upload_leaderboard

Makefile

@@ -1,13 +0,0 @@
-.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 .

README.md

@@ -1,14 +1,13 @@
 ---
-title: S Eval
+title: 🏆 S-Eval Leaderboard
 emoji: 🥇
 colorFrom: green
 colorTo: indigo
 sdk: gradio
+sdk_version: 5.25.2
 app_file: app.py
 pinned: true
-license: apache-2.0
-short_description: Duplicate this leaderboard to initialize your own!
-sdk_version: 5.19.0
+license: cc-by-nc-sa-4.0
 ---
 
 # Start the configuration
@@ -43,4 +42,4 @@ If you encounter problem on the space, don't hesitate to restart it to remove th
 You'll find 
 - the main table' columns names and properties in `src/display/utils.py`
 - the logic to read all results and request files, then convert them in dataframe lines, in `src/leaderboard/read_evals.py`, and `src/populate.py`
-- the logic to allow or filter submissions in `src/submission/submit.py` and `src/submission/check_validity.py`
+- teh logic to allow or filter submissions in `src/submission/submit.py` and `src/submission/check_validity.py`

app.py

@@ -1,204 +1,178 @@
 import gradio as gr
-from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
+
+__all__ = ["block", "make_clickable_model", "make_clickable_user", "get_submissions"]
+
+
+import numpy as np
 import pandas as pd
-from apscheduler.schedulers.background import BackgroundScheduler
-from huggingface_hub import snapshot_download
-
-from src.about import (
-    CITATION_BUTTON_LABEL,
-    CITATION_BUTTON_TEXT,
-    EVALUATION_QUEUE_TEXT,
-    INTRODUCTION_TEXT,
-    LLM_BENCHMARKS_TEXT,
-    TITLE,
-)
-from src.display.css_html_js import custom_css
-from src.display.utils import (
-    BENCHMARK_COLS,
-    COLS,
-    EVAL_COLS,
-    EVAL_TYPES,
-    AutoEvalColumn,
-    ModelType,
-    fields,
-    WeightType,
-    Precision
-)
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
-from src.populate import get_evaluation_queue_df, get_leaderboard_df
-from src.submission.submit import add_new_eval
-
-
-def restart_space():
-    API.restart_space(repo_id=REPO_ID)
-
-### Space initialisation
-try:
-    print(EVAL_REQUESTS_PATH)
-    snapshot_download(
-        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
-    )
-except Exception:
-    restart_space()
-try:
-    print(EVAL_RESULTS_PATH)
-    snapshot_download(
-        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
+
+from constants import *
+from src.auto_leaderboard.model_metadata_type import ModelType
+
+global data_component, filter_component, ref_dic
+
+
+def upload_file(files):
+    file_paths = [file.name for file in files]
+    return file_paths
+
+
+def read_xlsx_leaderboard():
+    df_dict = pd.read_excel(XLSX_DIR, sheet_name=None)  # get all sheet
+    return df_dict
+
+
+def get_specific_df(sheet_name):
+    df = read_xlsx_leaderboard()[sheet_name].sort_values(by="Overall", ascending=False)
+    return df
+
+def get_link_df(sheet_name):
+    df = read_xlsx_leaderboard()[sheet_name]
+    return df
+
+
+ref_df = get_link_df("main")
+
+ref_dic = {}
+for id, row in ref_df.iterrows():
+    
+    ref_dic[
+        str(row["Model"])
+    ] = f'<a href="{row["Link"]}" target="_blank"  style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{row["Model"]}</a>'
+
+def wrap_model(func):
+    def wrapper(*args, **kwargs):
+        df = func(*args, **kwargs)
+        df["Model"] = df["Model"].apply(lambda x: ref_dic[x])
+        # cols_to_round = df.select_dtypes(include=[np.number]).columns.tolist()
+        # cols_to_round = [col for col in cols_to_round if col != "Model"]
+        # df[cols_to_round] = df[cols_to_round].apply(lambda x: np.round(x, 2))
+
+        all_cols = df.columns.tolist()
+        non_numeric_cols = df.select_dtypes(exclude=[np.number]).columns.tolist()
+        cols_to_round = [col for col in all_cols if col not in non_numeric_cols and col != "Model"]
+        df[cols_to_round] = df[cols_to_round].apply(lambda x: np.round(x, 2))
+        return df
+
+    return wrapper
+
+
+@wrap_model
+def get_base_zh_df():
+    return get_specific_df("base-zh")
+
+
+@wrap_model
+def get_base_en_df():
+    return get_specific_df("base-en")
+
+
+@wrap_model
+def get_attack_zh_df():
+    return get_specific_df("attack-zh")
+
+
+@wrap_model
+def get_attack_en_df():
+    return get_specific_df("attack-en")
+
+
+def build_leaderboard(
+    TABLE_INTRODUCTION, TAX_COLUMNS, get_chinese_df, get_english_df
+):
+
+    gr.Markdown(TABLE_INTRODUCTION, elem_classes="markdown-text")
+    data_spilt_radio = gr.Radio(
+        choices=["Chinese", "English"],
+        value="Chinese",
+        label=SELECT_SET_INTRO,
     )
-except Exception:
-    restart_space()
-
-
-LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-
-(
-    finished_eval_queue_df,
-    running_eval_queue_df,
-    pending_eval_queue_df,
-) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
-
-def init_leaderboard(dataframe):
-    if dataframe is None or dataframe.empty:
-        raise ValueError("Leaderboard DataFrame is empty or None.")
-    return Leaderboard(
-        value=dataframe,
-        datatype=[c.type for c in fields(AutoEvalColumn)],
-        select_columns=SelectColumns(
-            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
-            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
-            label="Select Columns to Display:",
-        ),
-        search_columns=[AutoEvalColumn.model.name, AutoEvalColumn.license.name],
-        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
-        filter_columns=[
-            ColumnFilter(AutoEvalColumn.model_type.name, type="checkboxgroup", label="Model types"),
-            ColumnFilter(AutoEvalColumn.precision.name, type="checkboxgroup", label="Precision"),
-            ColumnFilter(
-                AutoEvalColumn.params.name,
-                type="slider",
-                min=0.01,
-                max=150,
-                label="Select the number of parameters (B)",
-            ),
-            ColumnFilter(
-                AutoEvalColumn.still_on_hub.name, type="boolean", label="Deleted/incomplete", default=True
-            ),
-        ],
-        bool_checkboxgroup_label="Hide models",
+
+    # 创建数据帧组件
+    data_component = gr.components.Dataframe(
+        value=get_chinese_df,
+        headers=OVERALL_INFO + TAX_COLUMNS,
+        type="pandas",
+        datatype=["markdown"] + ["number"] + ["number"] * len(TAX_COLUMNS),
         interactive=False,
+        visible=True,
+        wrap=True,
+        column_widths=[250] + [100] + [150] * len(TAX_COLUMNS),
+    )
+
+    def on_data_split_radio(seleted_split):
+        if "Chinese" in seleted_split:
+            updated_data = get_chinese_df()
+        if "English" in seleted_split:
+            updated_data = get_english_df()
+        current_columns = data_component.headers  # 获取的当前的column
+        current_datatype = data_component.datatype  # 获取当前的datatype
+        filter_component = gr.components.Dataframe(
+            value=updated_data,
+            headers=current_columns,
+            type="pandas",
+            datatype=current_datatype,
+            interactive=False,
+            visible=True,
+            wrap=True,
+            column_widths=[250] + [100] + [150] * (len(current_columns) - 2),
+        )
+        return filter_component
+
+    # 关联处理函数
+    data_spilt_radio.change(
+        fn=on_data_split_radio, inputs=data_spilt_radio, outputs=data_component
     )
 
