data_only_hallucination_leaderboard

Runtime error

App Files Files Community

pminervini commited on Jan 9

Commit

7dfe065

•

1 Parent(s): af0651e

update

Browse files

Files changed (6) hide show

app.py +21 -54
backend-cli.py +1 -11
src/tools/collections.py +0 -83
src/tools/model_backlinks.py +0 -1309
src/tools/plots.py +0 -133
src/utils.py +12 -0

app.py CHANGED Viewed

@@ -1,5 +1,8 @@
 import gradio as gr
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
 from huggingface_hub import snapshot_download
@@ -13,6 +16,7 @@ from src.display.about import (
     TITLE,
 )
 from src.display.css_html_js import custom_css
 from src.display.utils import (
     BENCHMARK_COLS,
     COLS,
@@ -26,44 +30,36 @@ from src.display.utils import (
     WeightType,
     Precision
 )
 from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
 from src.populate import get_evaluation_queue_df, get_leaderboard_df
 from src.submission.submit import add_new_eval
-# from src.submission.check_validity import already_submitted_models
-# from src.tools.collections import update_collections
-# from src.tools.plots import create_metric_plot_obj, create_plot_df, create_scores_df
 def restart_space():
     API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
-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)
-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)
-except Exception:
-    restart_space()
 raw_data, original_df = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
-# update_collections(original_df.copy())
 leaderboard_df = original_df.copy()
-# plot_df = create_plot_df(create_scores_df(raw_data))
 finished_eval_queue_df, running_eval_queue_df, pending_eval_queue_df = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
-# deleted_models_visibility = True
 # Searching and filtering
-def update_table(hidden_df: pd.DataFrame, columns: list, type_query: list, precision_query: str, size_query: list, query: str):
-    # show_deleted: bool,
     show_deleted = True
     filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
     filtered_df = filter_queries(query, filtered_df)
@@ -107,14 +103,12 @@ def filter_queries(query: str, filtered_df: pd.DataFrame):
     return filtered_df
-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.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
@@ -134,15 +128,11 @@ with demo:
     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 (separate multiple queries with `;`) and press ENTER...",
-                            show_label=False,
-                            elem_id="search-bar",
-                        )
                     with gr.Row():
                         shown_columns = gr.CheckboxGroup(
                             choices=[
@@ -159,12 +149,8 @@ with demo:
                             elem_id="column-select",
                             interactive=True,
                         )
-                    # with gr.Row():
-                    #     deleted_models_visibility = gr.Checkbox(
-                    #        value=False, 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=[t.to_str() for t in ModelType],
@@ -216,12 +202,11 @@ with demo:
                     filter_columns_type,
                     filter_columns_precision,
                     filter_columns_size,
-                    # deleted_models_visibility,
                     search_bar,
                 ],
                 leaderboard_table,
             )
-            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size]: #, deleted_models_visibility]:
                 selector.change(
                     update_table,
                     [
@@ -230,30 +215,12 @@ with demo:
                         filter_columns_type,
                         filter_columns_precision,
                         filter_columns_size,
-                        # deleted_models_visibility,
                         search_bar,
                     ],
                     leaderboard_table,
                     queue=True,
                 )
-        # with gr.TabItem("📈 Metrics through time", elem_id="llm-benchmark-tab-table", id=4):
-        #     with gr.Row():
-        #         with gr.Column():
-        #             chart = create_metric_plot_obj(
-        #                 plot_df,
-        #                 [AutoEvalColumn.average.name],
-        #                 title="Average of Top Scores and Human Baseline Over Time (from last update)",
-        #             )
-        #             gr.Plot(value=chart, min_width=500)
-        #         with gr.Column():
-        #             chart = create_metric_plot_obj(
-        #                 plot_df,
-        #                 BENCHMARK_COLS,
-        #                 title="Top Scores and Human Baseline Over Time (from last update)",
-        #             )
-        #             gr.Plot(value=chart, min_width=500)
         with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
             gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")

+#!/usr/bin/env python
 import gradio as gr
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
 from huggingface_hub import snapshot_download
     TITLE,
 )
 from src.display.css_html_js import custom_css
 from src.display.utils import (
     BENCHMARK_COLS,
     COLS,
     WeightType,
     Precision
 )
 from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
 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, token=H4_TOKEN)
+def ui_snapshot_download(repo_id, local_dir, repo_type, tqdm_class, etag_timeout):
+    try:
+        print(local_dir)
+        snapshot_download(repo_id=repo_id, local_dir=local_dir, repo_type=repo_type, tqdm_class=tqdm_class, etag_timeout=etag_timeout)
+    except Exception:
+        restart_space()
+ui_snapshot_download(repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30)
+ui_snapshot_download(repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30)
 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, columns: list, type_query: list, precision_query: list, size_query: list, query: str):
     show_deleted = True
     filtered_df = filter_models(hidden_df, type_query, size_query, precision_query, show_deleted)
     filtered_df = filter_queries(query, filtered_df)
     return filtered_df
+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] is True]
     type_emoji = [t[0] for t in type_query]
     filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
+        with gr.TabItem("Hallucination Benchmark", elem_id="llm-benchmark-tab-table", id=0):
             with gr.Row():
                 with gr.Column():
                     with gr.Row():
+                        search_bar = gr.Textbox(placeholder=" 🔍 Model search (separate multiple queries with `;`)", show_label=False, elem_id="search-bar",)
                     with gr.Row():
                         shown_columns = gr.CheckboxGroup(
                             choices=[
                             elem_id="column-select",
                             interactive=True,
                         )
                 with gr.Column(min_width=320):
                     filter_columns_type = gr.CheckboxGroup(
                         label="Model types",
                         choices=[t.to_str() for t in ModelType],
                     filter_columns_type,
                     filter_columns_precision,
                     filter_columns_size,
                     search_bar,
                 ],
                 leaderboard_table,
             )
+            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size]:
                 selector.change(
                     update_table,
                     [
                         filter_columns_type,
                         filter_columns_precision,
                         filter_columns_size,
                         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")
             gr.Markdown(FAQ_TEXT, elem_classes="markdown-text")

backend-cli.py CHANGED Viewed

@@ -17,6 +17,7 @@ from src.backend.manage_requests import EvalRequest
 from src.leaderboard.read_evals import EvalResult
 from src.envs import QUEUE_REPO, RESULTS_REPO, API
 import logging
 import pprint
@@ -34,17 +35,6 @@ FAILED_STATUS = "FAILED"
 TASKS_HARNESS = [task.value for task in Tasks]
-def my_snapshot_download(repo_id, revision, local_dir, repo_type, max_workers):
-    for i in range(10):
-        try:
-            snapshot_download(repo_id=repo_id, revision=revision, local_dir=local_dir, repo_type=repo_type, max_workers=max_workers)
-            return
-        except Exception:
-            import time
-            time.sleep(60)
-    return
 my_snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
 my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)

 from src.leaderboard.read_evals import EvalResult
 from src.envs import QUEUE_REPO, RESULTS_REPO, API
+from src.utils import my_snapshot_download
 import logging
 import pprint
 TASKS_HARNESS = [task.value for task in Tasks]
 my_snapshot_download(repo_id=RESULTS_REPO, revision="main", local_dir=EVAL_RESULTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
 my_snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)

src/tools/collections.py DELETED Viewed

@@ -1,83 +0,0 @@
-import os
-import pandas as pd
-from huggingface_hub import add_collection_item, delete_collection_item, get_collection, update_collection_item
-from huggingface_hub.utils._errors import HfHubHTTPError
-from pandas import DataFrame
-from src.display.utils import AutoEvalColumn, ModelType
-from src.envs import H4_TOKEN, PATH_TO_COLLECTION
-# Specific intervals for the collections
-intervals = {
-    "1B": pd.Interval(0, 1.5, closed="right"),
-    "3B": pd.Interval(2.5, 3.5, closed="neither"),
-    "7B": pd.Interval(6, 8, closed="neither"),
-    "13B": pd.Interval(10, 14, closed="neither"),
-    "30B": pd.Interval(25, 35, closed="neither"),
-    "65B": pd.Interval(60, 70, closed="neither"),
-}
-def update_collections(df: DataFrame):
-    """This function updates the Open LLM Leaderboard model collection with the latest best models for
-    each size category and type.
-    """
-    collection = get_collection(collection_slug=PATH_TO_COLLECTION, token=H4_TOKEN)
-    params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
-    cur_best_models = []
-    ix = 0
-    for type in ModelType:
-        if type.value.name == "":
-            continue
-        for size in intervals:
-            # We filter the df to gather the relevant models
-            type_emoji = [t[0] for t in type.value.symbol]
-            filtered_df = df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-            numeric_interval = pd.IntervalIndex([intervals[size]])
-            mask = params_column.apply(lambda x: any(numeric_interval.contains(x)))
-            filtered_df = filtered_df.loc[mask]
-            best_models = list(
-                filtered_df.sort_values(AutoEvalColumn.average.name, ascending=False)[AutoEvalColumn.dummy.name]
-            )
-            print(type.value.symbol, size, best_models[:10])
-            # We add them one by one to the leaderboard
-            for model in best_models:
-                ix += 1
-                cur_len_collection = len(collection.items)
-                try:
-                    collection = add_collection_item(
-                        PATH_TO_COLLECTION,
-                        item_id=model,
-                        item_type="model",
-                        exists_ok=True,
-                        note=f"Best {type.to_str(' ')} model of around {size} on the leaderboard today!",
-                        token=H4_TOKEN,
-                    )
-                    if (
-                        len(collection.items) > cur_len_collection
-                    ):  # we added an item - we make sure its position is correct
-                        item_object_id = collection.items[-1].item_object_id
-                        update_collection_item(
-                            collection_slug=PATH_TO_COLLECTION, item_object_id=item_object_id, position=ix
-                        )
-                        cur_len_collection = len(collection.items)
-                    cur_best_models.append(model)
-                    break
-                except HfHubHTTPError:
-                    continue
-    collection = get_collection(PATH_TO_COLLECTION, token=H4_TOKEN)
-    for item in collection.items:
-        if item.item_id not in cur_best_models:
-            try:
-                delete_collection_item(
-                    collection_slug=PATH_TO_COLLECTION, item_object_id=item.item_object_id, token=H4_TOKEN
-                )
-            except HfHubHTTPError:
-                continue

