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import pandas as pd
# Define the data
data = {
"Original Name" : [],
"Proper Display Name": [],
"Link" : [],
"Model Size" : [],
}
# Add model information to the
data['Original Name'].append('gemma-2-2b-it')
data['Proper Display Name'].append('Gemma-2-2B-IT')
data['Link'].append('https://huggingface.co/google/gemma-2-2b-it')
data['Model Size'].append('2')
data['Original Name'].append('gemma-2-9b-it')
data['Proper Display Name'].append('Gemma-2-9B-IT')
data['Link'].append('https://huggingface.co/google/gemma-2-9b-it')
data['Model Size'].append('9')
data['Original Name'].append('GPT4o_0513')
data['Proper Display Name'].append('GPT4o-0513')
data['Link'].append('https://openai.com/index/hello-gpt-4o/')
data['Model Size'].append('99999')
data['Original Name'].append('llama3-8b-cpt-sea-lionv2-base')
data['Proper Display Name'].append('Llama3-8B-CPT-SEA-Lion-v2-Base')
data['Link'].append('https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2-base')
data['Model Size'].append('8')
data['Original Name'].append('llama3-8b-cpt-sea-lionv2-instruct')
data['Proper Display Name'].append('Llama3-8B-CPT-SEA-Lion-v2-Instruct')
data['Link'].append('https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct')
data['Model Size'].append('8')
data['Original Name'].append('Meta-Llama-3-8B')
data['Proper Display Name'].append('Meta-Llama-3-8B')
data['Link'].append('https://ai.meta.com/blog/meta-llama-3/')
data['Model Size'].append('8')
data['Original Name'].append('Meta-Llama-3-8B-Instruct')
data['Proper Display Name'].append('Meta-Llama-3-8B-Instruct')
data['Link'].append('https://ai.meta.com/blog/meta-llama-3/')
data['Model Size'].append('8')
data['Original Name'].append('Meta-Llama-3-70B-Instruct')
data['Proper Display Name'].append('Meta-Llama-3-70B-Instruct')
data['Link'].append('https://ai.meta.com/blog/meta-llama-3/')
data['Model Size'].append('70')
data['Original Name'].append('Meta-Llama-3.1-8B')
data['Proper Display Name'].append('Meta-Llama-3.1-8B')
data['Link'].append('https://ai.meta.com/blog/meta-llama-3-1/')
data['Model Size'].append('8')
data['Original Name'].append('Meta-Llama-3.1-8B-Instruct')
data['Proper Display Name'].append('Meta-Llama-3.1-8B-Instruct')
data['Link'].append('https://ai.meta.com/blog/meta-llama-3-1/')
data['Model Size'].append('8')
data['Original Name'].append('Meta-Llama-3.1-70B')
data['Proper Display Name'].append('Meta-Llama-3.1-70B')
data['Link'].append('https://ai.meta.com/blog/meta-llama-3-1/')
data['Model Size'].append('70')
data['Original Name'].append('Meta-Llama-3.1-70B-Instruct')
data['Proper Display Name'].append('Meta-Llama-3.1-70B-Instruct')
data['Link'].append('https://ai.meta.com/blog/meta-llama-3-1/')
data['Model Size'].append('70')
data['Original Name'].append('Qwen2_5_0_5B_Instruct')
data['Proper Display Name'].append('Qwen2.5-0.5B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct')
data['Model Size'].append('0.5')
data['Original Name'].append('Qwen2_5_1_5B_Instruct')
data['Proper Display Name'].append('Qwen2.5-1.5B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct')
data['Model Size'].append('1.5')
data['Original Name'].append('Qwen2_5_3B_Instruct')
data['Proper Display Name'].append('Qwen2.5-3B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2.5-3B-Instruct')
data['Model Size'].append('3')
data['Original Name'].append('Qwen2_5_7B_Instruct')
data['Proper Display Name'].append('Qwen2.5-7B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2.5-7B-Instruct')
data['Model Size'].append('7')
data['Original Name'].append('Qwen2_5_14B_Instruct')
data['Proper Display Name'].append('Qwen2.5-14B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2.5-14B-Instruct')
data['Model Size'].append('14')
data['Original Name'].append('Qwen2_5_32B_Instruct')
data['Proper Display Name'].append('Qwen2.5-32B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2.5-32B-Instruct')
data['Model Size'].append('32')
data['Original Name'].append('Qwen2_5_72B_Instruct')
data['Proper Display Name'].append('Qwen2.5-72B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2.5-72B-Instruct')
data['Model Size'].append('72')
data['Original Name'].append('Qwen2-7B-Instruct')
data['Proper Display Name'].append('Qwen2-7B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2-7B-Instruct')
data['Model Size'].append('7')
data['Original Name'].append('Qwen2-72B-Instruct')
data['Proper Display Name'].append('Qwen2-72B-Instruct')
data['Link'].append('https://huggingface.co/Qwen/Qwen2-72B-Instruct')
data['Model Size'].append('72')
data['Original Name'].append('SeaLLMs-v3-7B-Chat')
data['Proper Display Name'].append('SeaLLMs-v3-7B-Chat')
data['Link'].append('https://arxiv.org/abs/2407.19672')
data['Model Size'].append('7')
data['Original Name'].append('gemma2-9b-cpt-sea-lionv3-instruct')
data['Proper Display Name'].append('Gemma2-9B-CPT-SEA-Lion-v3-Instruct')
data['Link'].append('https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-instruct')
data['Model Size'].append('9')
data['Original Name'].append('llama3-8b-cpt-sea-lionv2.1-instruct')
data['Proper Display Name'].append('Llama3-8B-CPT-SEA-LION-v2.1-Instruct')
data['Link'].append('https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct')
data['Model Size'].append('8')
data['Original Name'].append('Gemma-2-9b-it-sg-ultrachat-sft')
data['Proper Display Name'].append('Gemma-2-9B-IT-SG-Ultrachat-SFT')
data['Link'].append('https://huggingface.co/google/gemma-2-9b-it-sg-ultrachat-sft')
data['Model Size'].append('9')
data['Original Name'].append('meralion-merged-llama3-8b-sg-inst-avg-diff')
data['Proper Display Name'].append('Llama-3-MERaLiON-8B-Instruct')
data['Link'].append('https://huggingface.co/MERaLiON/LLaMA-3-MERaLiON-8B-Instruct')
data['Model Size'].append('8')
data['Original Name'].append('Sailor2-8B-Chat')
data['Proper Display Name'].append('Sailor2-8B-Chat')
data['Link'].append('https://huggingface.co/sail/Sailor2-8B-Chat')
data['Model Size'].append('8')
data['Original Name'].append('llama3.1-70b-cpt-sea-lionv3-instruct')
data['Proper Display Name'].append('Llama3.1-70B-CPT-SEA-LION-v3-Instruct')
data['Link'].append('https://huggingface.co/aisingapore/llama3.1-70b-cpt-sea-lionv3-instruct')
data['Model Size'].append('8')
def get_dataframe():
"""
Returns a DataFrame with the data and drops rows with missing values.
"""
df = pd.DataFrame(data)
return df.dropna(axis=0)
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