.IFT,
+    "Aspik101/vicuna-7b-v1.3-instruct-pl-lora_unload": ModelType.IFT,
+    "TheBloke/alpaca-lora-65B-HF": ModelType.FT,
+    "TheBloke/tulu-7B-fp16": ModelType.IFT,
+    "TheBloke/guanaco-7B-HF": ModelType.FT,
+    "TheBloke/koala-7B-HF": ModelType.FT,
+    "TheBloke/wizardLM-7B-HF": ModelType.IFT,
+    "TheBloke/airoboros-13B-HF": ModelType.IFT,
+    "TheBloke/koala-13B-HF": ModelType.FT,
+    "TheBloke/Wizard-Vicuna-7B-Uncensored-HF": ModelType.FT,
+    "TheBloke/dromedary-65b-lora-HF": ModelType.IFT,
+    "TheBloke/wizardLM-13B-1.0-fp16": ModelType.IFT,
+    "TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-fp16": ModelType.FT,
+    "TheBloke/Wizard-Vicuna-30B-Uncensored-fp16": ModelType.FT,
+    "TheBloke/wizard-vicuna-13B-HF": ModelType.IFT,
+    "TheBloke/UltraLM-13B-fp16": ModelType.IFT,
+    "TheBloke/OpenAssistant-FT-7-Llama-30B-HF": ModelType.FT,
+    "TheBloke/vicuna-13B-1.1-HF": ModelType.IFT,
+    "TheBloke/guanaco-13B-HF": ModelType.FT,
+    "TheBloke/guanaco-65B-HF": ModelType.FT,
+    "TheBloke/airoboros-7b-gpt4-fp16": ModelType.IFT,
+    "TheBloke/llama-30b-supercot-SuperHOT-8K-fp16": ModelType.IFT,
+    "TheBloke/Llama-2-13B-fp16": ModelType.PT,
+    "TheBloke/llama-2-70b-Guanaco-QLoRA-fp16": ModelType.FT,
+    "TheBloke/landmark-attention-llama7b-fp16": ModelType.IFT,
+    "TheBloke/Planner-7B-fp16": ModelType.IFT,
+    "TheBloke/Wizard-Vicuna-13B-Uncensored-HF": ModelType.FT,
+    "TheBloke/gpt4-alpaca-lora-13B-HF": ModelType.IFT,
+    "TheBloke/gpt4-x-vicuna-13B-HF": ModelType.IFT,
+    "TheBloke/gpt4-alpaca-lora_mlp-65B-HF": ModelType.IFT,
+    "TheBloke/tulu-13B-fp16": ModelType.IFT,
+    "TheBloke/VicUnlocked-alpaca-65B-QLoRA-fp16": ModelType.IFT,
+    "TheBloke/Llama-2-70B-fp16": ModelType.IFT,
+    "TheBloke/WizardLM-30B-fp16": ModelType.IFT,
+    "TheBloke/robin-13B-v2-fp16": ModelType.FT,
+    "TheBloke/robin-33B-v2-fp16": ModelType.FT,
+    "TheBloke/Vicuna-13B-CoT-fp16": ModelType.IFT,
+    "TheBloke/Vicuna-33B-1-3-SuperHOT-8K-fp16": ModelType.IFT,
+    "TheBloke/Wizard-Vicuna-30B-Superhot-8K-fp16": ModelType.FT,
+    "TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16": ModelType.IFT,
+    "TheBloke/GPlatty-30B-SuperHOT-8K-fp16": ModelType.FT,
+    "TheBloke/CAMEL-33B-Combined-Data-SuperHOT-8K-fp16": ModelType.IFT,
+    "TheBloke/Chinese-Alpaca-33B-SuperHOT-8K-fp16": ModelType.IFT,
+    "jphme/orca_mini_v2_ger_7b": ModelType.IFT,
+    "Ejafa/vicuna_7B_vanilla_1.1": ModelType.FT,
+    "kevinpro/Vicuna-13B-CoT": ModelType.IFT,
+    "AlekseyKorshuk/pygmalion-6b-vicuna-chatml": ModelType.FT,
+    "AlekseyKorshuk/chatml-pyg-v1": ModelType.FT,
+    "concedo/Vicuzard-30B-Uncensored": ModelType.FT,
+    "concedo/OPT-19M-ChatSalad": ModelType.FT,
+    "concedo/Pythia-70M-ChatSalad": ModelType.FT,
+    "digitous/13B-HyperMantis": ModelType.IFT,
+    "digitous/Adventien-GPTJ": ModelType.FT,
+    "digitous/Alpacino13b": ModelType.IFT,
+    "digitous/GPT-R": ModelType.IFT,
+    "digitous/Javelin-R": ModelType.IFT,
+    "digitous/Javalion-GPTJ": ModelType.IFT,
+    "digitous/Javalion-R": ModelType.IFT,
+    "digitous/Skegma-GPTJ": ModelType.FT,
+    "digitous/Alpacino30b": ModelType.IFT,
+    "digitous/Janin-GPTJ": ModelType.FT,
+    "digitous/Janin-R": ModelType.FT,
+    "digitous/Javelin-GPTJ": ModelType.FT,
+    "SaylorTwift/gpt2_test": ModelType.PT,
+    "anton-l/gpt-j-tiny-random": ModelType.FT,
+    "Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca": ModelType.FT,
+    "Lazycuber/pyg-instruct-wizardlm": ModelType.FT,
+    "Lazycuber/Janemalion-6B": ModelType.FT,
+    "IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1": ModelType.FT,
+    "IDEA-CCNL/Ziya-LLaMA-13B-v1": ModelType.IFT,
+    "dsvv-cair/alpaca-cleaned-llama-30b-bf16": ModelType.FT,
+    "gpt2-medium": ModelType.PT,
+    "camel-ai/CAMEL-13B-Combined-Data": ModelType.IFT,
+    "camel-ai/CAMEL-13B-Role-Playing-Data": ModelType.FT,
+    "camel-ai/CAMEL-33B-Combined-Data": ModelType.IFT,
+    "PygmalionAI/pygmalion-6b": ModelType.FT,
+    "PygmalionAI/metharme-1.3b": ModelType.IFT,
+    "PygmalionAI/pygmalion-1.3b": ModelType.FT,
+    "PygmalionAI/pygmalion-350m": ModelType.FT,
+    "PygmalionAI/pygmalion-2.7b": ModelType.FT,
+    "medalpaca/medalpaca-7b": ModelType.FT,
+    "lilloukas/Platypus-30B": ModelType.IFT,
+    "lilloukas/GPlatty-30B": ModelType.FT,
+    "mncai/chatdoctor": ModelType.FT,
+    "chaoyi-wu/MedLLaMA_13B": ModelType.FT,
+    "LoupGarou/WizardCoder-Guanaco-15B-V1.0": ModelType.IFT,
+    "LoupGarou/WizardCoder-Guanaco-15B-V1.1": ModelType.FT,
+    "hakurei/instruct-12b": ModelType.IFT,
+    "hakurei/lotus-12B": ModelType.FT,
+    "shibing624/chinese-llama-plus-13b-hf": ModelType.IFT,
+    "shibing624/chinese-alpaca-plus-7b-hf": ModelType.IFT,
+    "shibing624/chinese-alpaca-plus-13b-hf": ModelType.IFT,
+    "mosaicml/mpt-7b-instruct": ModelType.IFT,
+    "mosaicml/mpt-30b-chat": ModelType.IFT,
+    "mosaicml/mpt-7b-storywriter": ModelType.FT,
+    "mosaicml/mpt-30b-instruct": ModelType.IFT,
+    "mosaicml/mpt-7b-chat": ModelType.IFT,
+    "mosaicml/mpt-30b": ModelType.PT,
+    "Corianas/111m": ModelType.IFT,
+    "Corianas/Quokka_1.3b": ModelType.IFT,