src/tools/model_backlinks.py DELETED Viewed

@@ -1,1309 +0,0 @@
-models = [
-    "uni-tianyan/Uni-TianYan",
-    "fangloveskari/ORCA_LLaMA_70B_QLoRA",
-    "garage-bAInd/Platypus2-70B-instruct",
-    "upstage/Llama-2-70b-instruct-v2",
-    "fangloveskari/Platypus_QLoRA_LLaMA_70b",
-    "yeontaek/llama-2-70B-ensemble-v5",
-    "TheBloke/Genz-70b-GPTQ",
-    "TheBloke/Platypus2-70B-Instruct-GPTQ",
-    "psmathur/model_007",
-    "yeontaek/llama-2-70B-ensemble-v4",
-    "psmathur/orca_mini_v3_70b",
-    "ehartford/Samantha-1.11-70b",
-    "MayaPH/GodziLLa2-70B",
-    "psmathur/model_007_v2",
-    "chargoddard/MelangeA-70b",
-    "ehartford/Samantha-1.1-70b",
-    "psmathur/model_009",
-    "upstage/Llama-2-70b-instruct",
-    "yeontaek/llama-2-70B-ensemble-v7",
-    "yeontaek/llama-2-70B-ensemble-v6",
-    "chargoddard/MelangeB-70b",
-    "yeontaek/llama-2-70B-ensemble-v3",
-    "chargoddard/MelangeC-70b",
-    "garage-bAInd/Camel-Platypus2-70B",
-    "yeontaek/llama-2-70B-ensemble-v2",
-    "garage-bAInd/Camel-Platypus2-70B",
-    "migtissera/Synthia-70B-v1.2",
-    "v2ray/LLaMA-2-Wizard-70B-QLoRA",
-    "quantumaikr/llama-2-70b-fb16-orca-chat-10k",
-    "v2ray/LLaMA-2-Wizard-70B-QLoRA",
-    "stabilityai/StableBeluga2",
-    "quantumaikr/llama-2-70b-fb16-guanaco-1k",
-    "garage-bAInd/Camel-Platypus2-70B",
-    "migtissera/Synthia-70B-v1.1",
-    "migtissera/Synthia-70B",
-    "psmathur/model_101",
-    "augtoma/qCammel70",
-    "augtoma/qCammel-70",
-    "augtoma/qCammel-70v1",
-    "augtoma/qCammel-70x",
-    "augtoma/qCammel-70-x",
-    "jondurbin/airoboros-l2-70b-gpt4-1.4.1",
-    "dfurman/llama-2-70b-dolphin-peft",
-    "jondurbin/airoboros-l2-70b-2.1",
-    "TheBloke/llama-2-70b-Guanaco-QLoRA-fp16",
-    "quantumaikr/QuantumLM-llama2-70B-Korean-LoRA",
-    "quantumaikr/quantumairk-llama-2-70B-instruct",
-    "psmathur/model_420",
-    "psmathur/model_51",
-    "garage-bAInd/Camel-Platypus2-70B",
-    "TheBloke/Airoboros-L2-70B-2.1-GPTQ",
-    "OpenAssistant/llama2-70b-oasst-sft-v10",
-    "garage-bAInd/Platypus2-70B",
-    "liuxiang886/llama2-70B-qlora-gpt4",
-    "upstage/llama-65b-instruct",
-    "quantumaikr/llama-2-70b-fb16-korean",
-    "NousResearch/Nous-Hermes-Llama2-70b",
-    "v2ray/LLaMA-2-Jannie-70B-QLoRA",
-    "jondurbin/airoboros-l2-70b-gpt4-m2.0",
-    "jondurbin/airoboros-l2-70b-gpt4-m2.0",
-    "OpenAssistant/llama2-70b-oasst-sft-v10",
-    "yeontaek/llama-2-70B-ensemble-v8",
-    "jondurbin/airoboros-l2-70b-gpt4-2.0",
-    "jarradh/llama2_70b_chat_uncensored",
-    "WizardLM/WizardMath-70B-V1.0",
-    "jordiclive/Llama-2-70b-oasst-1-200",
-    "WizardLM/WizardMath-70B-V1.0",
-    "jondurbin/airoboros-l2-70b-gpt4-2.0",
-    "OpenLemur/lemur-70b-chat-v1",
-    "tiiuae/falcon-180B",
-    "tiiuae/falcon-180B",
-    "stabilityai/StableBeluga1-Delta",
-    "psmathur/model_42_70b",
-    "psmathur/test_42_70b",
-    "TheBloke/fiction.live-Kimiko-V2-70B-fp16",
-    "tiiuae/falcon-180B",
-    "WizardLM/WizardMath-70B-V1.0",
-    "tiiuae/falcon-180B-chat",
-    "jondurbin/airoboros-l2-70b-gpt4-2.0",
-    "ehartford/samantha-1.1-llama-33b",
-    "ajibawa-2023/scarlett-33b",
-    "ddobokki/Llama-2-70b-orca-200k",
-    "TheBloke/gpt4-alpaca-lora_mlp-65B-HF",
-    "tiiuae/falcon-180B-chat",
-    "tiiuae/falcon-180B-chat",
-    "tiiuae/falcon-180B",
-    "TheBloke/Lemur-70B-Chat-v1-GPTQ",
-    "NousResearch/Nous-Puffin-70B",
-    "WizardLM/WizardLM-70B-V1.0",
-    "WizardLM/WizardMath-70B-V1.0",
-    "meta-llama/Llama-2-70b-hf",
-    "TheBloke/Llama-2-70B-fp16",
-    "Weyaxi/llama-2-alpacagpt4-1000step",
-    "WizardLM/WizardLM-70B-V1.0",
-    "simsim314/WizardLM-70B-V1.0-HF",
-    "simsim314/WizardLM-70B-V1.0-HF",
-    "WizardLM/WizardLM-70B-V1.0",
-    "openbmb/UltraLM-65b",
-    "psmathur/model_420_preview",
-    "WizardLM/WizardLM-70B-V1.0",
-    "simsim314/WizardLM-70B-V1.0-HF",
-    "OpenBuddy/openbuddy-llama2-70b-v10.1-bf16",
-    "upstage/llama-30b-instruct-2048",
-    "jondurbin/airoboros-65b-gpt4-1.2",
-    "TheBloke/guanaco-65B-HF",
-    "jondurbin/airoboros-65b-gpt4-1.3",
-    "meta-llama/Llama-2-70b-chat-hf",
-    "ValiantLabs/ShiningValiant",
-    "Faradaylab/Aria-70B",
-    "lilloukas/GPlatty-30B",
-    "TheBloke/VicUnlocked-alpaca-65B-QLoRA-fp16",
-    "jondurbin/airoboros-65b-gpt4-1.4-peft",
-    "jondurbin/airoboros-65b-gpt4-1.4",
-    "jondurbin/airoboros-65b-gpt4-2.0",
-    "TheBloke/WizardLM-70B-V1.0-GPTQ",
-    "TheBloke/WizardLM-70B-V1.0-GPTQ",
-    "ariellee/SuperPlatty-30B",
-    "jondurbin/airoboros-65b-gpt4-1.4",
-    "jondurbin/airoboros-65b-gpt4-2.0",
-    "yeontaek/llama-2-70b-IA3-guanaco",
-    "CalderaAI/30B-Lazarus",
-    "Aspik101/trurl-2-13b-pl-instruct_unload",
-    "ehartford/WizardLM-33B-V1.0-Uncensored",
-    "ehartford/WizardLM-33B-V1.0-Uncensored",
-    "OpenBuddy/openbuddy-llama-65b-v8-bf16",
-    "Aspik101/llama-30b-instruct-2048-PL-lora",
-    "h2oai/h2ogpt-research-oasst1-llama-65b",
-    "Aspik101/llama-30b-instruct-2048-PL-lora",
-    "CalderaAI/30B-Epsilon",
-    "Aspik101/llama-30b-2048-instruct-PL-lora_unload",
-    "jondurbin/airoboros-65b-gpt4-m2.0",
-    "jondurbin/airoboros-65b-gpt4-m2.0",
-    "Aeala/Alpaca-elina-65b",
-    "TheBloke/robin-65b-v2-fp16",
-    "TheBloke/gpt4-alpaca-lora-30b-HF",
-    "TheBloke/Llama-2-70B-chat-GPTQ",
-    "upstage/llama-30b-instruct",
-    "OpenLemur/lemur-70b-v1",
-    "lmsys/vicuna-33b-v1.3",
-    "ausboss/llama-30b-supercot",
-    "ai-business/Luban-13B",
-    "Henk717/airochronos-33B",
-    "lmsys/vicuna-33b-v1.3",
-    "Henk717/airochronos-33B",
-    "bavest/fin-llama-33b-merged",
-    "jondurbin/airoboros-33b-gpt4-1.4",
-    "YeungNLP/firefly-llama-30b",
-    "Aspik101/30B-Lazarus-instruct-PL-lora_unload",
-    "uukuguy/speechless-llama2-luban-orca-platypus-13b",
-    "xxyyy123/test_merge_p_ov1_w0.66_w0.5_n1",
-    "jondurbin/airoboros-33b-gpt4-1.2",
-    "TheBloke/alpaca-lora-65B-HF",
-    "bofenghuang/vigogne-33b-instruct",
-    "yeontaek/llama-2-13B-ensemble-v5",
-    "garage-bAInd/Platypus-30B",
-    "Open-Orca/OpenOrca-Platypus2-13B",
-    "kajdun/viwaai-30b_v4",
-    "lilloukas/Platypus-30B",
-    "Open-Orca/OpenOrca-Platypus2-13B",
