# US 2026/0073218 A1 · Method and system for creating pruned large language models

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- Number: US 2026/0073218 A1
- Document ID: US20260073218A1
- Status: Published application
- Kind: A1
- Application number: 18/945,633
- Filed: 2024-11-13
- Published: 2026-03-12
- Earliest claimed: 2024-09-12
- Assignees: L&T Technology Services Limited
- Applicants: L&T Technology Services Limited
- Inventors: SUDHIR BHADAURIA; VIKRAM SUBRAMANI
- Web page: https://patentgrep.com/patent/US20260073218A1

## CPC

- G06N 3/082: modifying the architecture, e.g. adding, deleting or silencing nodes or connections
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A method and a system for creating a pruned large language model (LLM) is disclosed. A processor receives a weight file of a pretrained LLM, a first metadata, a predefined pruning criterion, and a predefined pruning ratio. A set of target layers are identified. At least one weight is determined from each of the set of target layers based on the predefined pruning criterion and the predefined pruning ratio. A position index of the at least one weight corresponding to each of the set of target layers is determined based on the set of weight matrices. A compressed weight file and a second metadata are generated by removing the at least one weight from each of the set of target layers based on the position index. The pruned LLM is created using the compressed weight file and the second metadata.

## Claims

### Claim 1

A method of creating a pruned large language model (LLM), the method comprising:
receiving, by a processor, a weight file of a pretrained LLM, a first metadata, a predefined pruning criterion, and a predefined pruning ratio,
wherein the first metadata comprises architecture information of the pretrained LLM,
wherein the pretrained LLM comprises a plurality of layers, and
wherein the weight file comprises a set of weight matrices each representing learned parameters of a corresponding layer from the plurality of layers;
identifying, by the processor, a set of target layers to be pruned from the plurality of layers based on the set of weight matrices;
determining, by the processor, at least one weight to be removed from each of the set of target layers based on the predefined pruning criterion and the predefined pruning ratio;
determining, by the processor, a position index of the at least one weight corresponding to each of the set of target layers based on the set of weight matrices;
generating, by the processor, a compressed weight file and a second metadata by removing the at least one weight from each of the set of target layers based on the position index; and
creating, by the processor, the pruned LLM corresponding to the pretrained LLM model using the compressed weight file and the second metadata.

### Claim 2 (depends on 1)

The method of claim 1, comprising:
determining, by the processor, the set of weight matrices from the weight file by parsing the weight file.

### Claim 3 (depends on 1)

The method of claim 1, wherein the predefined pruning criterion is selected from a set of predefined pruning criteria comprising a magnitude-based criteria, a geometric median-based criteria, a distance-based criteria, and a gradient-based criteria.

### Claim 4 (depends on 1)

The method of claim 1, wherein the position index is determined by determining a rank of importance of the at least one weight in each of the set of target layers based on the set of weight matrices.

### Claim 5 (depends on 1)

The method of claim 1, wherein the second metadata comprises architecture information of the pruned LLM, and dimension information of the pruned LLM.

### Claim 6

A system for creating a pruned large language model (LLM), comprising:
a processor;
a memory communicably coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to:
receive a weight file of a pretrained LLM, a first metadata, a predefined pruning criterion, and a predefined pruning ratio,
wherein the first metadata comprises architecture information of the pretrained LLM,
wherein the pretrained LLM comprises a plurality of layers, and
wherein the weight file comprises a set of weight matrices each representing learned parameters of a corresponding layer from the plurality of layers;
identify a set of target layers to be pruned from the plurality of layers based on the set of weight matrices;
determine at least one weight to be removed from each of the set of target layers based on the predefined pruning criterion and the predefined pruning ratio;
determine a position index of the at least one weight corresponding to each of the set of target layers based on the set of weight matrices;
generate a compressed weight file and a second metadata by removing the at least one weight from each of the set of target layers based on the position index; and
create the pruned LLM corresponding to the pretrained LLM using the compressed weight file and the second metadata.

### Claim 7 (depends on 6)

The system of claim 6, wherein processor-executable instructions cause the processor to:
determine the set of weight matrices from the weight file by parsing the weight file.

### Claim 8 (depends on 6)

The system of claim 6, wherein the predefined pruning criterion is selected from a set of predefined pruning criteria comprising a magnitude-based criteria, a geometric median-based criteria, a distance-based criteria, and a gradient-based criteria.

### Claim 9 (depends on 6)

The system of claim 6, wherein the position index is determined by determining a rank of importance of the at least one weight in each of the set of target layers based on the set of weight matrices.

### Claim 10 (depends on 1)

The system of claim 1, wherein the second metadata comprises architecture information of the pruned LLM, and dimension information of the pruned LLM.

### Claim 11

A non-transitory computer-readable medium storing computer-executable instructions for creating a pruned large language model (LLM), the computer-executable instructions configured for:
receiving a weight file of a pretrained LLM, a first metadata, a predefined pruning criterion, and a predefined pruning ratio;
wherein the first metadata comprises architecture information of the pretrained LLM, and
wherein the pretrained LLM comprises a plurality of layers, and
wherein the weight file comprises a set of weight matrices each representing learned parameters of a corresponding layer from the plurality of layers;
identifying a set of target layers to be pruned from the plurality of layers based on the set of weight matrices;
determining at least one weight to be removed from each of the set of target layers based on the predefined pruning criterion and the predefined pruning ratio;
determining a position index of the at least one weight corresponding to each of the set of target layers based on the set of weight matrices;
generating a compressed weight file and a second metadata by removing the at least one weight from each of the set of target layers based on the position index; and
creating the pruned LLM corresponding to the pretrained LLM model using the compressed weight file and the second metadata.

### Claim 12 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the computer-executable instructions are further configured for:
determining the set of weight matrices from the weight file by parsing the weight file.

### Claim 13 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the predefined pruning criterion is selected from a set of predefined pruning criteria comprising a magnitude-based criteria, a geometric median-based criteria, a distance-based criteria, and a gradient-based criteria.

### Claim 14 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the position index is determined by determining a rank of importance of the at least one weight in each of the set of target layers based on the set of weight matrices.

### Claim 15 (depends on 11)

The non-transitory computer-readable medium of claim 11, wherein the second metadata comprises architecture information of the pruned LLM, and dimension information of the pruned LLM.