+    "Corianas/256_5epoch": ModelType.FT,
+    "Corianas/Quokka_256m": ModelType.IFT,
+    "Corianas/Quokka_590m": ModelType.IFT,
+    "Corianas/gpt-j-6B-Dolly": ModelType.FT,
+    "Corianas/Quokka_2.7b": ModelType.IFT,
+    "cyberagent/open-calm-7b": ModelType.FT,
+    "Aspik101/Nous-Hermes-13b-pl-lora_unload": ModelType.IFT,
+    "THUDM/chatglm2-6b": ModelType.IFT,
+    "MetaIX/GPT4-X-Alpasta-30b": ModelType.IFT,
+    "NYTK/PULI-GPTrio": ModelType.PT,
+    "EleutherAI/pythia-1.3b": ModelType.PT,
+    "EleutherAI/pythia-2.8b-deduped": ModelType.PT,
+    "EleutherAI/gpt-neo-125m": ModelType.PT,
+    "EleutherAI/pythia-160m": ModelType.PT,
+    "EleutherAI/gpt-neo-2.7B": ModelType.PT,
+    "EleutherAI/pythia-1b-deduped": ModelType.PT,
+    "EleutherAI/pythia-6.7b": ModelType.PT,
+    "EleutherAI/pythia-70m-deduped": ModelType.PT,
+    "EleutherAI/gpt-neox-20b": ModelType.PT,
+    "EleutherAI/pythia-1.4b-deduped": ModelType.PT,
+    "EleutherAI/pythia-2.7b": ModelType.PT,
+    "EleutherAI/pythia-6.9b-deduped": ModelType.PT,
+    "EleutherAI/pythia-70m": ModelType.PT,
+    "EleutherAI/gpt-j-6b": ModelType.PT,
+    "EleutherAI/pythia-12b-deduped": ModelType.PT,
+    "EleutherAI/gpt-neo-1.3B": ModelType.PT,
+    "EleutherAI/pythia-410m-deduped": ModelType.PT,
+    "EleutherAI/pythia-160m-deduped": ModelType.PT,
+    "EleutherAI/polyglot-ko-12.8b": ModelType.PT,
+    "EleutherAI/pythia-12b": ModelType.PT,
+    "roneneldan/TinyStories-33M": ModelType.PT,
+    "roneneldan/TinyStories-28M": ModelType.PT,
+    "roneneldan/TinyStories-1M": ModelType.PT,
+    "roneneldan/TinyStories-8M": ModelType.PT,
+    "roneneldan/TinyStories-3M": ModelType.PT,
+    "jerryjalapeno/nart-100k-7b": ModelType.FT,
+    "lmsys/vicuna-13b-v1.3": ModelType.IFT,
+    "lmsys/vicuna-7b-v1.3": ModelType.IFT,
+    "lmsys/vicuna-13b-v1.1": ModelType.IFT,
+    "lmsys/vicuna-13b-delta-v1.1": ModelType.IFT,
+    "lmsys/vicuna-7b-delta-v1.1": ModelType.IFT,
+    "abhiramtirumala/DialoGPT-sarcastic-medium": ModelType.FT,
+    "haonan-li/bactrian-x-llama-13b-merged": ModelType.IFT,
+    "Gryphe/MythoLogic-13b": ModelType.IFT,
+    "Gryphe/MythoBoros-13b": ModelType.IFT,
+    "pillowtalks-ai/delta13b": ModelType.FT,
+    "wannaphong/openthaigpt-0.1.0-beta-full-model_for_open_llm_leaderboard": ModelType.FT,
+    "bigscience/bloom-7b1": ModelType.PT,
+    "bigcode/tiny_starcoder_py": ModelType.PT,
+    "bigcode/starcoderplus": ModelType.FT,
+    "bigcode/gpt_bigcode-santacoder": ModelType.PT,
+    "bigcode/starcoder": ModelType.PT,
+    "Open-Orca/OpenOrca-Preview1-13B": ModelType.IFT,
+    "microsoft/DialoGPT-large": ModelType.FT,
+    "microsoft/DialoGPT-small": ModelType.FT,
+    "microsoft/DialoGPT-medium": ModelType.FT,
+    "microsoft/CodeGPT-small-py": ModelType.FT,
+    "Tincando/fiction_story_generator": ModelType.FT,
+    "Pirr/pythia-13b-deduped-green_devil": ModelType.FT,
+    "Aeala/GPT4-x-AlpacaDente2-30b": ModelType.FT,
+    "Aeala/GPT4-x-AlpacaDente-30b": ModelType.FT,
+    "Aeala/GPT4-x-Alpasta-13b": ModelType.FT,
+    "Aeala/VicUnlocked-alpaca-30b": ModelType.IFT,
+    "Tap-M/Luna-AI-Llama2-Uncensored": ModelType.FT,
+    "illuin/test-custom-llama": ModelType.FT,
+    "dvruette/oasst-llama-13b-2-epochs": ModelType.FT,
+    "dvruette/oasst-gpt-neox-20b-1000-steps": ModelType.FT,
+    "dvruette/llama-13b-pretrained-dropout": ModelType.PT,
+    "dvruette/llama-13b-pretrained": ModelType.PT,
+    "dvruette/llama-13b-pretrained-sft-epoch-1": ModelType.FT,
+    "dvruette/llama-13b-pretrained-sft-do2": ModelType.FT,
+    "dvruette/oasst-gpt-neox-20b-3000-steps": ModelType.FT,
+    "dvruette/oasst-pythia-12b-pretrained-sft": ModelType.FT,
+    "dvruette/oasst-pythia-6.9b-4000-steps": ModelType.FT,
+    "dvruette/gpt-neox-20b-full-precision": ModelType.FT,
+    "dvruette/oasst-llama-13b-1000-steps": ModelType.FT,
+    "openlm-research/open_llama_7b_700bt_preview": ModelType.PT,
+    "openlm-research/open_llama_7b": ModelType.PT,
+    "openlm-research/open_llama_7b_v2": ModelType.PT,
+    "openlm-research/open_llama_3b": ModelType.PT,
+    "openlm-research/open_llama_13b": ModelType.PT,
+    "openlm-research/open_llama_3b_v2": ModelType.PT,
+    "PocketDoc/Dans-PileOfSets-Mk1-llama-13b-merged": ModelType.IFT,
+    "GeorgiaTechResearchInstitute/galpaca-30b": ModelType.IFT,
+    "GeorgiaTechResearchInstitute/starcoder-gpteacher-code-instruct": ModelType.IFT,
+    "databricks/dolly-v2-7b": ModelType.IFT,
+    "databricks/dolly-v2-3b": ModelType.IFT,
+    "databricks/dolly-v2-12b": ModelType.IFT,
+    "Rachneet/gpt2-xl-alpaca": ModelType.FT,
+    "Locutusque/gpt2-conversational-or-qa": ModelType.FT,
+    "psyche/kogpt": ModelType.FT,
+    "NbAiLab/nb-gpt-j-6B-alpaca": ModelType.IFT,
+    "Mikael110/llama-2-7b-guanaco-fp16": ModelType.FT,
+    "Mikael110/llama-2-13b-guanaco-fp16": ModelType.FT,
+    "Fredithefish/CrimsonPajama": ModelType.IFT,
+    "Fredithefish/RedPajama-INCITE-Chat-3B-ShareGPT-11K": ModelType.FT,
+    "Fredithefish/ScarletPajama-3B-HF": ModelType.FT,
+    "Fredithefish/RedPajama-INCITE-Chat-3B-Instruction-Tuning-with-GPT-4": ModelType.IFT,
+    "acrastt/RedPajama-INCITE-Chat-Instruct-3B-V1": ModelType.IFT,