-    "Henk717/chronoboros-33B",
-    "jondurbin/airoboros-33b-2.1",
-    "HiTZ/alpaca-lora-65b-en-pt-es-ca",
-    "quantumaikr/QuantumLM-70B-hf",
-    "uukuguy/speechless-llama2-13b",
-    "uukuguy/speechless-llama2-hermes-orca-platypus-13b",
-    "openaccess-ai-collective/manticore-30b-chat-pyg-alpha",
-    "LLMs/WizardLM-30B-V1.0",
-    "TheBloke/WizardLM-30B-fp16",
-    "openaccess-ai-collective/hippogriff-30b-chat",
-    "concedo/Vicuzard-30B-Uncensored",
-    "TFLai/OpenOrca-Platypus2-13B-QLoRA-0.80-epoch",
-    "huggingface/llama-65b",
-    "huggyllama/llama-65b",
-    "gaodrew/gaodrew-llama-30b-instruct-2048-Open-Platypus-100steps",
-    "uukuguy/speechless-llama2-hermes-orca-platypus-wizardlm-13b",
-    "Sao10K/Mythical-Destroyer-V2-L2-13B",
-    "camel-ai/CAMEL-33B-Combined-Data",
-    "dsvv-cair/alpaca-cleaned-llama-30b-bf16",
-    "MetaIX/GPT4-X-Alpasta-30b",
-    "garage-bAInd/Stable-Platypus2-13B",
-    "TFLai/Luban-Platypus2-13B-QLora-0.80-epoch",
-    "TheBloke/OpenOrca-Platypus2-13B-GPTQ",
-    "IkariDev/Athena-tmp",
-    "OpenBuddyEA/openbuddy-llama-30b-v7.1-bf16",
-    "OpenBuddyEA/openbuddy-llama-30b-v7.1-bf16",
-    "Open-Orca/OpenOrcaxOpenChat-Preview2-13B",
-    "psmathur/model_007_13b_v2",
-    "Aspik101/Vicuzard-30B-Uncensored-instruct-PL-lora_unload",
-    "jondurbin/airoboros-33b-gpt4-m2.0",
-    "Sao10K/Mythical-Destroyer-L2-13B",
-    "TheBloke/Wizard-Vicuna-30B-Uncensored-fp16",
-    "ehartford/Wizard-Vicuna-30B-Uncensored",
-    "TFLai/Nova-13B",
-    "TheBloke/robin-33B-v2-fp16",
-    "totally-not-an-llm/PuddleJumper-13b",
-    "Aeala/VicUnlocked-alpaca-30b",
-    "Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf",
-    "jondurbin/airoboros-33b-gpt4",
-    "jondurbin/airoboros-33b-gpt4-m2.0",
-    "tiiuae/falcon-40b-instruct",
-    "psmathur/orca_mini_v3_13b",
-    "Aeala/GPT4-x-AlpacaDente-30b",
-    "MayaPH/GodziLLa-30B",
-    "jondurbin/airoboros-33b-gpt4-m2.0",
-    "TFLai/SpeechlessV1-Nova-13B",
-    "yeontaek/llama-2-13B-ensemble-v4",
-    "ajibawa-2023/carl-33b",
-    "jondurbin/airoboros-33b-gpt4-2.0",
-    "TFLai/Stable-Platypus2-13B-QLoRA-0.80-epoch",
-    "jondurbin/airoboros-33b-gpt4-1.3",
-    "TehVenom/oasst-sft-6-llama-33b-xor-MERGED-16bit",
-    "TFLai/OrcaMini-Platypus2-13B-QLoRA-0.80-epoch",
-    "jondurbin/airoboros-33b-gpt4-2.0",
-    "chargoddard/Chronorctypus-Limarobormes-13b",
-    "jondurbin/airoboros-33b-gpt4-1.3",
-    "Open-Orca/OpenOrca-Platypus2-13B",
-    "FelixChao/vicuna-33b-coder",
-    "FelixChao/vicuna-33b-coder",
-    "Gryphe/MythoMix-L2-13b",
-    "Aeala/Enterredaas-33b",
-    "yeontaek/llama-2-13B-ensemble-v1",
-    "TFLai/OpenOrcaPlatypus2-Platypus2-13B-QLora-0.80-epoch",
-    "TFLai/Ensemble5-Platypus2-13B-QLora-0.80-epoch",
-    "yeontaek/llama-2-13B-ensemble-v3",
-    "TFLai/MythoMix-Platypus2-13B-QLoRA-0.80-epoch",
-    "yihan6324/llama2-13b-instructmining-40k-sharegpt",
-    "timdettmers/guanaco-33b-merged",
-    "TFLai/EnsembleV5-Nova-13B",
-    "circulus/Llama-2-13b-orca-v1",
-    "Undi95/ReMM-SLERP-L2-13B",
-    "Gryphe/MythoMax-L2-13b",
-    "stabilityai/StableBeluga-13B",
-    "circulus/Llama-2-13b-orca-v1",
-    "ehartford/WizardLM-30B-Uncensored",
-    "The-Face-Of-Goonery/huginnv1.2",
-    "TheBloke/OpenOrcaxOpenChat-Preview2-13B-GPTQ",
-    "Sao10K/Stheno-L2-13B",
-    "bofenghuang/vigogne-2-13b-instruct",
-    "The-Face-Of-Goonery/Huginn-13b-FP16",
-    "grimpep/L2-MythoMax22b-instruct-Falseblock",
-    "TFLai/Nous-Hermes-Platypus2-13B-QLoRA-0.80-epoch",
-    "yeontaek/Platypus2xOpenOrca-13B-IA3-v4",
-    "yeontaek/Platypus2xOpenOrca-13B-IA3",
-    "yeontaek/Platypus2xOpenOrca-13B-IA3-ensemble",
-    "Open-Orca/LlongOrca-13B-16k",
-    "Sao10K/Stheno-Inverted-L2-13B",
-    "garage-bAInd/Camel-Platypus2-13B",
-    "digitous/Alpacino30b",
-    "NousResearch/Nous-Hermes-Llama2-13b",
-    "yeontaek/Platypus2xOpenOrca-13B-IA3-v3",
-    "TFLai/MythicalDestroyerV2-Platypus2-13B-QLora-0.80-epoch",
-    "TheBloke/VicUnlocked-30B-LoRA-HF",
-    "Undi95/Nous-Hermes-13B-Code",
-    "The-Face-Of-Goonery/Chronos-Beluga-v2-13bfp16",
-    "NousResearch/Nous-Hermes-Llama2-13b",
-    "Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b",
-    "TheBloke/Wizard-Vicuna-30B-Uncensored-GPTQ",
-    "Open-Orca/OpenOrcaxOpenChat-Preview2-13B",
-    "Austism/chronos-hermes-13b-v2",
-    "yeontaek/Platypus2xOpenOrca-13B-IA3-v2.1",
-    "yeontaek/Platypus2xOpenOrca-13B-IA3-v2",
-    "Gryphe/MythoLogic-L2-13b",
-    "augtoma/qCammel-13",
-    "YeungNLP/firefly-llama2-13b-v1.2",
-    "Aspik101/StableBeluga-13B-instruct-PL-lora_unload",
-    "andreaskoepf/llama2-13b-megacode2_min100",
-    "rombodawg/LosslessMegaCoder-llama2-13b-mini",
-    "yulan-team/YuLan-Chat-2-13b-fp16",
-    "elinas/chronos-33b",
-    "YeungNLP/firefly-llama2-13b",
-    "Sao10K/Medusa-13b",
-    "OptimalScale/robin-65b-v2-delta",
-    "minlik/chinese-alpaca-33b-merged",
-    "OpenAssistant/llama2-13b-megacode2-oasst",
-    "TheBloke/OpenAssistant-SFT-7-Llama-30B-HF",
-    "Undi95/UndiMix-v1-13b",
-    "ehartford/Samantha-1.11-13b",
-    "beaugogh/Llama2-13b-sharegpt4",
-    "Aeala/GPT4-x-AlpacaDente2-30b",
-    "luffycodes/nash-vicuna-13b-v1dot5-ep2-w-rag-w-simple",
-    "WizardLM/WizardLM-13B-V1.1",
-    "uukuguy/speechless-orca-platypus-coig-lite-2k-0.6e-13b",
-    "huggyllama/llama-30b",
-    "Undi95/ReMM-L2-13B-PIPPA",
-    "Undi95/ReMM-L2-13B",
-    "gaodrew/gaodrew-gorgonzola-13b",
-    "lmsys/vicuna-13b-v1.5",
-    "yeontaek/Platypus2xOpenOrca-13B-LoRa",
-    "Yhyu13/llama-30B-hf-openassitant",
-    "huggingface/llama-30b",
-    "lmsys/vicuna-13b-v1.5",
-    "TFLai/Athena-Platypus2-13B-QLora-0.80-epoch",
-    "TheBloke/dromedary-65b-lora-HF",
-    "yeontaek/llama-2-13b-Beluga-QLoRA",
-    "The-Face-Of-Goonery/Huginn-13b-V4",
-    "The-Face-Of-Goonery/Huginn-13b-v4.5",
-    "The-Face-Of-Goonery/Huginn-v3-13b",
-    "tiiuae/falcon-40b",
-    "WhoTookMyAmogusNickname/NewHope_HF_not_official",
-    "gaodrew/OpenOrca-Platypus2-13B-thera-1250",
-    "SLAM-group/NewHope",
-    "garage-bAInd/Platypus2-13B",
-    "migtissera/Synthia-13B",
-    "elinas/chronos-13b-v2",
-    "mosaicml/mpt-30b-chat",
