MEMORY-EFFICIENT LARGE LANGUAGE MODEL
Systems and techniques train a first large language model (LLM) using domain data to output a second LLM, the second LLM having a first memory size. The systems and techniques generate predictions by both the first LLM and the second LLM using a same input data for generating the predictions. The systems and techniques compute a threshold by comparing the predictions by the first LLM and the second LLM. The systems and techniques iteratively adjusting a set of attention heads of the second LLM by comparing new predictions from the second LLM to the threshold using the same input data. The systems and techniques generate a third LLM having a different set of attention heads than the set of attention heads of the second LLM, the third LLM having a second memory size that is smaller than the first memory size of the second LLM.
1 . A computer-implemented method comprising:
training a first large language model (LLM) using domain data to output a second LLM, the second LLM having a first memory size;
generating predictions by both the first LLM and the second LLM using a same input data for generating the predictions;
computing a threshold by comparing the predictions by the first LLM and the second LLM;
iteratively adjusting a set of attention heads of the second LLM by comparing new predictions from the second LLM to the threshold using the same input data; and
generating a third LLM having a different set of attention heads than the set of attention heads of the second LLM, the third LLM having a second memory size that is smaller than the first memory size of the second LLM.
2 . The computer-implemented method of claim 1 , wherein the third LLM and the second LLM have a similar accuracy.
3 . The computer-implemented method of claim 1 , further comprising:
generating additional predictions for both the second LLM and the third LLM using new data; and
comparing the additional predictions for both the second LLM and the third LLM, wherein an accuracy of the additional predictions for the third LLM is similar to an accuracy of the additional predictions for the second LLM.
4 . The computer-implemented method of claim 1 , wherein iteratively adjusting the set of attention heads of the second LLM includes:
temporarily removing at least one attention head from the set of attention heads of the second LLM;
calculating a reward function with the at least one attention head removed; and
permanently removing the at least one attention head from the set of attention heads based on the reward function.
5 . The computer-implemented method of claim 4 , further comprising:
retaining the at least one attention head in the set of attention heads when the reward function indicates a loss of accuracy in the new predictions in the second LLM when compared to the threshold.
6 . The computer-implemented method of claim 4 , wherein permanently removing the at least one attention head includes permanently removing the at least one attention head when the reward function indicates no loss of accuracy in the new predictions in the second LLM when compared to the threshold.
7 . The computer-implemented method of claim 4 , wherein temporarily removing the at least one attention head from the set of attention heads of the second LLM includes randomly selecting the at least one attention head for temporary removal.
8 . The computer-implemented method of claim 4 , wherein temporarily removing the at least one attention head from the set of attention heads of the second LLM includes selecting the at least one attention head for temporary removal using an activation score.
9 . The computer-implemented method of claim 1 , further comprising stopping iteratively adjusting the set of attention heads of the second LLM in response to the new predictions being below the threshold.
10 . A computer program product, the computer program product being tangibly embodied on a non-transitory computer-readable medium and comprising instructions that, when executed by at least one computing device, are configured to cause the at least one computing device to:
train a first large language model (LLM) using domain data to output a second LLM, the second LLM having a first memory size;
generate predictions by both the first LLM and the second LLM using a same input data for generating the predictions;
compute a threshold by comparing the predictions by the first LLM and the second LLM;
iteratively adjust a set of attention heads of the second LLM by comparing new predictions from the second LLM to the threshold using the same input data; and
generate a third LLM having a different set of attention heads than the set of attention heads of the second LLM, the third LLM having a second memory size that is smaller than the first memory size of the second LLM.
11 . The computer program product of claim 10 , wherein the third LLM and the second LLM have a similar accuracy.
12 . The computer program product of claim 10 , wherein the instructions, when executed, are further configured to cause the at least one computing device to:
generate additional predictions for both the second LLM and the third LLM using new data; and
compare the additional predictions for both the second LLM and the third LLM, wherein an accuracy of the additional predictions for the third LLM is similar to an accuracy of the additional predictions for the second LLM.
13 . The computer program product of claim 10 , wherein iteratively adjusting the set of attention heads of the second LLM includes instructions that, when executed, are further configured to cause the at least one computing device to:
temporarily remove at least one attention head from the set of attention heads of the second LLM;
calculate a reward function with the at least one attention head removed; and
permanently remove the at least one attention head from the set of attention heads based on the reward function.
14 . The computer program product of claim 13 , wherein the instructions, when executed, are further configured to cause the at least one computing device to:
retain the at least one attention head in the set of attention heads when the reward function indicates a loss of accuracy in the new predictions in the second LLM when compared to the threshold.
15 . The computer program product of claim 13 , wherein permanently removing the at least one attention head includes instructions that, when executed, are further configured to cause the at least one computing device to:
permanently remove the at least one attention head when the reward function indicates no loss of accuracy in the new predictions in the second LLM when compared to the threshold.
16 . The computer program product of claim 13 , wherein temporarily removing the at least one attention head from the set of attention heads of the second LLM includes instructions that, when executed, are further configured to cause the at least one computing device to:
randomly select the at least one attention head for temporary removal.
17 . The computer program product of claim 13 , wherein temporarily removing the at least one attention head from the set of attention heads of the second LLM includes instructions that, when executed, are further configured to cause the at least one computing device to:
select the at least one attention head for temporary removal using an activation score.
18 . The computer program product of claim 10 , wherein the instructions, when executed, are further configured to cause the at least one computing device to stop iteratively adjusting the set of attention heads of the second LLM in response to the new predictions being below the threshold.
19 . A system comprising:
at least one memory including instructions; and
at least one processor that is operably coupled to the at least one memory and that is arranged and configured to execute instructions that, when executed, cause the at least one processor to:
train a first large language model (LLM) using domain data to output a second LLM, the second LLM having a first memory size;
generate predictions by both the first LLM and the second LLM using a same input data for generating the predictions;
compute a threshold by comparing the predictions by the first LLM and the second LLM;
iteratively adjust a set of attention heads of the second LLM by comparing new predictions from the second LLM to the threshold using the same input data; and
generate a third LLM having a different set of attention heads than the set of attention heads of the second LLM, the third LLM having a second memory size that is smaller than the first memory size of the second LLM.
20 . The system of claim 19 , wherein iteratively adjusting the set of attention heads of the second LLM includes instructions that, when executed, are further configured to cause the at least one processor to:
temporarily remove at least one attention head from the set of attention heads of the second LLM;
calculate a reward function with the at least one attention head removed; and
permanently remove the at least one attention head from the set of attention heads based on the reward function.
21 . The system of claim 19 , wherein the at least one processor includes at least one graphics processing unit (GPU) and the third LLM uses fewer processing resources of the at least one GPU than the second LLM.