 
-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):
-            leaderboard = init_leaderboard(LEADERBOARD_DF)
-
-        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,
-                                row_count=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,
-                                row_count=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,
-                                row_count=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 commit", placeholder="main")
-                    model_type = gr.Dropdown(
-                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
-                        label="Model type",
-                        multiselect=False,
-                        value=None,
-                        interactive=True,
-                    )
-
-                with gr.Column():
-                    precision = gr.Dropdown(
-                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
-                        label="Precision",
-                        multiselect=False,
-                        value="float16",
-                        interactive=True,
-                    )
-                    weight_type = gr.Dropdown(
-                        choices=[i.value.name for i in WeightType],
-                        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 Eval")
-            submission_result = gr.Markdown()
-            submit_button.click(
-                add_new_eval,
-                [
-                    model_name_textbox,
-                    base_model_name_textbox,
-                    revision_name_textbox,
-                    precision,
-                    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,
-                lines=20,
-                elem_id="citation-button",
-                show_copy_button=True,
-            )
-
-scheduler = BackgroundScheduler()
-scheduler.add_job(restart_space, "interval", seconds=1800)
-scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+def build_demo():
+    block = gr.Blocks()
+
+    with block:
+        gr.Markdown(LEADERBOARD_INTRODUCTION)
+
+        with gr.Tabs(elem_classes="tab-buttons") as tabs:
+            # first
+            with gr.TabItem(
+                "Base Risk Prompt Set Results",
+                elem_id="evalcrafter-benchmark-tab-table",
+                id=0,
+            ):
+                build_leaderboard(
+                    TABLE_INTRODUCTION_1,
+                    risk_topic_1_columns,
+                    get_base_zh_df,
+                    get_base_en_df
+                )
+            # second
+            with gr.TabItem(
+                "Attack Prompt Set Results",
+                elem_id="evalcrafter-benchmark-tab-table",
+                id=1,
+            ):
+                build_leaderboard(
+                    TABLE_INTRODUCTION_2,
+                    attack_columns,
+                    get_attack_zh_df,
+                    get_attack_en_df
+                )
+            # last table about
+            with gr.TabItem("📝 About", elem_id="evalcrafter-benchmark-tab-table", id=3):
+                gr.Markdown(LEADERBORAD_INFO, elem_classes="markdown-text")
+
+        with gr.Row():
+            with gr.Accordion("📙 Citation", open=True):
+                citation_button = gr.Textbox(
+                    value=CITATION_BUTTON_TEXT,
+                    label=CITATION_BUTTON_LABEL,
+                    lines=10,
+                    elem_id="citation-button",
+                    show_label=True,
+                    show_copy_button=True,
+                )
+
+    # block.launch(share=True)
+    block.launch()
+
+if __name__ == "__main__":
+    build_demo()

