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import pandas as pd
from app.models import Architecture, Estimate, Metadata, Tokenizer
from app.utils import abbreviate_number, human_readable_size
def get_model_info_df(
metadata: Metadata, architecture: Architecture, tokenizer: Tokenizer
):
return pd.DataFrame(
[
{
"Type": metadata.type_,
"Name": metadata.name,
"Architecture": metadata.architecture,
"File Size": human_readable_size(metadata.file_size),
"Parameters": abbreviate_number(metadata.parameters),
"Bits Per Weight": round(metadata.bits_per_weight, 2),
"Maximum Context Length": architecture.maximum_context_length,
"Vocabulary Length": architecture.vocabulary_length,
"Tokenizer Model": tokenizer.model,
"Tokens Size": human_readable_size(tokenizer.tokens_size),
}
]
)
def get_estimate_df(estimate: Estimate):
return pd.DataFrame(
[
{
"Context Size": estimate.context_size,
"Flash Attention": estimate.flash_attention,
"Logical Batch Size": estimate.logical_batch_size,
"Physical Batch Size": estimate.physical_batch_size,
}
]
)
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