-    "CHIH-HUNG/llama-2-13b-OpenOrca_5w",
-    "uukuguy/speechless-hermes-coig-lite-13b",
-    "TheBloke/tulu-30B-fp16",
-    "uukuguy/speechless-hermes-coig-lite-13b",
-    "xDAN-AI/xDAN_13b_l2_lora",
-    "lmsys/vicuna-13b-v1.5-16k",
-    "openchat/openchat_v3.1",
-    "CHIH-HUNG/llama-2-13b-dolphin_5w",
-    "Aspik101/vicuna-13b-v1.5-PL-lora_unload",
-    "Undi95/MLewd-L2-13B",
-    "ehartford/minotaur-llama2-13b-qlora",
-    "kajdun/iubaris-13b-v3",
-    "TFLai/Limarp-Platypus2-13B-QLoRA-0.80-epoch",
-    "openchat/openchat_v3.1",
-    "uukuguy/speechless-orca-platypus-coig-lite-4k-0.6e-13b",
-    "ziqingyang/chinese-alpaca-2-13b",
-    "TFLai/Airboros2.1-Platypus2-13B-QLora-0.80-epoch",
-    "yeontaek/llama-2-13b-Guanaco-QLoRA",
-    "lmsys/vicuna-13b-v1.5-16k",
-    "ehartford/based-30b",
-    "kingbri/airolima-chronos-grad-l2-13B",
-    "openchat/openchat_v3.2",
-    "uukuguy/speechless-orca-platypus-coig-lite-4k-0.5e-13b",
-    "yeontaek/Platypus2-13B-LoRa",
-    "kingbri/chronolima-airo-grad-l2-13B",
-    "openchat/openchat_v3.2",
-    "TFLai/PuddleJumper-Platypus2-13B-QLoRA-0.80-epoch",
-    "shareAI/llama2-13b-Chinese-chat",
-    "ehartford/WizardLM-1.0-Uncensored-Llama2-13b",
-    "Aspik101/Redmond-Puffin-13B-instruct-PL-lora_unload",
-    "yeontaek/llama-2-13B-ensemble-v6",
-    "WizardLM/WizardLM-13B-V1.2",
-    "TheBloke/WizardLM-13B-V1.1-GPTQ",
-    "bhenrym14/airophin-13b-pntk-16k-fp16",
-    "ehartford/WizardLM-1.0-Uncensored-Llama2-13b",
-    "Mikael110/llama-2-13b-guanaco-fp16",
-    "yeontaek/airoboros-2.1-llama-2-13B-QLoRa",
-    "CalderaAI/13B-Legerdemain-L2",
-    "grimpep/llama2-22b-wizard_vicuna",
-    "grimpep/llama2-22B-GPLATTY",
-    "bhenrym14/airophin-13b-pntk-16k-fp16",
-    "yeontaek/llama-2-13b-QLoRA",
-    "OpenAssistant/llama2-13b-orca-8k-3319",
-    "TheBloke/WizardLM-13B-V1-1-SuperHOT-8K-fp16",
-    "duliadotio/dulia-13b-8k-alpha",
-    "Undi95/LewdEngine",
-    "OpenBuddy/openbuddy-llama2-13b-v8.1-fp16",
-    "CHIH-HUNG/llama-2-13b-open_orca_20w",
-    "bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16",
-    "FlagAlpha/Llama2-Chinese-13b-Chat",
-    "LLMs/WizardLM-13B-V1.0",
-    "chansung/gpt4-alpaca-lora-13b-decapoda-1024",
-    "TheBloke/wizardLM-13B-1.0-fp16",
-    "digitous/13B-Chimera",
-    "yeontaek/Platypus2xOpenOrcaxGuanaco-13B-LoRa",
-    "jondurbin/airoboros-l2-13b-2.1",
-    "Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b",
-    "TheBloke/UltraLM-13B-fp16",
-    "openaccess-ai-collective/minotaur-13b-fixed",
-    "NousResearch/Redmond-Puffin-13B",
-    "KoboldAI/LLaMA2-13B-Holomax",
-    "Lajonbot/WizardLM-13B-V1.2-PL-lora_unload",
-    "yeontaek/Platypus2-13B-LoRa-v2",
-    "TheBloke/airoboros-13B-HF",
-    "jondurbin/airoboros-13b",
-    "jjaaaww/posi_13b",
-    "CoolWP/llama-2-13b-guanaco-fp16",
-    "yeontaek/Platypus2-13B-QLoRa",
-    "h2oai/h2ogpt-research-oig-oasst1-512-30b",
-    "dfurman/llama-2-13b-guanaco-peft",
-    "NousResearch/Redmond-Puffin-13B",
-    "pe-nlp/llama-2-13b-platypus-vicuna-wizard",
-    "CHIH-HUNG/llama-2-13b-dolphin_20w",
-    "NousResearch/Nous-Hermes-13b",
-    "NobodyExistsOnTheInternet/GiftedConvo13bLoraNoEconsE4",
-    "ehartford/Wizard-Vicuna-13B-Uncensored",
-    "TheBloke/Wizard-Vicuna-13B-Uncensored-HF",
-    "openchat/openchat_v3.2_super",
-    "bhenrym14/airophin-v2-13b-PI-8k-fp16",
-    "openaccess-ai-collective/manticore-13b",
-    "The-Face-Of-Goonery/Huginn-22b-Prototype",
-    "jphme/Llama-2-13b-chat-german",
-    "grimpep/llama2-28B-Airo03",
-    "TheBloke/Kimiko-v2-13B-fp16",
-    "FPHam/Free_Sydney_13b_HF",
-    "lmsys/vicuna-13b-v1.3",
-    "FelixChao/llama2-13b-math1.1",
-    "CalderaAI/13B-BlueMethod",
-    "meta-llama/Llama-2-13b-chat-hf",
-    "deepse/CodeUp-Llama-2-13b-chat-hf",
-    "WizardLM/WizardMath-13B-V1.0",
-    "WizardLM/WizardMath-13B-V1.0",
-    "HyperbeeAI/Tulpar-7b-v0",
-    "xxyyy123/test_qkvo_adptor",
-    "xxyyy123/mc_data_30k_from_platpus_orca_7b_10k_v1_lora_qkvo_rank14_v2",
-    "openchat/openchat_v2_w",
-    "FelixChao/llama2-13b-math1.1",
-    "psmathur/orca_mini_v3_7b",
-    "TehVenom/Metharme-13b-Merged",
-    "xxyyy123/10k_v1_lora_qkvo_rank14_v3",
-    "OpenAssistant/llama2-13b-orca-v2-8k-3166",
-    "openaccess-ai-collective/wizard-mega-13b",
-    "jondurbin/airoboros-13b-gpt4-1.4",
-    "jondurbin/airoboros-13b-gpt4-1.4-fp16",
-    "Monero/Manticore-13b-Chat-Pyg-Guanaco",
-    "FelixChao/llama2-13b-math1.2",
-    "chargoddard/platypus-2-22b-relora",
-    "FelixChao/llama2-13b-math1.2",
-    "Gryphe/MythoBoros-13b",
-    "CalderaAI/13B-Ouroboros",
-    "OpenAssistant/llama2-13b-orca-v2-8k-3166",
-    "heegyu/LIMA2-13b-hf",
-    "digitous/13B-HyperMantis",
-    "Gryphe/MythoLogic-13b",
-    "TheBloke/Airoboros-L2-13B-2.1-GPTQ",
-    "chargoddard/platypus2-22b-relora",
-    "openchat/openchat_v2",
-    "yeontaek/Platypus2-13B-IA3",
-    "stabilityai/StableBeluga-7B",
-    "circulus/Llama-2-7b-orca-v1",
-    "budecosystem/genz-13b-v2",
-    "TheBloke/gpt4-x-vicuna-13B-HF",
-    "NobodyExistsOnTheInternet/GiftedConvo13bLoraNoEcons",
-    "zarakiquemparte/zarafusionex-1.1-l2-7b",
-    "Lajonbot/tableBeluga-7B-instruct-pl-lora_unload",
-    "jondurbin/airoboros-13b-gpt4",
-    "gaodrew/gaodrew-gorgonzola-13b",
-    "jondurbin/airoboros-13b-gpt4-1.1",
-    "TheBloke/gpt4-alpaca-lora-13B-HF",
-    "zarakiquemparte/zarablendex-vq-l2-7b",
-    "openaccess-ai-collective/manticore-13b-chat-pyg",
-    "Lajonbot/Llama-2-13b-hf-instruct-pl-lora_unload",
-    "NobodyExistsOnTheInternet/PuffedLIMA13bQLORA",
-    "xxyyy123/10k_v1_lora_qkvo_rank28_v2",
-    "jondurbin/airoboros-l2-13b-gpt4-1.4.1",
-    "dhmeltzer/Llama-2-13b-hf-eli5-wiki-1024_r_64_alpha_16",
-    "NobodyExistsOnTheInternet/PuffedConvo13bLoraE4",
-    "yihan6324/llama2-7b-instructmining-40k-sharegpt",
-    "CHIH-HUNG/llama-2-13b-Open_Platypus_and_ccp_2.6w",
-    "Aeala/GPT4-x-Alpasta-13b",
-    "psmathur/orca_mini_v2_13b",
-    "YeungNLP/firefly-llama-13b",
-    "psmathur/orca_mini_v2_13b",
-    "zarakiquemparte/zarafusionix-l2-7b",
-    "yihan6324/llama2-7b-instructmining-60k-sharegpt",
-    "yihan6324/llama-2-7b-instructmining-60k-sharegpt",