constants.py

@@ -0,0 +1,75 @@
+# constants
+OVERALL_INFO = ["Model", "Overall"]
+
+risk_topic_1_columns = [
+    "Crimes and Illegal Activities",
+    "Cybersecurity",
+    "Data Privacy",
+    "Ethics and Morality",
+    "Physical and Mental Health",
+    "Hate Speech",
+    "Extremism",
+    "Inappropriate Suggestions"
+]
+risk_topic_1_columns = [item.lower() for item in risk_topic_1_columns]
+
+attack_columns = [
+    "Adaptive Attack",
+    "Positive Induction",
+    "Reverse Induction",
+    "Code Injection",
+    "Instruction Jailbreak",
+    "Goal Hijacking",
+    "Instruction Encryption",
+    "DeepInception",
+    "In-Context Attack",
+    "Chain of Utterances",
+    "Compositional Instructions"
+]
+attack_columns = [item.lower() for item in attack_columns]
+
+XLSX_DIR = "./file//results.xlsx"
+
+LEADERBOARD_INTRODUCTION = """# 🏆 S-Eval Leaderboard 
+    ## 🔔 Updates
+    📣 [2025/03/30]: 🎉 Our paper has been accepted by ISSTA 2025. To meet evaluation needs under different budgets, we partition the benchmark into four scales: [Small](https://github.com/IS2Lab/S-Eval/tree/main/s_eval/small) (1,000 Base and 10,000 Attack in each language), [Medium](https://github.com/IS2Lab/S-Eval/tree/main/s_eval/medium) (3,000 Base and 30,000 Attack in each language), [Large](https://github.com/IS2Lab/S-Eval/tree/main/s_eval/large) (5,000 Base and 50,000 Attack in each language) and [Full](https://github.com/IS2Lab/S-Eval/tree/main/s_eval/full) (10,000 Base and 100,000 Attack in each language), comprehensively considering the balance and harmfulness of data.
+    
+    📣 [2024/10/25]: We release all 20,000 base risk prompts and 200,000 corresponding attack prompts ([Version-0.1.2](https://github.com/IS2Lab/S-Eval)). We also update [🏆 LeaderBoard v0.1.2](https://huggingface.co/spaces/IS2Lab/S-Eval_v0.1.2) with new evaluation results including GPT-4 and other models.  
+    🎉 S-Eval has achieved about **7,000** total views and about **2,000** total downloads across multiple platforms. 🎉
+
+    📣 [2024/06/17]: We further release 10,000 base risk prompts and 100,000 corresponding attack prompts ([Version-0.1.1](https://github.com/IS2Lab/S-Eval)). If you require automatic safety evaluations, please feel free to submit a request via [Issues](https://huggingface.co/spaces/IS2Lab/S-Eval/discussions) or contact us by [Email](mailto:[email protected]).
+
+    📣 [2024/05/31]: We release 20,000 corresponding attack prompts.
+
+    📣 [2024/05/23]: We publish our [paper](https://arxiv.org/abs/2405.14191) and first release 2,000 base risk prompts. You can download the benchmark from our [project](https://github.com/IS2Lab/S-Eval), the [HuggingFace Dataset](https://huggingface.co/datasets/IS2Lab/S-Eval).
+
+    ### ❗️ Note
+    Due to the limited machine resource, please refresh the page if a connection timeout error occurs.
+
+    You can get more detailed information from our [Project](https://github.com/IS2Lab/S-Eval) and [Paper](https://arxiv.org/abs/2405.14191).
+"""
+
+SELECT_SET_INTRO = (
+    "Select whether Chinese or English results should be shown."
+)
+
+TABLE_INTRODUCTION_1 = """In the table below, we summarize the safety scores  (%) of differnet models on Base Risk Prompt Set."""
+TABLE_INTRODUCTION_2 = """In the table below, we summarize the attack success rates (%) of the instruction attacks in Attack Prompt Set on different models"""
+
+
+LEADERBORAD_INFO = """
+   S-Eval is designed to be a new comprehensive, multi-dimensional and open-ended safety evaluation benchmark. So far, S-Eval has 220,000 evaluation prompts in total (and is still in active expansion), including 20,000 base risk prompts (10,000 in Chinese and 10,000 in English) and 200,000 *corresponding* attack prompts derived from 10 popular adversarial instruction attacks. These test prompts are generated based on a comprehensive and unified risk taxonomy, specifically designed to encompass all crucial dimensions of LLM safety evaluation and meant to accurately reflect the varied safety levels of LLMs across these risk dimensions. 
+More details on the construction of the test suite including model-based test generation, selection and the expert critique LLM can be found in our [paper](https://arxiv.org/abs/2405.14191). 
+"""
+
+
+CITATION_BUTTON_LABEL = "If our work is useful for your own, you can cite us with the following BibTex entry:"
+
+CITATION_BUTTON_TEXT = r"""
+@article{yuan2024seval,
+  title={S-Eval: Towards Automated and Comprehensive Safety Evaluation for Large Language Models},
+  author={Xiaohan Yuan and Jinfeng Li and Dongxia Wang and Yuefeng Chen and Xiaofeng Mao and Longtao Huang and Jialuo Chen and Hui Xue and Xiaoxia Liu and Wenhai Wang and Kui Ren and Jingyi Wang},
+  journal={arXiv preprint arXiv:2405.14191},
+  year={2024}
+}
+"""

