remove submit tab

app.py

@@ -1,4 +1,3 @@
-import subprocess
 import gradio as gr
 import pandas as pd
 import pandas.io.formats.style as style
@@ -6,7 +5,6 @@ from apscheduler.schedulers.background import BackgroundScheduler
 from huggingface_hub import snapshot_download
 
 from src.about import (
-    EVALUATION_QUEUE_TEXT,
     INTRODUCTION_TEXT,
     LLM_BENCHMARKS_TEXT,
     TITLE,
@@ -15,20 +13,15 @@ from src.display.css_html_js import custom_css
 from src.display.utils import (
     BENCHMARK_COLS,
     COLS,
-    EVAL_COLS,
-    EVAL_TYPES,
     NUMERIC_INTERVALS,
     TYPES,
     AutoEvalColumn,
     ModelType,
-    ModelAPI,
     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
+from src.populate import get_leaderboard_df
 
 
 def restart_space():
@@ -53,13 +46,6 @@ except Exception:
 raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
 leaderboard_df = original_df.copy()
 
-(
-    finished_eval_queue_df,
-    running_eval_queue_df,
-    pending_eval_queue_df,
-) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
-
-
 # Searching and filtering
 def update_table(
     hidden_df: pd.DataFrame,
@@ -251,101 +237,6 @@ with demo:
         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_api = gr.Dropdown(
-                        choices=[a.value.name for a in ModelAPI],
-                        label="Model API",
-                        multiselect=False,
-                        value="hf",
-                        interactive=True,
-                    )
-                    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_api,
-                    model_name_textbox,
-                    base_model_name_textbox,
-                    revision_name_textbox,
-                    precision,
-                    weight_type,
-                    model_type,
-                ],
-                submission_result,
-            )
-
 
 scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", hours=1)

src/about.py

@@ -20,7 +20,6 @@ class Tasks(Enum):
     task5 = Task("icelandic_belebele", "exact_match,get-answer", "Belebele (IS)")
     task6 = Task("icelandic_arc_challenge", "exact_match,get-answer", "ARC-Challenge-IS")
 
-NUM_FEWSHOT = 0 # Change with your few shot
 # ---------------------------------------------------
 
 
@@ -70,33 +69,3 @@ A machine-translated version of the ARC-Challenge multiple-choice question-answe
 
 """
 
-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).
-"""
-

src/display/utils.py

@@ -32,7 +32,6 @@ for task in Tasks:
 # 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)])
@@ -50,7 +49,6 @@ class EvalQueueColumn:  # Queue column
     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
@@ -89,18 +87,10 @@ class ModelType(Enum):
             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")
     float32 = ModelDetails("float32")
-    #qt_8bit = ModelDetails("8bit")
-    #qt_4bit = ModelDetails("4bit")
-    #qt_GPTQ = ModelDetails("GPTQ")
     Unknown = ModelDetails("?")
 
     def from_str(precision):
@@ -110,12 +100,6 @@ class Precision(Enum):
             return Precision.bfloat16
         if precision in ["float32"]:
             return Precision.float32
-        #if precision in ["8bit"]:
-        #    return Precision.qt_8bit
-        #if precision in ["4bit"]:
-        #    return Precision.qt_4bit
-        #if precision in ["GPTQ", "None"]:
-        #    return Precision.qt_GPTQ
         return Precision.Unknown
 
 # Column selection

src/leaderboard/read_evals.py

@@ -8,7 +8,7 @@ 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.display.utils import AutoEvalColumn, ModelType, Tasks, Precision
 from src.submission.check_validity import is_model_on_hub
 
 
@@ -24,7 +24,6 @@ class EvalResult:
     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
@@ -99,7 +98,6 @@ class EvalResult:
             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)
@@ -115,7 +113,6 @@ class EvalResult:
             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,

src/populate.py

@@ -1,10 +1,7 @@
-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.display.formatting import has_no_nan_values
+from src.display.utils import AutoEvalColumn
 from src.leaderboard.read_evals import get_raw_eval_results
 
 
@@ -20,39 +17,3 @@ def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchm
     # filter out if any of the benchmarks have not been produced
     df = df[has_no_nan_values(df, benchmark_cols)]
     return raw_data, 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 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 +1,36 @@
-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]:
+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)
+        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)
+                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,
-                    f"uses a tokenizer which is not in a transformers release: {e}",
-                    None
+                    "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?",
+                    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
+            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

@@ -1,128 +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_api: str,
-    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_api not in ["hf", "openai-chat-completions", "anthropic-chat-completions"]:
-        return styled_error('Please select a model API from one of "hf", "openai-chat-completions" or "anthropic-chat-completions"')
-
-    if model_type is None or model_type == "":
-        return styled_error("Please select a model type.")
-
-    if model_api in ["openai-chat-completions", "anthropic-chat-completions"]:
-        # Don't need to check for model details for these APIs
-        print("Adding new eval for OpenAI/Anthropic model")
-    else:
-        # 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 for HF model")
-
-    eval_entry = {
-        "model_api": model_api,
-        "model": model,
-        "base_model": base_model,
-        "revision": revision,
-        "precision": precision if model_api == "hf" else None,
-        "weight_type": weight_type,
-        "status": "PENDING",
-        "submitted_time": current_time,
-        "model_type": model_type,
-        "likes": model_info.likes if model_api == "hf" else None,
-        "params": model_size if model_api == "hf" else None,
-        "license": license if model_api == "hf" else None,
-        "private": False if model_api == "hf" else True,
-    }
-
-    # 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 = os.path.join(EVAL_REQUESTS_PATH, user_name)
-    os.makedirs(OUT_DIR, exist_ok=True)
-    out_path = os.path.join(OUT_DIR, f"{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."
-    )