File size: 1,654 Bytes
c9c557d 95bea38 0aba987 b09d01b 63362b3 c9c557d b09d01b c79c66f b09d01b 0145ad5 b09d01b 95bea38 9a929f5 c9c557d c79c66f b09d01b 9a929f5 95bea38 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
from smolagents import Tool
class FrugalAI_methods(Tool):
name = "FrugalAI_methods"
description = "Retrieves methods for model frugalization. It will return ideas to frugalize a code, please use it."
inputs = {
"method": {
"type": "string",
"description": "Always set it to 'Frugal' or it will raise an error."
}
}
output_type = "string"
def forward(self, method):
ideas=[]
ideas.append(pruning())
ideas.append(quantization())
return ideas
def pruning():
"""
Optimizes models by removing unnecessary components, such as certain weights in a neural network.
This function demonstrates how to apply pruning.
"""
code = "model = apply_pruning(model, amount=0.3)"
return (
f"To apply pruning to a model, use the following code snippet: {code}. "
f"You should adapt it to your actual implementation. In particular, the 'amount' parameter "
f"can be increased or decreased depending on the initial number of weights and the complexity of your use case (minimu value: 0, maximum value: 1). You should give these information to the manager, you can not prune the model yourself."
)
def quantization():
"""
Converts high-precision weights into lower-precision one to reduce cost.
"""
code = "model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)"
return (
f"To apply quantization to a model, use the following code snippet: {code}. You should give these information to the manager, you can not quantize the model yourself."
) |