pyproject.toml

@@ -1,13 +0,0 @@
-[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

@@ -1,16 +1,16 @@
 APScheduler
 black
+click
 datasets
 gradio
-gradio[oauth]
-gradio_leaderboard==0.0.13
 gradio_client
-huggingface-hub>=0.18.0
+huggingface-hub
 matplotlib
 numpy
+openpyxl
 pandas
+plotly
 python-dateutil
+requests
+sentencepiece
 tqdm
-transformers
-tokenizers>=0.15.0
-sentencepiece

src/about.py

@@ -1,72 +0,0 @@
-from dataclasses import dataclass
-from enum import Enum
-
-@dataclass
-class Task:
-    benchmark: str
-    metric: str
-    col_name: str
-
-
-# Select your tasks here
-# ---------------------------------------------------
-class Tasks(Enum):
-    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
-    task0 = Task("anli_r1", "acc", "ANLI")
-    task1 = Task("logiqa", "acc_norm", "LogiQA")
-
-NUM_FEWSHOT = 0 # Change with your few shot
-# ---------------------------------------------------
-
-
-
-# Your leaderboard name
-TITLE = """<h1 align="center" id="space-title">Demo leaderboard</h1>"""
-
-# What does your leaderboard evaluate?
-INTRODUCTION_TEXT = """
-Intro text
-"""
-
-# Which evaluations are you running? how can people reproduce what you have?
-LLM_BENCHMARKS_TEXT = f"""
-## How it works
-
-## Reproducibility
-To reproduce our results, here is the commands you can run:
-
-"""
-
-EVALUATION_QUEUE_TEXT = """
-## Some good practices before submitting a model
-
-### 1) 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.
-
-Note: make sure your model is public!
-Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
-
-### 2) 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`!
-
-### 3) Make sure your model has an open license!
-This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
-
-### 4) 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"
-CITATION_BUTTON_TEXT = r"""
-"""

src/auto_leaderboard/model_metadata_type.py

@@ -0,0 +1,26 @@
+from dataclasses import dataclass
+from enum import Enum
+
+
+@dataclass
+class ModelInfo:
+    name: str
+    symbol: str  # emoji
+
+
+model_type_symbols = {
+    "LLM": "🟢",
+    "ImageLLM": "🔶",
+    "VideoLLM": "⭕",
+    "Other": "🟦",
+}
+
+
+class ModelType(Enum):
+    PT = ModelInfo(name="LLM", symbol="🟢")
+    FT = ModelInfo(name="ImageLLM", symbol="🔶")
+    IFT = ModelInfo(name="VideoLLM", symbol="⭕")
+    RL = ModelInfo(name="Other", symbol="🟦")
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"

src/display/css_html_js.py

@@ -1,105 +0,0 @@
-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;
-}
-
-/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
-#leaderboard-table td:nth-child(2),
-#leaderboard-table th:nth-child(2) {
-    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/display/formatting.py