-    "layoric/llama-2-13b-code-alpaca",
-    "bofenghuang/vigogne-13b-instruct",
-    "Lajonbot/vicuna-13b-v1.3-PL-lora_unload",
-    "lvkaokao/llama2-7b-hf-chat-lora-v3",
-    "ehartford/dolphin-llama-13b",
-    "YeungNLP/firefly-llama-13b-v1.2",
-    "TheBloke/Kimiko-13B-fp16",
-    "kevinpro/Vicuna-13B-CoT",
-    "eachadea/vicuna-13b-1.1",
-    "pillowtalks-ai/delta13b",
-    "TheBloke/vicuna-13B-1.1-HF",
-    "TheBloke/Vicuna-13B-CoT-fp16",
-    "lmsys/vicuna-13b-delta-v1.1",
-    "lmsys/vicuna-13b-v1.1",
-    "xxyyy123/20k_v1_lora_qkvo_rank14_v2",
-    "TheBloke/guanaco-13B-HF",
-    "TheBloke/vicuna-13b-v1.3.0-GPTQ",
-    "edor/Stable-Platypus2-mini-7B",
-    "totally-not-an-llm/EverythingLM-13b-V2-16k",
-    "zarakiquemparte/zaraxe-l2-7b",
-    "beaugogh/Llama2-7b-openorca-mc-v2",
-    "TheBloke/Nous-Hermes-13B-SuperHOT-8K-fp16",
-    "quantumaikr/QuantumLM",
-    "jondurbin/airoboros-13b-gpt4-1.2",
-    "TheBloke/robin-13B-v2-fp16",
-    "TFLai/llama-2-13b-4bit-alpaca-gpt4",
-    "yihan6324/llama2-7b-instructmining-orca-40k",
-    "dvruette/oasst-llama-13b-2-epochs",
-    "Open-Orca/LlongOrca-7B-16k",
-    "Aspik101/Nous-Hermes-13b-pl-lora_unload",
-    "ehartford/Samantha-1.11-CodeLlama-34b",
-    "nkpz/llama2-22b-chat-wizard-uncensored",
-    "bofenghuang/vigogne-13b-chat",
-    "beaugogh/Llama2-7b-openorca-mc-v1",
-    "OptimalScale/robin-13b-v2-delta",
-    "pe-nlp/llama-2-13b-vicuna-wizard",
-    "chargoddard/llama2-22b",
-    "gywy/llama2-13b-chinese-v1",
-    "frank098/Wizard-Vicuna-13B-juniper",
-    "IGeniusDev/llama13B-quant8-testv1-openorca-customdataset",
-    "CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-gate_up_down_proj",
-    "eachadea/vicuna-13b",
-    "yihan6324/llama2-7b-instructmining-orca-90k",
-    "chargoddard/llama2-22b-blocktriangular",
-    "luffycodes/mcq-vicuna-13b-v1.5",
-    "Yhyu13/chimera-inst-chat-13b-hf",
-    "luffycodes/mcq-vicuna-13b-v1.5",
-    "chargoddard/ypotryll-22b-epoch2-qlora",
-    "totally-not-an-llm/EverythingLM-13b-16k",
-    "luffycodes/mcq-hal-vicuna-13b-v1.5",
-    "openaccess-ai-collective/minotaur-13b",
-    "IGeniusDev/llama13B-quant8-testv1-openorca-customdataset",
-    "chargoddard/llama2-22b-blocktriangular",
-    "TFLai/Platypus2-13B-QLoRA-0.80-epoch",
-    "meta-llama/Llama-2-13b-hf",
-    "CHIH-HUNG/llama-2-13b-huangyt_FINETUNE2_3w-gate_up_down_proj",
-    "luffycodes/mcq-hal-vicuna-13b-v1.5",
-    "TheBloke/Llama-2-13B-fp16",
-    "TaylorAI/Flash-Llama-13B",
-    "shareAI/bimoGPT-llama2-13b",
-    "wahaha1987/llama_13b_sharegpt94k_fastchat",
-    "openchat/openchat_8192",
-    "CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w-q_k_v_o_proj",
-    "dvruette/llama-13b-pretrained-sft-do2",
-    "CHIH-HUNG/llama-2-13b-alpaca-test",
-    "OpenBuddy/openbuddy-llama2-13b-v11.1-bf16",
-    "CHIH-HUNG/llama-2-13b-FINETUNE2_TEST_2.2w",
-    "project-baize/baize-v2-13b",
-    "jondurbin/airoboros-l2-13b-gpt4-m2.0",
-    "yeontaek/Platypus2xOpenOrca-13B-LoRa-v2",
-    "CHIH-HUNG/llama-2-13b-huangyt_FINETUNE2_3w",
-    "xzuyn/Alpacino-SuperCOT-13B",
-    "jondurbin/airoboros-l2-13b-gpt4-2.0",
-    "aiplanet/effi-13b",
-    "clibrain/Llama-2-13b-ft-instruct-es",
-    "CHIH-HUNG/llama-2-13b-huangyt_Fintune_1_17w",
-    "bofenghuang/vigogne-2-7b-instruct",
-    "CHIH-HUNG/llama-2-13b-huangyt_FINETUNE2_3w-q_k_v_o_proj",
-    "bofenghuang/vigogne-2-7b-chat",
-    "aiplanet/effi-13b",
-    "haonan-li/bactrian-x-llama-13b-merged",
-    "beaugogh/Llama2-7b-sharegpt4",
-    "HWERI/Llama2-7b-sharegpt4",
-    "jondurbin/airoboros-13b-gpt4-1.3",
-    "jondurbin/airoboros-c34b-2.1",
-    "junelee/wizard-vicuna-13b",
-    "TheBloke/wizard-vicuna-13B-HF",
-    "Open-Orca/OpenOrca-Preview1-13B",
-    "TheBloke/h2ogpt-oasst1-512-30B-HF",
-    "TheBloke/Llama-2-13B-GPTQ",
-    "camel-ai/CAMEL-13B-Combined-Data",
-    "lmsys/vicuna-7b-v1.5",
-    "lmsys/vicuna-7b-v1.5-16k",
-    "lmsys/vicuna-7b-v1.5",
-    "ausboss/llama-13b-supercot",
-    "TheBloke/tulu-13B-fp16",
-    "NousResearch/Nous-Hermes-llama-2-7b",
-    "jlevin/guanaco-13b-llama-2",
-    "lmsys/vicuna-7b-v1.5-16k",
-    "dvruette/llama-13b-pretrained",
-    "nkpz/llama2-22b-daydreamer-v3",
-    "dvruette/llama-13b-pretrained-dropout",
-    "jondurbin/airoboros-l2-13b-2.1",
-    "LLMs/Stable-Vicuna-13B",
-    "64bits/LexPodLM-13B",
-    "lizhuang144/llama_mirror_13b_v1.0",
-    "TheBloke/stable-vicuna-13B-HF",
-    "zarakiquemparte/zaraxls-l2-7b",
-    "TheBloke/Llama-2-13B-GPTQ",
-    "Kiddyz/testlm-3",
-    "migtissera/Synthia-7B",
-    "zarakiquemparte/zarablend-l2-7b",
-    "mosaicml/mpt-30b-instruct",
-    "PocketDoc/Dans-PileOfSets-Mk1-llama-13b-merged",
-    "vonjack/Qwen-LLaMAfied-HFTok-7B-Chat",
-    "l3utterfly/llama2-7b-layla",
-    "Lajonbot/vicuna-7b-v1.5-PL-lora_unload",
-    "heegyu/LIMA-13b-hf",
-    "frank098/WizardLM_13B_juniper",
-    "ashercn97/manatee-7b",
-    "chavinlo/gpt4-x-alpaca",
-    "PocketDoc/Dans-PersonalityEngine-13b",
-    "ehartford/WizardLM-1.0-Uncensored-CodeLlama-34b",
-    "digitous/Alpacino13b",
-    "edor/Hermes-Platypus2-mini-7B",
-    "lvkaokao/llama2-7b-hf-chat-lora-v2",
-    "Kiddyz/testlm-1-1",
-    "Kiddyz/testlm",
-    "Kiddyz/testlm-1",
-    "Kiddyz/testlm2",
-    "radm/Philosophy-Platypus2-13b",
-    "aiplanet/effi-13b",
-    "Harshvir/Llama-2-7B-physics",
-    "YeungNLP/firefly-ziya-13b",
-    "LinkSoul/Chinese-Llama-2-7b",
-    "PeanutJar/LLaMa-2-PeanutButter_v10-7B",
-    "OpenBuddy/openbuddy-llama2-13b-v11-bf16",
-    "StudentLLM/Alpagasus-2-13B-QLoRA-pipeline",
-    "meta-llama/Llama-2-13b-hf",
-    "WizardLM/WizardCoder-Python-34B-V1.0",
-    "dvruette/llama-13b-pretrained-sft-epoch-1",
-    "camel-ai/CAMEL-13B-Role-Playing-Data",
-    "ziqingyang/chinese-llama-2-13b",
-    "rombodawg/LosslessMegaCoder-llama2-7b-mini",
-    "TheBloke/koala-13B-HF",
-    "lmsys/vicuna-7b-delta-v1.1",
-    "eachadea/vicuna-7b-1.1",
-    "Ejafa/vicuna_7B_vanilla_1.1",
-    "lvkaokao/llama2-7b-hf-chat-lora",
-    "OpenBuddy/openbuddy-atom-13b-v9-bf16",
-    "Norquinal/llama-2-7b-claude-chat-rp",
-    "Danielbrdz/Barcenas-7b",
-    "heegyu/WizardVicuna2-13b-hf",
-    "meta-llama/Llama-2-7b-chat-hf",
