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https://github.com/facebookresearch/MobileLLM

MobileLLM Optimizing Sub-billion Parameter Language Models for On-Device Use Cases. In ICML 2024.
https://github.com/facebookresearch/MobileLLM

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MobileLLM Optimizing Sub-billion Parameter Language Models for On-Device Use Cases. In ICML 2024.

Host: GitHub
URL: https://github.com/facebookresearch/MobileLLM
Owner: facebookresearch
License: other
Created: 2024-07-09T00:19:08.000Z (4 months ago)
Default Branch: main
Last Pushed: 2024-07-19T17:39:58.000Z (4 months ago)
Last Synced: 2024-07-19T22:49:38.301Z (4 months ago)
Language: Python
Homepage:
Size: 352 KB
Stars: 775
Watchers: 20
Forks: 38
Open Issues: 3
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

README

        # MobileLLM

This repository contains the training code of MobileLLM introduced in our work: "[MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases](https://arxiv.org/abs/2402.14905)", published in ICML 2024.

In this work, we comprehensively consider multiple design factors to obtain high-quality LLMs with fewer than a billion parameters. We integrated (1) SwiGLU activation function, (2) deep and thin architectures, (3) embedding sharing, (4) grouped-query attention to build MobileLLM. MobileLLM-125M/350M attains a remarkable 2.7%/4.3% accuracy boost over preceding 125M/350M SoTA models on zero-shot commonsense reasoning tasks. In our updated version, we further demonstrate that our design philosophy scales effectively to larger models, with SoTA results for MobileLLM-600M/1B/1.5B.







## Citation

If you find our code useful for your research, please consider citing:

    

    @article{liu2024mobilellm,

        title={MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases},

        author={Liu, Zechun and Zhao, Changsheng and Iandola, Forrest and Lai, Chen and Tian, Yuandong and Fedorov, Igor and Xiong, Yunyang and Chang, Ernie and Shi, Yangyang and Krishnamoorthi, Raghuraman and others},

        journal={arXiv preprint arXiv:2402.14905},

        year={2024}

    }

    

## Run

### Step 1. Requirements:

* python 3.9, pytorch >= 2.0

* pip install -r requirement.txt

   

### Step 2. Data preprocessing

Dividing a tokenized dataset or tokenize your own dataset, and even distribute it across the total number of training nodes, where each node comprises 1x8 GPUs. Next, organize the data into the following structure: 

- basepath

  - 1

    - xxx.jsonl

  - 2

    - xxx.jsonl

  - ...

  - #nodes

    - xxx.jsonl

Each line of a jsonl file is a key-value pair of tokenized data {"token_ids": [1,2,3,4,...]}. 

Our training code is compatible with the data pre-processing method in https://github.com/LLM360/amber-data-prep.

### Step 3. Training script

The script `pretrain.sh` is provided to initiate training on a 1x8 node setup using torchrun. This script can be modified to adjust the `--nnodes` parameter and other settings to suit different multi-node configurations, such as those using slurm or torchx. The learning rate in the script is for 1x8 node with a batch size of 32. If you increase the number of nodes or the batch size, you need to increase the learning rate linearly.

Steps to run:

* In `pretrain.sh` file, specify the  `--train_data_local_path` to the pre-processed data in Step 2 and `--input_model_filename` to `./configs/{model_size}/`.

* Run `bash pretrain.sh `

### Others

The model weights is still under legal review. If you have any questions, feel free to email (zechunliu at meta dot com) and (cszhao at meta dot com)

## Training cost 

It takes the following number of days to train MobileLLM on 1T tokens using 32 NVIDIA A100 80G GPUs.

| 125M | 350M | 600M | 1B | 1.5B | 

| --- | --- | --- | --- | --- |

| ~3 days| ~6 days| ~8 days | ~12 days | ~18 days |

## Results on Zero-shot Common Sense Reasoning tasks

### MobileLLM-125M

| model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |

| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| OPT-125M | 41.3 | 25.2 | 57.5 | 62.0 | 41.9 | 31.1 | 31.2 | 50.8 | 42.6 |

| GPT-neo-125M | 40.7 | 24.8 | 61.3 | 62.5 | 41.9 | 29.7 | 31.6 | 50.7 | 42.9 |

| Pythia-160M | 40.0 | 25.3 | 59.5 | 62.0 | 41.5 | 29.9 | 31.2 | 50.9 | 42.5 |

| **MobileLLM-125M** | 43.9 | 27.1 | 60.2 | 65.3 | 42.4 | 38.9 | 39.5 | 53.1 | **46.3** |

| **MobileLLM-LS-125M** | 45.8 | 28.7 | 60.4 | 65.7 | 42.9 | 39.5 | 41.1 | 52.1 | **47.0** |