@@ -1,27 +0,0 @@
-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}"
-    return model_hyperlink(link, model_name)
-
-
-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/display/utils.py

@@ -1,110 +0,0 @@
-from dataclasses import dataclass, make_dataclass
-from enum import Enum
-
-import pandas as pd
-
-from src.about import Tasks
-
-def fields(raw_class):
-    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
-
-
-# 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
-    never_hidden: bool = False
-
-## Leaderboard columns
-auto_eval_column_dict = []
-# Init
-auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
-auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
-#Scores
-auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
-for task in Tasks:
-    auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
-# Model information
-auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
-auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
-auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
-auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
-auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
-auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
-auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
-auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
-auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
-
-# We use make dataclass to dynamically fill the scores from Tasks
-AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
-
-## For the queue columns in the submission tab
-@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)
-
-## All the model information that we might need
-@dataclass
-class ModelDetails:
-    name: str
-    display_name: str = ""
-    symbol: str = "" # emoji
-
-
-class ModelType(Enum):
-    PT = ModelDetails(name="pretrained", symbol="🟢")
-    FT = ModelDetails(name="fine-tuned", symbol="🔶")
-    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
-    RL = ModelDetails(name="RL-tuned", symbol="🟦")
-    Unknown = ModelDetails(name="", symbol="?")
-
-    def to_str(self, separator=" "):
-        return f"{self.value.symbol}{separator}{self.value.name}"
-
-    @staticmethod
-    def 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
-
-class WeightType(Enum):
-    Adapter = ModelDetails("Adapter")
-    Original = ModelDetails("Original")
-    Delta = ModelDetails("Delta")
-
-class Precision(Enum):
-    float16 = ModelDetails("float16")
-    bfloat16 = ModelDetails("bfloat16")
-    Unknown = ModelDetails("?")
-
-    def from_str(precision):
-        if precision in ["torch.float16", "float16"]:
-            return Precision.float16
-        if precision in ["torch.bfloat16", "bfloat16"]:
-            return Precision.bfloat16
-        return Precision.Unknown
-
-# Column selection
-COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
-
-EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
-EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
-
-BENCHMARK_COLS = [t.value.col_name for t in Tasks]
-

src/envs.py

@@ -1,25 +0,0 @@
-import os
-
-from huggingface_hub import HfApi
-
-# Info to change for your repository
-# ----------------------------------
-TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org
-
-OWNER = "demo-leaderboard-backend" # Change to your org - don't forget to create a results and request dataset, with the correct format!
-# ----------------------------------
-
-REPO_ID = f"{OWNER}/leaderboard"
-QUEUE_REPO = f"{OWNER}/requests"
-RESULTS_REPO = f"{OWNER}/results"
-
-# If you setup a cache later, just change HF_HOME
-CACHE_PATH=os.getenv("HF_HOME", ".")
-
-# Local caches
-EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
-EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
-EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
-EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
-
-API = HfApi(token=TOKEN)

