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betamark

An experimental benchmark meant for portability and finite compute.

Installation Instructions

pip install git+https://github.com/exobyte-labs/betamark.git

To update the installation:

pip install git+https://github.com/exobyte-labs/betamark.git -U

For HackOS 1, the current code base uses train or dev datasets. For the final benchmarking near demo time, this repository will be updated to use validation datasets.

External Datasets of Interest:

ARC-AGI

from betamark import arc_agi
def placeholder(x):
 """
 Params:
 -------
 x: dict of ARC AGI input

 Returns:
 --------
 y_pred: list, a n x n matrix of the predicted answer
 """
 return [[0,0],[0,0]]
results = arc_agi.run_eval(user_func=placeholder)

TinyMMLU Eval

from betamark import tiny_mmlu
def placeholder(x):
 """
 Params:
 -------
 x: string that is a text prompt

 Returns:
 --------
 y_pred: str, ideally a single character {A, B, C, D} corresponding to a multiple choice answer from MMLU or tinyMMLU
 """
 return "A"
tiny_mmlu.run_eval(user_func=placeholder)

OCR Binary Classification

from betamark import ocr
def placeholder(x):
 """
 Params:
 -------
 x: string representing a genomic sequence

 Returns:
 --------
 y_pred: int where 0 is negative (not an OCR) or 1 (is an OCR)
 """
 return 0
ocr.run_eval(user_func=placeholder)

Bike Detection on MS Coco Subsample

from betamark import bicycle
def placeholder(x):
 """
 Params:
 -------
 x: NumPy array representation of an image (dimensions are non-fixed)

 Returns:
 --------
 y_pred: int where 0 is negative (no bicycle) or 1 (there is a bicycle)
 """
 return 0
bicycle.run_eval(user_func=placeholder)

Example of Running Validation Code

When the validation code is pushed at around 2:00 PM ET (Saturday, Oct. 26), please do the following:

(1) Run the following command to update betamark

pip install git+https://github.com/exobyte-labs/betamark.git -U

(2) Replace run_eval with run_validation, for example, the bicycle case becomes:

bicycle.run_validation(user_func=placeholder)

The function static types are the same, so you don't need to worry about integration. The same replacement applies for ocr and tiny_mmlu.

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An experimental lightweight benchmarking tool

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