### MobileLLM-350M

| model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |

| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| OPT-350M | 41.9 | 25.7 | 54.0 | 64.8 | 42.6 | 36.2 | 33.3 | 52.4 | 43.9 |

| Pythia-410M | 47.1 | 30.3 | 55.3 | 67.2 | 43.1 | 40.1 | 36.2 | 53.4 | 46.6 |

| **MobileLLM-350M** | 53.8 | 33.5 | 62.4 | 68.6 | 44.7 | 49.6 | 40.0 | 57.6 | **51.3** |

| **MobileLLM-LS-350M** | 54.4 | 32.5 | 62.8 | 69.8 | 44.1 | 50.6 | 45.8 | 57.2 | **52.1** | 

### MobileLLM-600M

| model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |

| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| Qwen1.5-500M | 54.7 | 32.1 | 46.9 | 68.9 | 46.0 |  48.8 | 37.7 | 55.0 | 48.8 | 

| BLOOM-560M | 43.7 | 27.5 | 53.7 | 65.1 | 42.5 | 36.5 | 32.6 | 52.2 | 44.2 | 

| MobiLlama-800M | 52.0 | 31.7 | 54.6 | 73.0 |  43.3 | 52.3 | 42.5 | 56.3 | 50.7 | 

| **MobileLLM-600M** | 58.1 |  35.8 |  61.0 |  72.3 | 44.9 | 55.9 |  47.9 |  58.6 | **54.3** |  

### MobileLLM-1B

| model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |

| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| Pythia-1B | 49.9 | 30.4 | 58.7 | 69.2 | 43.3 | 47.4 | 38.6 | 52.2 | 48.7 | 

| MobiLlama-1B | 59.7 | 38.4 | 59.2 | 74.5 | 44.9 | 62.0 | 43.7 | 59.0 | 55.2 | 

| Falcon-1B | 59.5 | 38.4 | 63.9 | 74.6 |  44.6 | 62.9 |  45.6 | 60.9 | 56.3 | 

| BLOOM-1.1B | 47.6 | 27.3 | 58.6 | 67.0 | 42.4 | 42.2 | 36.6 | 53.8 | 46.9 | 

| TinyLlama-1.1B | 59.2 | 37.1 | 58.1 | 72.9 | 43.9 | 59.1 | 44.7 | 58.8 | 54.2 | 

| **MobileLLM-1B** | 63.0 |  39.0 |  66.7 |  74.4 | 45.0 |  61.4 | 46.8 | 62.3 | **57.3** |  

### MobileLLM-1.5B

| model | boolq | piqa | siqa | hellaswag | winogrande | arc_easy | arc_challenge | obqa | avg. |

| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |

| GPT-neo-1.3B | 51.3 | 33.0 | 61.8 | 70.9 | 43.7 | 48.6 | 41.2 | 54.5 | 50.6 | 

| OPT-1.3B | 54.4 | 31.7 | 58.4 | 71.5 | 44.7 | 53.7 | 44.6 | 59.1 | 52.3 | 

| BLOOM-1.7B | 50.9 | 31.2 | 61.7 | 70.0 | 43.2 | 47.2 | 36.2 | 56.1 | 49.6 | 

| Qwen1.5-1.8B | 61.1 | 36.5 | 68.3 | 74.1 | 47.2 |  60.4 | 42.9 | 61.2 | 56.5 | 

| GPT-neo-2.7B | 55.8 | 34.3 | 62.4 | 72.9 | 43.6 | 55.6 | 40.0 | 57.9 | 52.8 | 

| OPT-2.7B | 56.6 | 34.6 | 61.8 | 74.5 | 45.6 | 60.2 | 48.2 | 59.6 | 55.1 | 

| Pythia-2.8B | 59.4 | 38.9 | 66.1 |  73.8 | 44.5 | 59.6 | 45.0 | 59.4 | 55.8 | 

| BLOOM-3B | 55.1 | 33.6 | 62.1 | 70.5 | 43.2 | 53.9 | 41.6 | 58.2 | 52.3 | 

| **MobileLLM-1.5B** | 67.5 |  40.9 |  65.7 | 74.8 |  46.4 | 64.5 | 50.5 | 64.7 | **59.4** | 

## Acknowledgement

This code is partially based on Hugging Face transformer repo.

## Contact

Zechun Liu, Meta Inc (zechunliu at meta dot com)

Changsheng Zhao, Meta Inc (cszhao at meta dot com)

## Relevant Projects

SpinQuant: LLM Quantization with Learned Rotations [[Paper](https://arxiv.org/pdf/2405.16406)] [[Code](https://github.com/facebookresearch/SpinQuant)]

LLM-QAT: Data-Free Quantization Aware Training for Large Language Models [[Paper](https://arxiv.org/pdf/2305.17888)] [[Code](https://github.com/facebookresearch/LLM-QAT)]

## License

BiT is CC-BY-NC 4.0 licensed as of now.