src/leaderboard/read_evals.py

@@ -1,196 +0,0 @@
-import glob
-import json
-import math
-import os
-from dataclasses import dataclass
-
-import dateutil
-import numpy as np
-
-from src.display.formatting import make_clickable_model
-from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType
-from src.submission.check_validity import is_model_on_hub
-
-
-@dataclass
-class EvalResult:
-    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
-    """
-    eval_name: str # org_model_precision (uid)
-    full_model: str # org/model (path on hub)
-    org: str 
-    model: str
-    revision: str # commit hash, "" if main
-    results: dict
-    precision: Precision = Precision.Unknown
-    model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
-    weight_type: WeightType = WeightType.Original # Original or Adapter
-    architecture: str = "Unknown" 
-    license: str = "?"
-    likes: int = 0
-    num_params: int = 0
-    date: str = "" # submission date of request file
-    still_on_hub: bool = False
-
-    @classmethod
-    def init_from_json_file(self, json_filepath):
-        """Inits the result from the specific model result file"""
-        with open(json_filepath) as fp:
-            data = json.load(fp)
-
-        config = data.get("config")
-
-        # Precision
-        precision = Precision.from_str(config.get("model_dtype"))
-
-        # Get model and org
-        org_and_model = config.get("model_name", config.get("model_args", None))
-        org_and_model = org_and_model.split("/", 1)
-
-        if len(org_and_model) == 1:
-            org = None
-            model = org_and_model[0]
-            result_key = f"{model}_{precision.value.name}"
-        else:
-            org = org_and_model[0]
-            model = org_and_model[1]
-            result_key = f"{org}_{model}_{precision.value.name}"
-        full_model = "/".join(org_and_model)
-
-        still_on_hub, _, model_config = is_model_on_hub(
-            full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
-        )
-        architecture = "?"
-        if model_config is not None:
-            architectures = getattr(model_config, "architectures", None)
-            if architectures:
-                architecture = ";".join(architectures)
-
-        # Extract results available in this file (some results are split in several files)
-        results = {}
-        for task in Tasks:
-            task = task.value
-
-            # We average all scores of a given metric (not all metrics are present in all files)
-            accs = np.array([v.get(task.metric, None) for k, v in data["results"].items() if task.benchmark == k])
-            if accs.size == 0 or any([acc is None for acc in accs]):
-                continue
-
-            mean_acc = np.mean(accs) * 100.0
-            results[task.benchmark] = mean_acc
-
-        return self(
-            eval_name=result_key,
-            full_model=full_model,
-            org=org,
-            model=model,
-            results=results,
-            precision=precision,  
-            revision= config.get("model_sha", ""),
-            still_on_hub=still_on_hub,
-            architecture=architecture
-        )
-
-    def update_with_request_file(self, requests_path):
-        """Finds the relevant request file for the current model and updates info with it"""
-        request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
-
-        try:
-            with open(request_file, "r") as f:
-                request = json.load(f)
-            self.model_type = ModelType.from_str(request.get("model_type", ""))
-            self.weight_type = WeightType[request.get("weight_type", "Original")]
-            self.license = request.get("license", "?")
-            self.likes = request.get("likes", 0)
-            self.num_params = request.get("params", 0)
-            self.date = request.get("submitted_time", "")
-        except Exception:
-            print(f"Could not find request file for {self.org}/{self.model} with precision {self.precision.value.name}")
-
-    def to_dict(self):
-        """Converts the Eval Result to a dict compatible with our dataframe display"""
-        average = sum([v for v in self.results.values() if v is not None]) / len(Tasks)
-        data_dict = {
-            "eval_name": self.eval_name,  # not a column, just a save name,
-            AutoEvalColumn.precision.name: self.precision.value.name,
-            AutoEvalColumn.model_type.name: self.model_type.value.name,
-            AutoEvalColumn.model_type_symbol.name: self.model_type.value.symbol,
-            AutoEvalColumn.weight_type.name: self.weight_type.value.name,
-            AutoEvalColumn.architecture.name: self.architecture,
-            AutoEvalColumn.model.name: make_clickable_model(self.full_model),
-            AutoEvalColumn.revision.name: self.revision,
-            AutoEvalColumn.average.name: average,
-            AutoEvalColumn.license.name: self.license,
-            AutoEvalColumn.likes.name: self.likes,
-            AutoEvalColumn.params.name: self.num_params,
-            AutoEvalColumn.still_on_hub.name: self.still_on_hub,
-        }
-
-        for task in Tasks:
-            data_dict[task.value.col_name] = self.results[task.value.benchmark]
-
-        return data_dict
-
-
-def get_request_file_for_model(requests_path, model_name, precision):
-    """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
-    request_files = os.path.join(
-        requests_path,
-        f"{model_name}_eval_request_*.json",
-    )
-    request_files = glob.glob(request_files)
-
-    # Select correct request file (precision)
-    request_file = ""
-    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"] in ["FINISHED"]
-                and req_content["precision"] == precision.split(".")[-1]
-            ):
-                request_file = tmp_request_file
-    return request_file
-
-
-def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
-    """From the path of the results folder root, extract all needed info for results"""
-    model_result_filepaths = []
-
-    for root, _, files in os.walk(results_path):
-        # 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
-        try:
-            files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
-        except dateutil.parser._parser.ParserError:
-            files = [files[-1]]
-
-        for file in files:
-            model_result_filepaths.append(os.path.join(root, file))
-
-    eval_results = {}
-    for model_result_filepath in model_result_filepaths:
-        # Creation of result
-        eval_result = EvalResult.init_from_json_file(model_result_filepath)
-        eval_result.update_with_request_file(requests_path)
-
-        # Store results of same eval together
-        eval_name = eval_result.eval_name
-        if eval_name in eval_results.keys():
-            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
-        else:
-            eval_results[eval_name] = eval_result
-
-    results = []
-    for v in eval_results.values():
-        try:
-            v.to_dict() # we test if the dict version is complete
-            results.append(v)
-        except KeyError:  # not all eval values present
-            continue
-
-    return results