+    "eachadea/vicuna-13b-1.1": ModelType.FT,
+    "eachadea/vicuna-7b-1.1": ModelType.FT,
+    "eachadea/vicuna-13b": ModelType.FT,
+    "openaccess-ai-collective/wizard-mega-13b": ModelType.IFT,
+    "openaccess-ai-collective/manticore-13b": ModelType.IFT,
+    "openaccess-ai-collective/manticore-30b-chat-pyg-alpha": ModelType.IFT,
+    "openaccess-ai-collective/minotaur-13b": ModelType.IFT,
+    "openaccess-ai-collective/minotaur-13b-fixed": ModelType.IFT,
+    "openaccess-ai-collective/hippogriff-30b-chat": ModelType.IFT,
+    "openaccess-ai-collective/manticore-13b-chat-pyg": ModelType.IFT,
+    "pythainlp/wangchanglm-7.5B-sft-enth": ModelType.IFT,
+    "pythainlp/wangchanglm-7.5B-sft-en-sharded": ModelType.IFT,
+    "euclaise/gpt-neox-122m-minipile-digits": ModelType.FT,
+    "stabilityai/StableBeluga1-Delta": ModelType.IFT,
+    "stabilityai/stablelm-tuned-alpha-7b": ModelType.IFT,
+    "stabilityai/StableBeluga2": ModelType.IFT,
+    "stabilityai/StableBeluga-13B": ModelType.IFT,
+    "stabilityai/StableBeluga-7B": ModelType.IFT,
+    "stabilityai/stablelm-base-alpha-7b": ModelType.PT,
+    "stabilityai/stablelm-base-alpha-3b": ModelType.PT,
+    "stabilityai/stablelm-tuned-alpha-3b": ModelType.IFT,
+    "alibidaran/medical_transcription_generator": ModelType.FT,
+    "CalderaAI/30B-Lazarus": ModelType.IFT,
+    "CalderaAI/13B-BlueMethod": ModelType.IFT,
+    "CalderaAI/13B-Ouroboros": ModelType.IFT,
+    "KoboldAI/OPT-13B-Erebus": ModelType.FT,
+    "KoboldAI/GPT-J-6B-Janeway": ModelType.FT,
+    "KoboldAI/GPT-J-6B-Shinen": ModelType.FT,
+    "KoboldAI/fairseq-dense-2.7B": ModelType.PT,
+    "KoboldAI/OPT-6B-nerys-v2": ModelType.FT,
+    "KoboldAI/GPT-NeoX-20B-Skein": ModelType.FT,
+    "KoboldAI/PPO_Pygway-6b-Mix": ModelType.FT,
+    "KoboldAI/fairseq-dense-6.7B": ModelType.PT,
+    "KoboldAI/fairseq-dense-125M": ModelType.PT,
+    "KoboldAI/OPT-13B-Nerybus-Mix": ModelType.FT,
+    "KoboldAI/OPT-2.7B-Erebus": ModelType.FT,
+    "KoboldAI/OPT-350M-Nerys-v2": ModelType.FT,
+    "KoboldAI/OPT-2.7B-Nerys-v2": ModelType.FT,
+    "KoboldAI/OPT-2.7B-Nerybus-Mix": ModelType.FT,
+    "KoboldAI/OPT-13B-Nerys-v2": ModelType.FT,
+    "KoboldAI/GPT-NeoX-20B-Erebus": ModelType.FT,
+    "KoboldAI/OPT-6.7B-Erebus": ModelType.FT,
+    "KoboldAI/fairseq-dense-355M": ModelType.PT,
+    "KoboldAI/OPT-6.7B-Nerybus-Mix": ModelType.FT,
+    "KoboldAI/GPT-J-6B-Adventure": ModelType.FT,
+    "KoboldAI/OPT-350M-Erebus": ModelType.FT,
+    "KoboldAI/GPT-J-6B-Skein": ModelType.FT,
+    "KoboldAI/OPT-30B-Erebus": ModelType.FT,
+    "klosax/pythia-160m-deduped-step92k-193bt": ModelType.PT,
+    "klosax/open_llama_3b_350bt_preview": ModelType.PT,
+    "klosax/openllama-3b-350bt": ModelType.PT,
+    "klosax/pythia-70m-deduped-step44k-92bt": ModelType.PT,
+    "klosax/open_llama_13b_600bt_preview": ModelType.PT,
+    "klosax/open_llama_7b_400bt_preview": ModelType.PT,
+    "kfkas/Llama-2-ko-7b-Chat": ModelType.IFT,
+    "WeOpenML/Alpaca-7B-v1": ModelType.IFT,
+    "WeOpenML/PandaLM-Alpaca-7B-v1": ModelType.IFT,
+    "TFLai/gpt2-turkish-uncased": ModelType.FT,
+    "ehartford/WizardLM-13B-Uncensored": ModelType.IFT,
+    "ehartford/dolphin-llama-13b": ModelType.IFT,
+    "ehartford/Wizard-Vicuna-30B-Uncensored": ModelType.FT,
+    "ehartford/WizardLM-30B-Uncensored": ModelType.IFT,
+    "ehartford/Wizard-Vicuna-13B-Uncensored": ModelType.FT,
+    "ehartford/WizardLM-7B-Uncensored": ModelType.IFT,
+    "ehartford/based-30b": ModelType.FT,
+    "ehartford/Wizard-Vicuna-7B-Uncensored": ModelType.FT,
+    "wahaha1987/llama_7b_sharegpt94k_fastchat": ModelType.FT,
+    "wahaha1987/llama_13b_sharegpt94k_fastchat": ModelType.FT,
+    "OpenAssistant/oasst-sft-1-pythia-12b": ModelType.FT,
+    "OpenAssistant/stablelm-7b-sft-v7-epoch-3": ModelType.IFT,
+    "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5": ModelType.FT,
+    "OpenAssistant/pythia-12b-sft-v8-2.5k-steps": ModelType.IFT,
+    "OpenAssistant/pythia-12b-sft-v8-7k-steps": ModelType.IFT,
+    "OpenAssistant/pythia-12b-pre-v8-12.5k-steps": ModelType.IFT,
+    "OpenAssistant/llama2-13b-orca-8k-3319": ModelType.IFT,
+    "junelee/wizard-vicuna-13b": ModelType.FT,
+    "BreadAi/gpt-YA-1-1_160M": ModelType.PT,
+    "BreadAi/MuseCan": ModelType.PT,
+    "BreadAi/MusePy-1-2": ModelType.PT,
+    "BreadAi/DiscordPy": ModelType.PT,
+    "BreadAi/PM_modelV2": ModelType.PT,
+    "BreadAi/gpt-Youtube": ModelType.PT,
+    "BreadAi/StoryPy": ModelType.FT,
+    "julianweng/Llama-2-7b-chat-orcah": ModelType.FT,
+    "AGI-inc/lora_moe_7b_baseline": ModelType.FT,
+    "AGI-inc/lora_moe_7b": ModelType.FT,
+    "togethercomputer/GPT-NeoXT-Chat-Base-20B": ModelType.IFT,
+    "togethercomputer/RedPajama-INCITE-Chat-7B-v0.1": ModelType.IFT,
+    "togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1": ModelType.IFT,
+    "togethercomputer/RedPajama-INCITE-7B-Base": ModelType.PT,
+    "togethercomputer/RedPajama-INCITE-7B-Instruct": ModelType.IFT,
+    "togethercomputer/RedPajama-INCITE-Base-3B-v1": ModelType.PT,
+    "togethercomputer/Pythia-Chat-Base-7B": ModelType.IFT,
+    "togethercomputer/RedPajama-INCITE-Base-7B-v0.1": ModelType.PT,
