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Update scrape-leaderboard.py
Browse files- scrape-leaderboard.py +117 -6
scrape-leaderboard.py
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import requests
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from bs4 import BeautifulSoup
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benchmark_data = [
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{
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"rank": 44,
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import requests
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from bs4 import BeautifulSoup
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#!/usr/bin/env python3
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def main():
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print("""### INSTRUCTION ###
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Read the instructions below:
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1. You will be presented with benchmark scores by various LLM's.
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2. The layout of the data presented to you is as follows:
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>>> START LAYOUT EXAMPLE <<<
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--- (start of a new model marker)
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Model ranking
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Model name
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Model average score across benchmarks in %
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Models average score on IFEval benchmarks in %
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Models average score on BBH benchmarks in %
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Models average score on MATH benchmarks in %
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Models average score in GPQA benchmarks in %
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Models average score in MUSR benchmarks in %
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Models average score in MMLU-PRO benchmarks in %
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### (start of YAML-configuration marker)
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The YAML-configuration file that was used to create the model in mergekit.
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Note that this part is only available for certain models and not for all models on the list!
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The configuration starts and ends with '###'
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### (end of YAML-configuration marker)
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>>> END LAYOUT EXAMPLE <<<
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For example, the following input could be possible:
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>>> START INPUT EXAMPLE <<<
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---
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44
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sometimesanotion/Qwen2.5-14B-Vimarckoso-v3
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40.10 %
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72.57 %
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48.58 %
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34.44 %
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17.34 %
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19.39 %
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48.26 %
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###
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models:
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- model: CultriX/SeQwence-14Bv1
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- model: allknowingroger/Qwenslerp5-14B
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merge_method: slerp
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base_model: CultriX/SeQwence-14Bv1
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dtype: bfloat16
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parameters:
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t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
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###
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---
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45
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sthenno-com/miscii-14b-1225
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40.08 %
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78.78 %
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50.91 %
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31.57 %
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17.00 %
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14.77 %
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47.46 %
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---
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>>> END INPUT EXAMPLE <<<
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>>> START INTERPRETATION OF INPUT EXAMPLE <<<
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---
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Model Rank: 44
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Model Name: sometimesanotion/Qwen2.5-14B-Vimarckoso-v3
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Model average score across benchmarks in %: 40.10
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Models average score on IFEval benchmarks in %: 72.57
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Models average score on BBH benchmarks in %: 48.58
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Models average score on MATH benchmarks in % 34.44
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Models average score in GPQA benchmarks in % 17.34
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Models average score in MUSR benchmarks in % 19.39
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Models average score in MMLU-PRO benchmarks in % 48.26
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### (THE CONFIGURATION FOR MERGING THE MODEL: sometimesanotion/Qwen2.5-14B-Vimarckoso-v3 was found)
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models:
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- model: CultriX/SeQwence-14Bv1
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- model: allknowingroger/Qwenslerp5-14B
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merge_method: slerp
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base_model: CultriX/SeQwence-14Bv1
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dtype: bfloat16
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parameters:
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t: [0, 0.5, 1, 0.5, 0] # V shaped curve: Hermes for input & output, WizardMath in the middle layers
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###
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---
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Model Rank: 45
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Model Name: sthenno-com/miscii-14b-1225
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Model average score across benchmarks in %: 40.08
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Models average score on IFEval benchmarks in %: 78.78
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Models average score on BBH benchmarks in %: 50.91
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Models average score on MATH benchmarks in % 31.57
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Models average score in GPQA benchmarks in % 17.00
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Models average score in MUSR benchmarks in % 14.77
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Models average score in MMLU-PRO benchmarks in % 47.46
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###
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THE MERGEKIT CONFIGURATION FOR MODEL sthenno-com/miscii-14b-1225 WAS NOT FOUND SO IT IS SKIPPED
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###
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--- (next model etc...)
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>>> END INTERPRETATION OF INPUT EXAMPLE <<<
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4. >>> INSTRUCTIONS <<<
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Below follows the scraped data from the leaderboard.
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>>> DATA START <<<
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"""
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benchmark_data = [
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{
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"rank": 44,
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