src/populate.py

@@ -1,58 +0,0 @@
-import json
-import os
-
-import pandas as pd
-
-from src.display.formatting import has_no_nan_values, make_clickable_model
-from src.display.utils import AutoEvalColumn, EvalQueueColumn
-from src.leaderboard.read_evals import get_raw_eval_results
-
-
-def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
-    """Creates a dataframe from all the individual experiment results"""
-    raw_data = get_raw_eval_results(results_path, requests_path)
-    all_data_json = [v.to_dict() for v in raw_data]
-
-    df = pd.DataFrame.from_records(all_data_json)
-    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(save_path: str, cols: list) -> list[pd.DataFrame]:
-    """Creates the different dataframes for the evaluation queues requestes"""
-    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 os.path.isfile(e) and 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]

src/submission/check_validity.py

@@ -1,99 +0,0 @@
-import json
-import os
-import re
-from collections import defaultdict
-from datetime import datetime, timedelta, timezone
-
-import huggingface_hub
-from huggingface_hub import ModelCard
-from huggingface_hub.hf_api import ModelInfo
-from transformers import AutoConfig
-from transformers.models.auto.tokenization_auto import AutoTokenizer
-
-def check_model_card(repo_id: str) -> tuple[bool, str]:
-    """Checks if the model card and license exist and have been filled"""
-    try:
-        card = ModelCard.load(repo_id)
-    except huggingface_hub.utils.EntryNotFoundError:
-        return False, "Please add a model card to your model to explain how you trained/fine-tuned it."
-
-    # Enforce license metadata
-    if card.data.license is None:
-        if not ("license_name" in card.data and "license_link" in card.data):
-            return False, (
-                "License not found. Please add a license to your model card using the `license` metadata or a"
-                " `license_name`/`license_link` pair."
-            )
-
-    # Enforce card content
-    if len(card.text) < 200:
-        return False, "Please add a description to your model card, it is too short."
-
-    return True, ""
-
-def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]:
-    """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
-    try:
-        config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
-        if test_tokenizer:
-            try:
-                tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
-            except ValueError as e:
-                return (
-                    False,
-                    f"uses a tokenizer which is not in a transformers release: {e}",
-                    None
-                )
-            except Exception as e:
-                return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
-        return True, None, config
-
-    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.",
-            None
-        )
-
-    except Exception as e:
-        return False, "was not found on hub!", None
-
-
-def get_model_size(model_info: ModelInfo, precision: str):
-    """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
-    try:
-        model_size = round(model_info.safetensors["total"] / 1e9, 3)
-    except (AttributeError, TypeError):
-        return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
-
-    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
-    model_size = size_factor * model_size
-    return model_size
-
-def get_model_arch(model_info: ModelInfo):
-    """Gets the model architecture from the configuration"""
-    return model_info.config.get("architectures", "Unknown")
-
-def already_submitted_models(requested_models_dir: str) -> set[str]:
-    """Gather a list of already submitted models to avoid duplicates"""
-    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