+    "togethercomputer/GPT-JT-6B-v1": ModelType.IFT,
+    "togethercomputer/GPT-JT-6B-v0": ModelType.IFT,
+    "togethercomputer/RedPajama-INCITE-Chat-3B-v1": ModelType.IFT,
+    "togethercomputer/RedPajama-INCITE-7B-Chat": ModelType.IFT,
+    "togethercomputer/RedPajama-INCITE-Instruct-3B-v1": ModelType.IFT,
+    "Writer/camel-5b-hf": ModelType.IFT,
+    "Writer/palmyra-base": ModelType.PT,
+    "MBZUAI/LaMini-GPT-1.5B": ModelType.IFT,
+    "MBZUAI/lamini-cerebras-111m": ModelType.IFT,
+    "MBZUAI/lamini-neo-1.3b": ModelType.IFT,
+    "MBZUAI/lamini-cerebras-1.3b": ModelType.IFT,
+    "MBZUAI/lamini-cerebras-256m": ModelType.IFT,
+    "MBZUAI/LaMini-GPT-124M": ModelType.IFT,
+    "MBZUAI/lamini-neo-125m": ModelType.IFT,
+    "TehVenom/DiffMerge-DollyGPT-Pygmalion": ModelType.FT,
+    "TehVenom/PPO_Shygmalion-6b": ModelType.FT,
+    "TehVenom/Dolly_Shygmalion-6b-Dev_V8P2": ModelType.FT,
+    "TehVenom/Pygmalion_AlpacaLora-7b": ModelType.FT,
+    "TehVenom/PPO_Pygway-V8p4_Dev-6b": ModelType.FT,
+    "TehVenom/Dolly_Malion-6b": ModelType.FT,
+    "TehVenom/PPO_Shygmalion-V8p4_Dev-6b": ModelType.FT,
+    "TehVenom/ChanMalion": ModelType.FT,
+    "TehVenom/GPT-J-Pyg_PPO-6B": ModelType.IFT,
+    "TehVenom/Pygmalion-13b-Merged": ModelType.FT,
+    "TehVenom/Metharme-13b-Merged": ModelType.IFT,
+    "TehVenom/Dolly_Shygmalion-6b": ModelType.FT,
+    "TehVenom/GPT-J-Pyg_PPO-6B-Dev-V8p4": ModelType.IFT,
+    "georgesung/llama2_7b_chat_uncensored": ModelType.FT,
+    "vicgalle/gpt2-alpaca": ModelType.IFT,
+    "vicgalle/alpaca-7b": ModelType.FT,
+    "vicgalle/gpt2-alpaca-gpt4": ModelType.IFT,
+    "facebook/opt-350m": ModelType.PT,
+    "facebook/opt-125m": ModelType.PT,
+    "facebook/xglm-4.5B": ModelType.PT,
+    "facebook/opt-2.7b": ModelType.PT,
+    "facebook/opt-6.7b": ModelType.PT,
+    "facebook/galactica-30b": ModelType.PT,
+    "facebook/opt-13b": ModelType.PT,
+    "facebook/opt-66b": ModelType.PT,
+    "facebook/xglm-7.5B": ModelType.PT,
+    "facebook/xglm-564M": ModelType.PT,
+    "facebook/opt-30b": ModelType.PT,
+    "golaxy/gogpt-7b": ModelType.FT,
+    "golaxy/gogpt2-7b": ModelType.FT,
+    "golaxy/gogpt-7b-bloom": ModelType.FT,
+    "golaxy/gogpt-3b-bloom": ModelType.FT,
+    "psmathur/orca_mini_v2_7b": ModelType.IFT,
+    "psmathur/orca_mini_7b": ModelType.IFT,
+    "psmathur/orca_mini_3b": ModelType.IFT,
+    "psmathur/orca_mini_v2_13b": ModelType.IFT,
+    "gpt2-xl": ModelType.PT,
+    "lxe/Cerebras-GPT-2.7B-Alpaca-SP": ModelType.FT,
+    "Monero/Manticore-13b-Chat-Pyg-Guanaco": ModelType.FT,
+    "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b": ModelType.IFT,
+    "Monero/WizardLM-13b-OpenAssistant-Uncensored": ModelType.IFT,
+    "Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b": ModelType.IFT,
+    "jzjiao/opt-1.3b-rlhf": ModelType.FT,
+    "HuggingFaceH4/starchat-beta": ModelType.IFT,
+    "KnutJaegersberg/gpt-2-xl-EvolInstruct": ModelType.IFT,
+    "KnutJaegersberg/megatron-GPT-2-345m-EvolInstruct": ModelType.IFT,
+    "KnutJaegersberg/galactica-orca-wizardlm-1.3b": ModelType.IFT,
+    "openchat/openchat_8192": ModelType.IFT,
+    "openchat/openchat_v2": ModelType.IFT,
+    "openchat/openchat_v2_w": ModelType.IFT,
+    "ausboss/llama-13b-supercot": ModelType.IFT,
+    "ausboss/llama-30b-supercot": ModelType.IFT,
+    "Neko-Institute-of-Science/metharme-7b": ModelType.IFT,
+    "Neko-Institute-of-Science/pygmalion-7b": ModelType.FT,
+    "SebastianSchramm/Cerebras-GPT-111M-instruction": ModelType.IFT,
+    "victor123/WizardLM-13B-1.0": ModelType.IFT,
+    "OpenBuddy/openbuddy-openllama-13b-v7-fp16": ModelType.FT,
+    "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16": ModelType.FT,
+    "OpenBuddyEA/openbuddy-llama-30b-v7.1-bf16": ModelType.FT,
+    "baichuan-inc/Baichuan-7B": ModelType.PT,
+    "tiiuae/falcon-40b-instruct": ModelType.IFT,
+    "tiiuae/falcon-40b": ModelType.PT,
+    "tiiuae/falcon-7b": ModelType.PT,
+    "YeungNLP/firefly-llama-13b": ModelType.FT,
+    "YeungNLP/firefly-llama-13b-v1.2": ModelType.FT,
+    "YeungNLP/firefly-llama2-13b": ModelType.FT,
+    "YeungNLP/firefly-ziya-13b": ModelType.FT,
+    "shaohang/Sparse0.5_OPT-1.3": ModelType.FT,
+    "xzuyn/Alpacino-SuperCOT-13B": ModelType.IFT,
+    "xzuyn/MedicWizard-7B": ModelType.FT,
+    "xDAN-AI/xDAN_13b_l2_lora": ModelType.FT,
+    "beomi/KoAlpaca-Polyglot-5.8B": ModelType.FT,
+    "beomi/llama-2-ko-7b": ModelType.IFT,
+    "Salesforce/codegen-6B-multi": ModelType.PT,
+    "Salesforce/codegen-16B-nl": ModelType.PT,
+    "Salesforce/codegen-6B-nl": ModelType.PT,
+    "ai-forever/rugpt3large_based_on_gpt2": ModelType.FT,
+    "gpt2-large": ModelType.PT,
+    "frank098/orca_mini_3b_juniper": ModelType.FT,
+    "frank098/WizardLM_13B_juniper": ModelType.FT,
+    "FPHam/Free_Sydney_13b_HF": ModelType.FT,
+    "huggingface/llama-13b": ModelType.PT,
+    "huggingface/llama-7b": ModelType.PT,
+    "huggingface/llama-65b": ModelType.PT,
+    "huggingface/llama-30b": ModelType.PT,
+    "Henk717/chronoboros-33B": ModelType.IFT,
+    "jondurbin/airoboros-13b-gpt4-1.4": ModelType.IFT,
+    "jondurbin/airoboros-7b": ModelType.IFT,
+    "jondurbin/airoboros-7b-gpt4": ModelType.IFT,