-    "PeanutJar/LLaMa-2-PeanutButter_v14-7B",
-    "PeanutJar/LLaMa-2-PeanutButter_v4-7B",
-    "davzoku/cria-llama2-7b-v1.3",
-    "OpenBuddy/openbuddy-atom-13b-v9-bf16",
-    "lvkaokao/llama2-7b-hf-instruction-lora",
-    "Tap-M/Luna-AI-Llama2-Uncensored",
-    "ehartford/Samantha-1.11-7b",
-    "WizardLM/WizardCoder-Python-34B-V1.0",
-    "TheBloke/Manticore-13B-Chat-Pyg-Guanaco-SuperHOT-8K-GPTQ",
-    "Mikael110/llama-2-7b-guanaco-fp16",
-    "garage-bAInd/Platypus2-7B",
-    "PeanutJar/LLaMa-2-PeanutButter_v18_B-7B",
-    "mosaicml/mpt-30b",
-    "garage-bAInd/Platypus2-7B",
-    "huggingface/llama-13b",
-    "dvruette/oasst-llama-13b-1000-steps",
-    "jordiclive/gpt4all-alpaca-oa-codealpaca-lora-13b",
-    "huggyllama/llama-13b",
-    "Voicelab/trurl-2-7b",
-    "TFLai/llama-13b-4bit-alpaca",
-    "gywy/llama2-13b-chinese-v2",
-    "lmsys/longchat-13b-16k",
-    "Aspik101/trurl-2-7b-pl-instruct_unload",
-    "WizardLM/WizardMath-7B-V1.0",
-    "Norquinal/llama-2-7b-claude-chat",
-    "TheTravellingEngineer/llama2-7b-chat-hf-dpo",
-    "HuggingFaceH4/starchat-beta",
-    "joehuangx/spatial-vicuna-7b-v1.5-LoRA",
-    "conceptofmind/LLongMA-2-13b-16k",
-    "tianyil1/denas-llama2",
-    "lmsys/vicuna-7b-v1.3",
-    "conceptofmind/LLongMA-2-13b-16k",
-    "openchat/opencoderplus",
-    "ajibawa-2023/scarlett-7b",
-    "dhmeltzer/llama-7b-SFT_eli5_wiki65k_1024_r_64_alpha_16_merged",
-    "psyche/kollama2-7b-v2",
-    "heegyu/LIMA2-7b-hf",
-    "dhmeltzer/llama-7b-SFT-qlora-eli5-wiki_DPO_ds_RM_top_2_1024_r_64_alpha_16",
-    "abhishek/llama2guanacotest",
-    "jondurbin/airoboros-l2-7b-2.1",
-    "llama-anon/instruct-13b",
-    "FelixChao/vicuna-7B-physics",
-    "Aspik101/Llama-2-7b-hf-instruct-pl-lora_unload",
-    "shibing624/chinese-alpaca-plus-13b-hf",
-    "davzoku/cria-llama2-7b-v1.3_peft",
-    "quantumaikr/llama-2-7b-hf-guanaco-1k",
-    "togethercomputer/Llama-2-7B-32K-Instruct",
-    "sia-ai/llama-2-7b-1-percent-open-orca-1000-steps-v0",
-    "TheTravellingEngineer/llama2-7b-hf-guanaco",
-    "Lajonbot/Llama-2-7b-chat-hf-instruct-pl-lora_unload",
-    "jondurbin/airoboros-l2-7b-gpt4-1.4.1",
-    "wahaha1987/llama_7b_sharegpt94k_fastchat",
-    "FelixChao/vicuna-7B-chemical",
-    "TinyPixel/llama2-7b-oa",
-    "chaoyi-wu/MedLLaMA_13B",
-    "edor/Platypus2-mini-7B",
-    "RoversX/llama-2-7b-hf-small-shards-Samantha-V1-SFT",
-    "venkycs/llama-v2-7b-32kC-Security",
-    "psyche/kollama2-7b",
-    "Fredithefish/Guanaco-7B-Uncensored",
-    "TheTravellingEngineer/llama2-7b-chat-hf-guanaco",
-    "ehartford/WizardLM-13B-Uncensored",
-    "PocketDoc/Dans-CreepingSenseOfDoom",
-    "wenge-research/yayi-7b-llama2",
-    "georgesung/llama2_7b_chat_uncensored",
-    "TinyPixel/llama2-7b-instruct",
-    "quantumaikr/QuantumLM-7B",
-    "xzuyn/MedicWizard-7B",
-    "wenge-research/yayi-7b-llama2",
-    "TinyPixel/lima-test",
-    "elyza/ELYZA-japanese-Llama-2-7b-instruct",
-    "lgaalves/llama-2-7b-hf_open-platypus",
-    "ziqingyang/chinese-alpaca-2-7b",
-    "TehVenom/Pygmalion-Vicuna-1.1-7b",
-    "meta-llama/Llama-2-7b-hf",
-    "bongchoi/test-llama2-7b",
-    "TaylorAI/Flash-Llama-7B",
-    "TheTravellingEngineer/llama2-7b-chat-hf-v2",
-    "TheTravellingEngineer/llama2-7b-chat-hf-v4",
-    "kashif/stack-llama-2",
-    "PeanutJar/LLaMa-2-PeanutButter_v18_A-7B",
-    "ToolBench/ToolLLaMA-7b-LoRA",
-    "Monero/WizardLM-13b-OpenAssistant-Uncensored",
-    "TheTravellingEngineer/llama2-7b-chat-hf-v2",
-    "TheTravellingEngineer/llama2-7b-chat-hf-v4",
-    "mrm8488/llama-2-coder-7b",
-    "elyza/ELYZA-japanese-Llama-2-7b-fast-instruct",
-    "clibrain/Llama-2-7b-ft-instruct-es",
-    "medalpaca/medalpaca-7b",
-    "TheBloke/tulu-7B-fp16",
-    "OpenBuddy/openbuddy-openllama-13b-v7-fp16",
-    "TaylorAI/FLAN-Llama-7B-2_Llama2-7B-Flash_868_full_model",
-    "Aspik101/vicuna-7b-v1.3-instruct-pl-lora_unload",
-    "jondurbin/airoboros-l2-7b-gpt4-2.0",
-    "dhmeltzer/llama-7b-SFT_ds_eli5_1024_r_64_alpha_16_merged",
-    "GOAT-AI/GOAT-7B-Community",
-    "AtomEchoAI/AtomGPT_56k",
-    "julianweng/Llama-2-7b-chat-orcah",
-    "TehVenom/Pygmalion-13b-Merged",
-    "jondurbin/airoboros-7b-gpt4-1.1",
-    "dhmeltzer/llama-7b-SFT_ds_wiki65k_1024_r_64_alpha_16_merged",
-    "bofenghuang/vigogne-7b-chat",
-    "lmsys/longchat-7b-v1.5-32k",
-    "jondurbin/airoboros-l2-7b-gpt4-m2.0",
-    "synapsoft/Llama-2-7b-chat-hf-flan2022-1.2M",
-    "jondurbin/airoboros-7b-gpt4-1.4",
-    "Charlie911/vicuna-7b-v1.5-lora-mctaco",
-    "yihan6324/instructmining-platypus-15k",
-    "meta-llama/Llama-2-7b-hf",
-    "TheTravellingEngineer/llama2-7b-chat-hf-v3",
-    "quantumaikr/KoreanLM-hf",
-    "openthaigpt/openthaigpt-1.0.0-alpha-7b-chat-ckpt-hf",
-    "TheBloke/Llama-2-7B-GPTQ",
-    "TheBloke/Llama-2-7B-GPTQ",
-    "LLMs/AlpacaGPT4-7B-elina",
-    "ehartford/Wizard-Vicuna-7B-Uncensored",
-    "TheBloke/Wizard-Vicuna-7B-Uncensored-HF",
-    "TheTravellingEngineer/llama2-7b-chat-hf-v3",
-    "golaxy/gowizardlm",
-    "ehartford/dolphin-llama2-7b",
-    "CHIH-HUNG/llama-2-7b-dolphin_10w-test",
-    "mncai/chatdoctor",
-    "psyche/kollama2-7b-v3",
-    "jondurbin/airoboros-7b-gpt4",
-    "jondurbin/airoboros-7b",
-    "TheBloke/airoboros-7b-gpt4-fp16",
-    "mosaicml/mpt-7b-8k-chat",
-    "elyza/ELYZA-japanese-Llama-2-7b",
-    "bofenghuang/vigogne-7b-instruct",
-    "jxhong/CAlign-alpaca-7b",
-    "golaxy/goims",
-    "jondurbin/airoboros-7b-gpt4-1.2",
-    "jphme/orca_mini_v2_ger_7b",
-    "psmathur/orca_mini_v2_7b",
-    "notstoic/PygmalionCoT-7b",
-    "golaxy/gogpt2-13b",
-    "golaxy/gogpt2-13b-chat",
-    "togethercomputer/LLaMA-2-7B-32K",
-    "TheBloke/wizardLM-7B-HF",
-    "keyfan/vicuna-chinese-replication-v1.1",
-    "golaxy/gogpt2-7b",
-    "aiplanet/effi-7b",
-    "arver/llama7b-qlora",
-    "titan087/OpenLlama13B-Guanaco",
-    "chavinlo/alpaca-native",
-    "project-baize/baize-healthcare-lora-7B",
-    "AlpinDale/pygmalion-instruct",
-    "openlm-research/open_llama_13b",
-    "jondurbin/airoboros-7b-gpt4-1.3",
-    "elyza/ELYZA-japanese-Llama-2-7b-fast",
-    "jondurbin/airoboros-gpt-3.5-turbo-100k-7b",
-    "uukuguy/speechless-codellama-orca-13b",