src/submission/submit.py

@@ -1,119 +0,0 @@
-import json
-import os
-from datetime import datetime, timezone
-
-from src.display.formatting import styled_error, styled_message, styled_warning
-from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO
-from src.submission.check_validity import (
-    already_submitted_models,
-    check_model_card,
-    get_model_size,
-    is_model_on_hub,
-)
-
-REQUESTED_MODELS = None
-USERS_TO_SUBMISSION_DATES = None
-
-def add_new_eval(
-    model: str,
-    base_model: str,
-    revision: str,
-    precision: str,
-    weight_type: str,
-    model_type: str,
-):
-    global REQUESTED_MODELS
-    global USERS_TO_SUBMISSION_DATES
-    if not REQUESTED_MODELS:
-        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
-
-    user_name = ""
-    model_path = model
-    if "/" in model:
-        user_name = model.split("/")[0]
-        model_path = model.split("/")[1]
-
-    precision = precision.split(" ")[0]
-    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-
-    if model_type is None or model_type == "":
-        return styled_error("Please select a model type.")
-
-    # Does the model actually exist?
-    if revision == "":
-        revision = "main"
-
-    # Is the model on the hub?
-    if weight_type in ["Delta", "Adapter"]:
-        base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True)
-        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_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
-        if not model_on_hub:
-            return styled_error(f'Model "{model}" {error}')
-
-    # Is the model info correctly filled?
-    try:
-        model_info = API.model_info(repo_id=model, revision=revision)
-    except Exception:
-        return styled_error("Could not get your model information. Please fill it up properly.")
-
-    model_size = get_model_size(model_info=model_info, precision=precision)
-
-    # Were the model card and license filled?
-    try:
-        license = model_info.cardData["license"]
-    except Exception:
-        return styled_error("Please select a license for your model")
-
-    modelcard_OK, error_msg = check_model_card(model)
-    if not modelcard_OK:
-        return styled_error(error_msg)
-
-    # Seems good, creating the eval
-    print("Adding new eval")
-
-    eval_entry = {
-        "model": model,
-        "base_model": base_model,
-        "revision": revision,
-        "precision": precision,
-        "weight_type": weight_type,
-        "status": "PENDING",
-        "submitted_time": current_time,
-        "model_type": model_type,
-        "likes": model_info.likes,
-        "params": model_size,
-        "license": license,
-        "private": False,
-    }
-
-    # Check for duplicate submission
-    if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
-        return styled_warning("This model has been already submitted.")
-
-    print("Creating eval file")
-    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
-    os.makedirs(OUT_DIR, exist_ok=True)
-    out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"
-
-    with open(out_path, "w") as f:
-        f.write(json.dumps(eval_entry))
-
-    print("Uploading eval file")
-    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."
-    )

src/utils_display.py

@@ -0,0 +1,143 @@
+# from dataclasses import dataclass
+
+
+# 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
+#     never_hidden: bool = False
+#     dummy: 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, never_hidden=True)
+#     average = ColumnContent("Average ⬆️", "number", True)
+#     arc = ColumnContent("ARC", "number", True)
+#     hellaswag = ColumnContent("HellaSwag", "number", True)
+#     mmlu = ColumnContent("MMLU", "number", True)
+#     truthfulqa = ColumnContent("TruthfulQA", "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)
+#     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", "bool", 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
+    # else:
+    #    link = MODEL_PAGE
+
+#     return model_hyperlink(link, model_name)
+
+
+# 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>"
+#     )
+
+Qwen_1_8B_Chat_Link = "https://huggingface.co/Qwen/Qwen-1_8B-Chat"
+Qwen_7B_Chat_Link = "https://huggingface.co/Qwen/Qwen-7B-Chat"
+Qwen_14B_Chat_Link = "https://huggingface.co/Qwen/Qwen-14B-Chat"
+Qwen_72B_Chat_Link = "https://huggingface.co/Qwen/Qwen-72B-Chat"
+Gemma_2B_it_Link = "https://huggingface.co/google/gemma-2b-it"
+Gemma_7B_it__Link = "https://huggingface.co/google/gemma-7b-it"
+ChatGLM3_6B_Link = "https://huggingface.co/THUDM/chatglm3-6b"
+Mistral_7B_Instruct_v0_2_Link = "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2"
+LLaMA_2_7B_Chat_Link = "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"
+LLaMA_2_13B_Chat_Link = "https://huggingface.co/meta-llama/Llama-2-13b-chat-hf"
+LLaMA_2_70B_Chat_Link = "https://huggingface.co/meta-llama/Llama-2-70b-chat-hf"
+LLaMA_3_8B_Instruct_Link = "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"
+LLaMA_3_70B_Instruct_Link = "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct"
+Vicuna_7B_v1_3_Link = "https://huggingface.co/lmsys/vicuna-7b-v1.3"
+Vicuna_13B_v1_3_Link = "https://huggingface.co/lmsys/vicuna-13b-v1.3"
+Vicuna_33B_v1_3_Link = "https://huggingface.co/lmsys/vicuna-33b-v1.3"
+Baichuan2_13B_Chat_Link = "https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat"
+Yi_34B_Chat_Link = "https://huggingface.co/01-ai/Yi-34B-Chat"
+GPT_4_Turbo_Link = "https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4"
+ErnieBot_4_0_Link = "https://cloud.baidu.com/doc/WENXINWORKSHOP/s/clntwmv7t"
+Gemini_1_0_Pro_Link = "https://ai.google.dev/gemini-api/docs/models/gemini"