+    "jondurbin/airoboros-7b-gpt4-1.1": ModelType.IFT,
+    "jondurbin/airoboros-7b-gpt4-1.2": ModelType.IFT,
+    "jondurbin/airoboros-7b-gpt4-1.3": ModelType.IFT,
+    "jondurbin/airoboros-7b-gpt4-1.4": ModelType.IFT,
+    "jondurbin/airoboros-l2-7b-gpt4-1.4.1": ModelType.IFT,
+    "jondurbin/airoboros-l2-13b-gpt4-1.4.1": ModelType.IFT,
+    "jondurbin/airoboros-l2-70b-gpt4-1.4.1": ModelType.IFT,
+    "jondurbin/airoboros-13b": ModelType.IFT,
+    "jondurbin/airoboros-33b-gpt4-1.4": ModelType.IFT,
+    "jondurbin/airoboros-33b-gpt4-1.2": ModelType.IFT,
+    "jondurbin/airoboros-65b-gpt4-1.2": ModelType.IFT,
+    "ariellee/SuperPlatty-30B": ModelType.IFT,
+    "danielhanchen/open_llama_3b_600bt_preview": ModelType.FT,
+    "cerebras/Cerebras-GPT-256M": ModelType.PT,
+    "cerebras/Cerebras-GPT-1.3B": ModelType.PT,
+    "cerebras/Cerebras-GPT-13B": ModelType.PT,
+    "cerebras/Cerebras-GPT-2.7B": ModelType.PT,
+    "cerebras/Cerebras-GPT-111M": ModelType.PT,
+    "cerebras/Cerebras-GPT-6.7B": ModelType.PT,
+    "Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf": ModelType.RL,
+    "Yhyu13/llama-30B-hf-openassitant": ModelType.FT,
+    "NousResearch/Nous-Hermes-Llama2-13b": ModelType.IFT,
+    "NousResearch/Nous-Hermes-llama-2-7b": ModelType.IFT,
+    "NousResearch/Redmond-Puffin-13B": ModelType.IFT,
+    "NousResearch/Nous-Hermes-13b": ModelType.IFT,
+    "project-baize/baize-v2-7b": ModelType.IFT,
+    "project-baize/baize-v2-13b": ModelType.IFT,
+    "LLMs/WizardLM-13B-V1.0": ModelType.FT,
+    "LLMs/AlpacaGPT4-7B-elina": ModelType.FT,
+    "wenge-research/yayi-7b": ModelType.FT,
+    "wenge-research/yayi-7b-llama2": ModelType.FT,
+    "wenge-research/yayi-13b-llama2": ModelType.FT,
+    "yhyhy3/open_llama_7b_v2_med_instruct": ModelType.IFT,
+    "llama-anon/instruct-13b": ModelType.IFT,
+    "huggingtweets/jerma985": ModelType.FT,
+    "huggingtweets/gladosystem": ModelType.FT,
+    "huggingtweets/bladeecity-jerma985": ModelType.FT,
+    "huggyllama/llama-13b": ModelType.PT,
+    "huggyllama/llama-65b": ModelType.PT,
+    "FabbriSimo01/Facebook_opt_1.3b_Quantized": ModelType.PT,
+    "upstage/Llama-2-70b-instruct": ModelType.IFT,
+    "upstage/Llama-2-70b-instruct-1024": ModelType.IFT,
+    "upstage/llama-65b-instruct": ModelType.IFT,
+    "upstage/llama-30b-instruct-2048": ModelType.IFT,
+    "upstage/llama-30b-instruct": ModelType.IFT,
+    "WizardLM/WizardLM-13B-1.0": ModelType.IFT,
+    "WizardLM/WizardLM-13B-V1.1": ModelType.IFT,
+    "WizardLM/WizardLM-13B-V1.2": ModelType.IFT,
+    "WizardLM/WizardLM-30B-V1.0": ModelType.IFT,
+    "WizardLM/WizardCoder-15B-V1.0": ModelType.IFT,
+    "gpt2": ModelType.PT,
+    "keyfan/vicuna-chinese-replication-v1.1": ModelType.IFT,
+    "nthngdy/pythia-owt2-70m-100k": ModelType.FT,
+    "nthngdy/pythia-owt2-70m-50k": ModelType.FT,
+    "quantumaikr/KoreanLM-hf": ModelType.FT,
+    "quantumaikr/open_llama_7b_hf": ModelType.FT,
+    "quantumaikr/QuantumLM-70B-hf": ModelType.IFT,
+    "MayaPH/FinOPT-Lincoln": ModelType.FT,
+    "MayaPH/FinOPT-Franklin": ModelType.FT,
+    "MayaPH/GodziLLa-30B": ModelType.IFT,
+    "MayaPH/GodziLLa-30B-plus": ModelType.IFT,
+    "MayaPH/FinOPT-Washington": ModelType.FT,
+    "ogimgio/gpt-neo-125m-neurallinguisticpioneers": ModelType.FT,
+    "layoric/llama-2-13b-code-alpaca": ModelType.FT,
+    "CobraMamba/mamba-gpt-3b": ModelType.FT,
+    "CobraMamba/mamba-gpt-3b-v2": ModelType.FT,
+    "CobraMamba/mamba-gpt-3b-v3": ModelType.FT,
+    "timdettmers/guanaco-33b-merged": ModelType.FT,
+    "elinas/chronos-33b": ModelType.IFT,
+    "heegyu/RedTulu-Uncensored-3B-0719": ModelType.IFT,
+    "heegyu/WizardVicuna-Uncensored-3B-0719": ModelType.IFT,
+    "heegyu/WizardVicuna-3B-0719": ModelType.IFT,
+    "meta-llama/Llama-2-7b-chat-hf": ModelType.RL,
+    "meta-llama/Llama-2-7b-hf": ModelType.PT,
+    "meta-llama/Llama-2-13b-chat-hf": ModelType.RL,
+    "meta-llama/Llama-2-13b-hf": ModelType.PT,
+    "meta-llama/Llama-2-70b-chat-hf": ModelType.RL,
+    "meta-llama/Llama-2-70b-hf": ModelType.PT,
+    "xhyi/PT_GPTNEO350_ATG": ModelType.FT,
+    "h2oai/h2ogpt-gm-oasst1-en-1024-20b": ModelType.FT,
+    "h2oai/h2ogpt-gm-oasst1-en-1024-open-llama-7b-preview-400bt": ModelType.FT,
+    "h2oai/h2ogpt-oig-oasst1-512-6_9b": ModelType.IFT,
+    "h2oai/h2ogpt-oasst1-512-12b": ModelType.IFT,
+    "h2oai/h2ogpt-oig-oasst1-256-6_9b": ModelType.IFT,
+    "h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt": ModelType.FT,
+    "h2oai/h2ogpt-oasst1-512-20b": ModelType.IFT,
+    "h2oai/h2ogpt-gm-oasst1-en-2048-open-llama-7b-preview-300bt-v2": ModelType.FT,
+    "h2oai/h2ogpt-gm-oasst1-en-1024-12b": ModelType.FT,
+    "h2oai/h2ogpt-gm-oasst1-multilang-1024-20b": ModelType.FT,
+    "bofenghuang/vigogne-13b-instruct": ModelType.IFT,
+    "bofenghuang/vigogne-13b-chat": ModelType.FT,
+    "bofenghuang/vigogne-2-7b-instruct": ModelType.IFT,
+    "bofenghuang/vigogne-7b-instruct": ModelType.IFT,
+    "bofenghuang/vigogne-7b-chat": ModelType.FT,
+    "Vmware/open-llama-7b-v2-open-instruct": ModelType.IFT,
+    "VMware/open-llama-0.7T-7B-open-instruct-v1.1": ModelType.IFT,
+    "ewof/koishi-instruct-3b": ModelType.IFT,
+    "gywy/llama2-13b-chinese-v1": ModelType.FT,