-    "bigcode/starcoderplus",
-    "TheBloke/guanaco-7B-HF",
-    "Neko-Institute-of-Science/metharme-7b",
-    "TigerResearch/tigerbot-7b-base",
-    "golaxy/gogpt-7b",
-    "togethercomputer/LLaMA-2-7B-32K",
-    "yhyhy3/open_llama_7b_v2_med_instruct",
-    "ajibawa-2023/carl-7b",
-    "stabilityai/stablelm-base-alpha-7b-v2",
-    "conceptofmind/LLongMA-2-7b-16k",
-    "TehVenom/Pygmalion_AlpacaLora-7b",
-    "jondurbin/airoboros-7b-gpt4-1.4.1-qlora",
-    "wannaphong/openthaigpt-0.1.0-beta-full-model_for_open_llm_leaderboard",
-    "ausboss/llama7b-wizardlm-unfiltered",
-    "project-baize/baize-v2-7b",
-    "LMFlow/Robin-v2",
-    "HanningZhang/Robin-v2",
-    "LMFlow/Robin-7b-v2",
-    "OptimalScale/robin-7b-v2-delta",
-    "uukuguy/speechless-codellama-platypus-13b",
-    "jerryjalapeno/nart-100k-7b",
-    "wenge-research/yayi-13b-llama2",
-    "fireballoon/baichuan-vicuna-chinese-7b",
-    "jlevin/guanaco-unchained-llama-2-7b",
-    "csitfun/llama-7b-logicot",
-    "DevaMalla/llama7b_alpaca_1gpu_bf16",
-    "WeOpenML/PandaLM-Alpaca-7B-v1",
-    "illuin/test-custom-llama",
-    "yeontaek/WizardCoder-Python-13B-LoRa",
-    "ashercn97/giraffe-7b",
-    "mosaicml/mpt-7b-chat",
-    "abhishek/autotrain-llama-alpaca-peft-52508123785",
-    "Neko-Institute-of-Science/pygmalion-7b",
-    "TFLai/llama-7b-4bit-alpaca",
-    "huggingface/llama-7b",
-    "TheBloke/Planner-7B-fp16",
-    "shibing624/chinese-llama-plus-13b-hf",
-    "AGI-inc/lora_moe_7b_baseline",
-    "DevaMalla/llama-base-7b",
-    "AGI-inc/lora_moe_7b",
-    "togethercomputer/GPT-JT-6B-v0",
-    "ehartford/WizardLM-7B-Uncensored",
-    "shibing624/chinese-alpaca-plus-7b-hf",
-    "beomi/llama-2-ko-7b",
-    "mosaicml/mpt-7b-8k-instruct",
-    "Enno-Ai/ennodata-7b",
-    "mosaicml/mpt-7b-instruct",
-    "facebook/opt-iml-max-30b",
-    "WeOpenML/Alpaca-7B-v1",
-    "TheBloke/Project-Baize-v2-7B-GPTQ",
-    "codellama/CodeLlama-13b-Instruct-hf",
-    "TheBloke/CodeLlama-13B-Instruct-fp16",
-    "facebook/galactica-30b",
-    "FreedomIntelligence/phoenix-inst-chat-7b",
-    "openlm-research/open_llama_7b_v2",
-    "GeorgiaTechResearchInstitute/galpaca-30b",
-    "THUDM/chatglm2-6b",
-    "togethercomputer/GPT-JT-6B-v1",
-    "TheBloke/koala-7B-HF",
-    "nathan0/mpt_delta_tuned_model_v3",
-    "nathan0/mpt_delta_tuned_model_v2",
-    "GeorgiaTechResearchInstitute/galpaca-30b",
-    "JosephusCheung/Guanaco",
-    "shareAI/CodeLLaMA-chat-13b-Chinese",
-    "TigerResearch/tigerbot-7b-sft",
-    "Writer/InstructPalmyra-20b",
-    "OpenAssistant/codellama-13b-oasst-sft-v10",
-    "bigscience/bloomz-7b1-mt",
-    "nathan0/mpt_delta_tuned_model_v3",
-    "VMware/open-llama-7b-open-instruct",
-    "baichuan-inc/Baichuan-7B",
-    "anas-awadalla/mpt-7b",
-    "mosaicml/mpt-7b",
-    "bigscience/bloomz-7b1",
-    "ziqingyang/chinese-llama-2-7b",
-    "OpenAssistant/codellama-13b-oasst-sft-v10",
-    "wenge-research/yayi-7b",
-    "tiiuae/falcon-7b",
-    "togethercomputer/RedPajama-INCITE-Instruct-7B-v0.1",
-    "togethercomputer/RedPajama-INCITE-7B-Instruct",
-    "TheBloke/landmark-attention-llama7b-fp16",
-    "togethercomputer/GPT-JT-Moderation-6B",
-    "h2oai/h2ogpt-gm-oasst1-en-1024-20b",
-    "dvruette/gpt-neox-20b-full-precision",
-    "TehVenom/Moderator-Chan_GPT-JT-6b",
-    "dvruette/oasst-gpt-neox-20b-1000-steps",
-    "AlekseyKorshuk/pygmalion-6b-vicuna-chatml",
-    "facebook/opt-66b",
-    "Salesforce/codegen-16B-nl",
-    "Vmware/open-llama-7b-v2-open-instruct",
-    "mosaicml/mpt-7b-storywriter",
-    "acrastt/Marx-3B-V2",
-    "openlm-research/open_llama_7b",
-    "Fredithefish/ReasonixPajama-3B-HF",
-    "togethercomputer/GPT-NeoXT-Chat-Base-20B",
-    "psmathur/orca_mini_13b",
-    "RWKV/rwkv-raven-14b",
-    "h2oai/h2ogpt-oasst1-512-20b",
-    "acrastt/Marx-3B",
-    "klosax/open_llama_13b_600bt_preview",
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-    "cerebras/Cerebras-GPT-256M",
-    "ai-forever/rugpt3large_based_on_gpt2",
-    "alibidaran/medical_transcription_generator",
-    "Deci/DeciCoder-1b",
-    "microsoft/DialoGPT-medium",
-    "ogimgio/gpt-neo-125m-neurallinguisticpioneers",
-    "open-llm-leaderboard/bloom-560m-4bit-alpaca-auto-eval-adapter-applied",
-    "BreadAi/gpt-YA-1-1_160M",
-    "microsoft/DialoGPT-large",
-    "facebook/opt-125m",
-    "huggingtweets/jerma985",
-    "Locutusque/gpt2-conversational-or-qa",
-    "concedo/Pythia-70M-ChatSalad",
-    "roneneldan/TinyStories-1M",
-    "BreadAi/DiscordPy",
-    "bigcode/gpt_bigcode-santacoder",
-    "Tincando/fiction_story_generator",
-    "klosax/pythia-70m-deduped-step44k-92bt",
-    "Quake24/easyTermsSummerizer",
-    "BreadAi/gpt-YA-1-1_70M",
-    "EleutherAI/pythia-160m",
-    "euclaise/gpt-neox-122m-minipile-digits",
-    "MBZUAI/lamini-cerebras-590m",
-    "nicholasKluge/Aira-124M",
-    "MayaPH/FinOPT-Washington",
-    "cyberagent/open-calm-large",
-    "BreadAi/StoryPy",
-    "EleutherAI/pythia-70m",
-    "BreadAi/gpt-Youtube",
-    "roneneldan/TinyStories-33M",
-    "EleutherAI/pythia-70m-deduped",
-    "lgaalves/gpt2_guanaco-dolly-platypus",
-    "Corianas/Quokka_590m",
-    "lgaalves/gpt2_platypus-dolly-guanaco",
-    "cyberagent/open-calm-7b",
-    "RWKV/rwkv-4-169m-pile",
-    "gpt2",
-    "roneneldan/TinyStories-28M",
-    "lgaalves/gpt2_open-platypus",
-    "gpt2",
-    "SaylorTwift/gpt2_test",
-    "roneneldan/TinyStories-3M",
-    "nthngdy/pythia-owt2-70m-50k",
-    "Corianas/256_5epoch",
-    "roneneldan/TinyStories-8M",
-    "lgaalves/gpt2-dolly",
-    "nthngdy/pythia-owt2-70m-100k",
-    "aisquared/dlite-v2-124m",
-    "mncai/SGPT-1.3B-insurance-epoch10",
-    "huggingtweets/gladosystem",
-    "abhiramtirumala/DialoGPT-sarcastic-medium",
-    "MBZUAI/lamini-cerebras-256m",
-    "cerebras/Cerebras-GPT-111M",
-    "uberkie/metharme-1.3b-finetuned",
-    "MBZUAI/lamini-cerebras-111m",
-    "psyche/kogpt",
-    "Corianas/Quokka_256m",
-    "vicgalle/gpt2-alpaca-gpt4",
-    "aisquared/dlite-v1-124m",
-    "Mikivis/xuanxuan",
-    "MBZUAI/LaMini-GPT-124M",
-    "vicgalle/gpt2-alpaca",
-    "huashiyiqike/testmodel",
-    "Corianas/111m",
-    "baseline",
-]