+    "GOAT-AI/GOAT-7B-Community": ModelType.FT,
+    "psyche/kollama2-7b": ModelType.FT,
+    "TheTravellingEngineer/llama2-7b-hf-guanaco": ModelType.FT,
+    "beaugogh/pythia-1.4b-deduped-sharegpt": ModelType.FT,
+    "augtoma/qCammel-70-x": ModelType.IFT,
+    "Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_unload": ModelType.IFT,
+    "anhnv125/pygmalion-6b-roleplay": ModelType.FT,
+    "64bits/LexPodLM-13B": ModelType.FT,
+}
+
+
+def model_type_from_str(type):
+    if "fine-tuned" in type or "🔶" in type:
+        return ModelType.FT
+    if "pretrained" in type or "🟢" in type:
+        return ModelType.PT
+    if "RL-tuned" in type or "🟦" in type:
+        return ModelType.RL
+    if "instruction-tuned" in type or "⭕" in type:
+        return ModelType.IFT
+    return ModelType.Unknown

src/display_models/read_results.py

@@ -0,0 +1,152 @@
+import json
+import os
+from dataclasses import dataclass
+from typing import Dict, List, Tuple
+from distutils.util import strtobool
+
+import dateutil
+import numpy as np
+
+from src.display_models.utils import AutoEvalColumn, make_clickable_model
+
+# 현우 - ko_commongen_v2 : acc_norm인지 체크 필요함  
+METRICS = ["acc_norm", "acc_norm", "acc", "mc2", "acc_norm"]
+BENCHMARKS = ["ko_arc_challenge", "ko_hellaswag", "ko_mmlu", "ko_truthfulqa_mc", "ko_commongen_v2"] #, "ethicalverification"]
+BENCH_TO_NAME = {
+    "ko_arc_challenge": AutoEvalColumn.arc.name,
+    "ko_hellaswag": AutoEvalColumn.hellaswag.name,
+    "ko_mmlu": AutoEvalColumn.mmlu.name,
+    "ko_truthfulqa_mc": AutoEvalColumn.truthfulqa.name,
+    "ko_commongen_v2": AutoEvalColumn.commongen_v2.name,
+    # TODO: Uncomment when we have results for these
+    # "ethicalverification": AutoEvalColumn.ethicalverification.name,
+}
+IS_PUBLIC = bool(strtobool(os.environ.get("IS_PUBLIC", "True")))
+
+@dataclass
+class EvalResult:
+    eval_name: str
+    org: str
+    model: str
+    revision: str
+    results: dict
+    precision: str = ""
+    model_type: str = ""
+    weight_type: str = ""
+
+    def to_dict(self):
+        from src.load_from_hub import is_model_on_hub
+
+        if self.org is not None:
+            base_model = f"{self.org}/{self.model}"
+        else:
+            base_model = f"{self.model}"
+        data_dict = {}
+
+        data_dict["eval_name"] = self.eval_name  # not a column, just a save name
+        data_dict["weight_type"] = self.weight_type  # not a column, just a save name
+        data_dict[AutoEvalColumn.precision.name] = self.precision
+        data_dict[AutoEvalColumn.model_type.name] = self.model_type
+        data_dict[AutoEvalColumn.model.name] = make_clickable_model(base_model)
+        data_dict[AutoEvalColumn.dummy.name] = base_model
+        data_dict[AutoEvalColumn.revision.name] = self.revision
+        data_dict[AutoEvalColumn.average.name] = sum([v for k, v in self.results.items()]) / 5.0
+        data_dict[AutoEvalColumn.still_on_hub.name] = (
+            is_model_on_hub(base_model, self.revision)[0] or base_model == "baseline"
+        )
+
+        for benchmark in BENCHMARKS:
+            if benchmark not in self.results.keys():
+                self.results[benchmark] = None
+
+        for k, v in BENCH_TO_NAME.items():
+            data_dict[v] = self.results[k]
+
+        return data_dict
+
+
+def parse_eval_result(json_filepath: str) -> Tuple[str, list[dict]]:
+    with open(json_filepath) as fp:
+        data = json.load(fp)
+
+    try:
+        config = data["config"]
+    except KeyError:
+        config = data["config_general"]
+    model = config.get("model_name", None)
+    if model is None:
+        model = config.get("model_args", None)
+
+    model_sha = config.get("model_sha", "")
+    model_split = model.split("/", 1)
+
+    precision = config.get("model_dtype")
+
+    model = model_split[-1]
+
+    if len(model_split) == 1:
+        org = None
+        model = model_split[0]
+        result_key = f"{model}_{precision}"
+    else:
+        org = model_split[0]
+        model = model_split[1]
+        result_key = f"{org}_{model}_{precision}"
+
+    eval_results = []
+    for benchmark, metric in zip(BENCHMARKS, METRICS):
+        accs = np.array([v.get(metric, None) for k, v in data["results"].items() if benchmark in k])
+        if accs.size == 0 or any([acc is None for acc in accs]):
+            continue
+        mean_acc = np.mean(accs) * 100.0
+        eval_results.append(
+            EvalResult(
+                eval_name=result_key,
+                org=org,
+                model=model,
+                revision=model_sha,
+                results={benchmark: mean_acc},
+                precision=precision,  # todo model_type=, weight_type=
+            )
+        )
+
+    return result_key, eval_results
+
+
+def get_eval_results() -> List[EvalResult]:
+    json_filepaths = []
+
+    for root, dir, files in os.walk("eval-results" + ("-private" if not IS_PUBLIC else "")):
+        # We should only have json files in model results
+        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
+            continue
+
+        # Sort the files by date
+        # store results by precision maybe?
+        try:
+            files.sort(key=lambda x: dateutil.parser.parse(x.split("_", 1)[-1][:-5]))
+        except dateutil.parser._parser.ParserError:
+            files = [files[-1]]
+
+        # up_to_date = files[-1]
+        for file in files:
+            json_filepaths.append(os.path.join(root, file))
+
+    eval_results = {}
+    for json_filepath in json_filepaths:
+        result_key, results = parse_eval_result(json_filepath)
+        for eval_result in results:
+            if result_key in eval_results.keys():
+                eval_results[result_key].results.update(eval_result.results)
+            else:
+                eval_results[result_key] = eval_result
+
+    eval_results = [v for v in eval_results.values()]
+
+    return eval_results
+
+
+def get_eval_results_dicts() -> List[Dict]:
+    eval_results = get_eval_results()
+
+    return [e.to_dict() for e in eval_results]

src/display_models/utils.py

@@ -0,0 +1,149 @@
+import os
+from dataclasses import dataclass
+
+from huggingface_hub import HfApi
+
+API = HfApi()
+
+
+# These classes are for user facing column names, to avoid having to change them
+# all around the code when a modif is needed
+@dataclass
+class ColumnContent:
+    name: str
+    type: str
+    displayed_by_default: bool
+    hidden: bool = False
+
+
+def fields(raw_class):
+    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
+
+
+@dataclass(frozen=True)
+class AutoEvalColumn:  # Auto evals column
+    model_type_symbol = ColumnContent("T", "str", True)
+    model = ColumnContent("Model", "markdown", True)
+    average = ColumnContent("Average ⬆️", "number", True)
+    arc = ColumnContent("Ko-ARC", "number", True)
+    hellaswag = ColumnContent("Ko-HellaSwag", "number", True)
+    mmlu = ColumnContent("Ko-MMLU", "number", True)
+    truthfulqa = ColumnContent("Ko-TruthfulQA", "number", True)
+    commongen_v2 = ColumnContent("Ko-CommonGen V2", "number", True)
+    # TODO: Uncomment when we have results for these
+    # ethicalverification = ColumnContent("EthicalVerification", "number", True)
+    model_type = ColumnContent("Type", "str", False)
+    precision = ColumnContent("Precision", "str", False)  # , True)
+    license = ColumnContent("Hub License", "str", False)
+    params = ColumnContent("#Params (B)", "number", False)
+    likes = ColumnContent("Hub ❤️", "number", False)
+    still_on_hub = ColumnContent("Available on the hub", "bool", False)
+    revision = ColumnContent("Model sha", "str", False, False)
+    dummy = ColumnContent(
+        "model_name_for_query", "str", True
+    )  # dummy col to implement search bar (hidden by custom CSS)
+
+
+@dataclass(frozen=True)
+class EloEvalColumn:  # Elo evals column
+    model = ColumnContent("Model", "markdown", True)
+    gpt4 = ColumnContent("GPT-4 (all)", "number", True)
+    human_all = ColumnContent("Human (all)", "number", True)
+    human_instruct = ColumnContent("Human (instruct)", "number", True)
+    human_code_instruct = ColumnContent("Human (code-instruct)", "number", True)
+
+
+@dataclass(frozen=True)
+class EvalQueueColumn:  # Queue column
+    model = ColumnContent("model", "markdown", True)
+    revision = ColumnContent("revision", "str", True)
+    private = ColumnContent("private", "bool", True)
+    precision = ColumnContent("precision", "str", True)
+    weight_type = ColumnContent("weight_type", "str", "Original")
+    status = ColumnContent("status", "str", True)
+
+
+LLAMAS = [
+    "huggingface/llama-7b",
+    "huggingface/llama-13b",
+    "huggingface/llama-30b",
+    "huggingface/llama-65b",
+]
+
+
+KOALA_LINK = "https://huggingface.co/TheBloke/koala-13B-HF"
+VICUNA_LINK = "https://huggingface.co/lmsys/vicuna-13b-delta-v1.1"
+OASST_LINK = "https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
+DOLLY_LINK = "https://huggingface.co/databricks/dolly-v2-12b"
+MODEL_PAGE = "https://huggingface.co/models"
+LLAMA_LINK = "https://ai.facebook.com/blog/large-language-model-llama-meta-ai/"
+VICUNA_LINK = "https://huggingface.co/CarperAI/stable-vicuna-13b-delta"
+ALPACA_LINK = "https://crfm.stanford.edu/2023/03/13/alpaca.html"
+
+
+def model_hyperlink(link, model_name):
+    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+
+
+def make_clickable_model(model_name):
+    link = f"https://huggingface.co/{model_name}"
+
+    if model_name in LLAMAS:
+        link = LLAMA_LINK
+        model_name = model_name.split("/")[1]
+    elif model_name == "HuggingFaceH4/stable-vicuna-13b-2904":
+        link = VICUNA_LINK
+        model_name = "stable-vicuna-13b"
+    elif model_name == "HuggingFaceH4/llama-7b-ift-alpaca":
+        link = ALPACA_LINK
+        model_name = "alpaca-13b"
+    if model_name == "dolly-12b":
+        link = DOLLY_LINK
+    elif model_name == "vicuna-13b":
+        link = VICUNA_LINK
+    elif model_name == "koala-13b":
+        link = KOALA_LINK
+    elif model_name == "oasst-12b":
+        link = OASST_LINK
+
+    details_model_name = model_name.replace("/", "__")
+    # details_link = f"https://huggingface.co/datasets/open-ko-llm-leaderboard/details_{details_model_name}"
+
+    # if not bool(os.getenv("DEBUG", "False")):
+    #     # We only add these checks when not debugging, as they are extremely slow
+    #     print(f"details_link: {details_link}")
+    #     try:
+    #         check_path = list(
+    #             API.list_files_info(
+    #                 repo_id=f"open-ko-llm-leaderboard/details_{details_model_name}",
+    #                 paths="README.md",
+    #                 repo_type="dataset",
+    #             )
+    #         )
+    #         print(f"check_path: {check_path}")
+    #     except Exception as err:
+    #         # No details repo for this model
+    #         print(f"No details repo for this model: {err}")
+    #         return model_hyperlink(link, model_name)
+
+    return model_hyperlink(link, model_name) # + "  " + model_hyperlink(details_link, "📑")
+
+
+def styled_error(error):
+    return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
+
+
+def styled_warning(warn):
+    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
+
+
+def styled_message(message):
+    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
+
+
+def has_no_nan_values(df, columns):