src/tools/plots.py DELETED Viewed

@@ -1,133 +0,0 @@
-import pandas as pd
-import numpy as np
-import plotly.express as px
-from plotly.graph_objs import Figure
-from src.leaderboard.filter_models import FLAGGED_MODELS
-from src.display.utils import AutoEvalColumn, Tasks, Task, BENCHMARK_COLS
-from src.leaderboard.read_evals import EvalResult
-def create_scores_df(raw_data: list[EvalResult]) -> dict[str, pd.DataFrame]:
-    """
-    Generates a DataFrame containing the maximum scores until each date.
-    :param results_df: A DataFrame containing result information including metric scores and dates.
-    :return: A new DataFrame containing the maximum scores until each date for every metric.
-    """
-    # Step 1: Ensure 'date' is in datetime format and sort the DataFrame by it
-    results_df = pd.DataFrame(raw_data)
-    #results_df["date"] = pd.to_datetime(results_df["date"], format="mixed", utc=True)
-    results_df.sort_values(by="date", inplace=True)
-    # Step 2: Initialize the scores dictionary
-    scores = {k: [] for k in BENCHMARK_COLS + [AutoEvalColumn.average.name]}
-    # Step 3: Iterate over the rows of the DataFrame and update the scores dictionary
-    for task in [t.value for t in Tasks] + [Task("Average", "avg", AutoEvalColumn.average.name)]:
-        current_max = 0
-        last_date = ""
-        column = task.col_name
-        for _, row in results_df.iterrows():
-            current_model = row["full_model"]
-            if current_model in FLAGGED_MODELS:
-                continue
-            current_date = row["date"]
-            if task.benchmark == "Average":
-                current_score = np.mean(list(row["results"].values()))
-            else:
-                current_score = row["results"][task.benchmark]
-            if current_score > current_max:
-                if current_date == last_date and len(scores[column]) > 0:
-                    scores[column][-1] = {"model": current_model, "date": current_date, "score": current_score}
-                else:
-                    scores[column].append({"model": current_model, "date": current_date, "score": current_score})
-                current_max = current_score
-                last_date = current_date
-    # Step 4: Return all dictionaries as DataFrames
-    return {k: pd.DataFrame(v) for k, v in scores.items()}
-def create_plot_df(scores_df: dict[str: pd.DataFrame]) -> pd.DataFrame:
-    """
-    Transforms the scores DataFrame into a new format suitable for plotting.
-    :param scores_df: A DataFrame containing metric scores and dates.
-    :return: A new DataFrame reshaped for plotting purposes.
-    """
-    # Initialize the list to store DataFrames
-    dfs = []
-    # Iterate over the cols and create a new DataFrame for each column
-    for col in BENCHMARK_COLS + [AutoEvalColumn.average.name]:
-        d = scores_df[col].reset_index(drop=True)
-        d["task"] = col
-        dfs.append(d)
-    # Concatenate all the created DataFrames
-    concat_df = pd.concat(dfs, ignore_index=True)
-    # Sort values by 'date'
-    concat_df.sort_values(by="date", inplace=True)
-    concat_df.reset_index(drop=True, inplace=True)
-    return concat_df
-def create_metric_plot_obj(df: pd.DataFrame, metrics: list[str], title: str) -> Figure:
-    """
-    Create a Plotly figure object with lines representing different metrics
-    and horizontal dotted lines representing human baselines.
-    :param df: The DataFrame containing the metric values, names, and dates.
-    :param metrics: A list of strings representing the names of the metrics
-                    to be included in the plot.
-    :param title: A string representing the title of the plot.
-    :return: A Plotly figure object with lines representing metrics and
-             horizontal dotted lines representing human baselines.
-    """
-    # Filter the DataFrame based on the specified metrics
-    df = df[df["task"].isin(metrics)]
-    # Create a line figure using plotly express with specified markers and custom data
-    fig = px.line(
-        df,
-        x="date",
-        y="score",
-        color="task",
-        markers=True,
-        custom_data=["task", "score", "model"],
-        title=title,
-    )
-    # Update hovertemplate for better hover interaction experience
-    fig.update_traces(
-        hovertemplate="<br>".join(
-            [
-                "Model Name: %{customdata[2]}",
-                "Metric Name: %{customdata[0]}",
-                "Date: %{x}",
-                "Metric Value: %{y}",
-            ]
-        )
-    )
-    # Update the range of the y-axis
-    fig.update_layout(yaxis_range=[0, 100])
-    # Create a dictionary to hold the color mapping for each metric
-    metric_color_mapping = {}
-    # Map each metric name to its color in the figure
-    for trace in fig.data:
-        metric_color_mapping[trace.name] = trace.line.color
-    return fig
-# Example Usage:
-# human_baselines dictionary is defined.
-# chart = create_metric_plot_obj(scores_df, ["ARC", "HellaSwag", "MMLU", "TruthfulQA"], human_baselines, "Graph Title")

src/utils.py ADDED Viewed

	@@ -0,0 +1,12 @@

+from huggingface_hub import snapshot_download
+def my_snapshot_download(repo_id, revision, local_dir, repo_type, max_workers):
+    for i in range(10):
+        try:
+            snapshot_download(repo_id=repo_id, revision=revision, local_dir=local_dir, repo_type=repo_type, max_workers=max_workers)
+            return
+        except Exception:
+            import time
+            time.sleep(60)
+    return