+    return df[columns].notna().all(axis=1)
+
+
+def has_nan_values(df, columns):
+    return df[columns].isna().any(axis=1)

src/load_from_hub.py

@@ -0,0 +1,145 @@
+import json
+import os
+
+import pandas as pd
+from huggingface_hub import Repository
+from transformers import AutoConfig
+from collections import defaultdict
+
+from src.assets.hardcoded_evals import baseline
+from src.display_models.get_model_metadata import apply_metadata
+from src.display_models.read_results import get_eval_results_dicts, make_clickable_model
+from src.display_models.utils import AutoEvalColumn, EvalQueueColumn, has_no_nan_values
+
+
+def get_all_requested_models(requested_models_dir: str) -> set[str]:
+    depth = 1
+    file_names = []
+    users_to_submission_dates = defaultdict(list)
+
+    for root, _, files in os.walk(requested_models_dir):
+        current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
+        if current_depth == depth:
+            for file in files:
+                if not file.endswith(".json"): continue
+                with open(os.path.join(root, file), "r") as f:
+                    info = json.load(f)
+                    file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
+
+                    # Select organisation
+                    if info["model"].count("/") == 0 or "submitted_time" not in info:
+                        continue
+                    organisation, _ = info["model"].split("/")
+                    users_to_submission_dates[organisation].append(info["submitted_time"])
+
+    return set(file_names), users_to_submission_dates
+
+
+def load_all_info_from_hub(QUEUE_REPO: str, RESULTS_REPO: str, QUEUE_PATH: str, RESULTS_PATH: str) -> list[Repository]:
+    eval_queue_repo = None
+    eval_results_repo = None
+    requested_models = None
+
+    print("Pulling evaluation requests and results.")
+
+    eval_queue_repo = Repository(
+        local_dir=QUEUE_PATH,
+        clone_from=QUEUE_REPO,
+        repo_type="dataset",
+    )
+    eval_queue_repo.git_pull()
+
+    eval_results_repo = Repository(
+        local_dir=RESULTS_PATH,
+        clone_from=RESULTS_REPO,
+        repo_type="dataset",
+    )
+    eval_results_repo.git_pull()
+
+    requested_models, users_to_submission_dates = get_all_requested_models("eval-queue")
+
+    return eval_queue_repo, requested_models, eval_results_repo, users_to_submission_dates
+
+
+def get_leaderboard_df(
+    eval_results: Repository, eval_results_private: Repository, cols: list, benchmark_cols: list
+) -> pd.DataFrame:
+    if eval_results:
+        print("Pulling evaluation results for the leaderboard.")
+        eval_results.git_pull()
+    if eval_results_private:
+        print("Pulling evaluation results for the leaderboard.")
+        eval_results_private.git_pull()
+
+    all_data = get_eval_results_dicts()
+
+    # all_data.append(baseline)
+    apply_metadata(all_data)  # Populate model type based on known hardcoded values in `metadata.py`
+
+    df = pd.DataFrame.from_records(all_data)
+    df = df.sort_values(by=[AutoEvalColumn.average.name], ascending=False)
+    df = df[cols].round(decimals=2)
+
+    # filter out if any of the benchmarks have not been produced
+    df = df[has_no_nan_values(df, benchmark_cols)]
+    return df
+
+
+def get_evaluation_queue_df(
+    eval_queue: Repository, eval_queue_private: Repository, save_path: str, cols: list
+) -> list[pd.DataFrame]:
+    if eval_queue:
+        print("Pulling changes for the evaluation queue.")
+        eval_queue.git_pull()
+    if eval_queue_private:
+        print("Pulling changes for the evaluation queue.")
+        eval_queue_private.git_pull()
+
+    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
+    all_evals = []
+
+    for entry in entries:
+        if ".json" in entry:
+            file_path = os.path.join(save_path, entry)
+            with open(file_path) as fp:
+                data = json.load(fp)
+
+            data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
+            data[EvalQueueColumn.revision.name] = data.get("revision", "main")
+
+            all_evals.append(data)
+        elif ".md" not in entry:
+            # this is a folder
+            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if not e.startswith(".")]
+            for sub_entry in sub_entries:
+                file_path = os.path.join(save_path, entry, sub_entry)
+                with open(file_path) as fp:
+                    data = json.load(fp)
+
+                data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
+                data[EvalQueueColumn.revision.name] = data.get("revision", "main")
+                all_evals.append(data)
+
+    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
+    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
+    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
+    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
+    df_running = pd.DataFrame.from_records(running_list, columns=cols)
+    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
+    return df_finished[cols], df_running[cols], df_pending[cols]
+
+
+def is_model_on_hub(model_name: str, revision: str) -> bool:
+    try:
+        AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=False)
+        return True, None
+
+    except ValueError:
+        return (
+            False,
+            "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
+        )
+
+    except Exception as e:
+        print(f"Could not get the model config from the hub.: {e}")
+        return False, "was not found on hub!"

src/rate_limiting.py

@@ -0,0 +1,16 @@
+
+from datetime import datetime, timezone, timedelta
+
+
+def user_submission_permission(submission_name, users_to_submission_dates, rate_limit_period):
+    org_or_user, _ = submission_name.split("/")
+    if org_or_user not in users_to_submission_dates:
+        return 0
+    submission_dates = sorted(users_to_submission_dates[org_or_user])
+
+    time_limit = (datetime.now(timezone.utc) - timedelta(days=rate_limit_period)).strftime("%Y-%m-%dT%H:%M:%SZ")
+    submissions_after_timelimit = [d for d in submission_dates if d > time_limit]
+
+    return len(submissions_after_timelimit)
+
+