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MiniPLM: Knowledge Distillation of Pre-Training Language Models

1 Setup

pip3 install -r requirements.txt
git clone https://github.com/EleutherAI/lm-evaluation-harness
pip3 install -e lm-evaluation-harness

bash install.sh

2 Pre-Training Corpus $\mathcal{D}$

We use the Pile as our pre-training corpus. Refer to tools/get_pile.py to get the data ready. Run the following command for tokenization:

bash scripts/tools/process_data/pile_qwen.sh /PATH/TO/MiniPLM

The processed data is stored in processed_data/pretrain/pile/qwen-1025, containing several shards (a pair of .bin and .idx files). Each shard contains about 1B tokens. We provide the processed version (100B tokens) for reproducibility.

3 Models

3.1 Teacher Model

We use Qwen1.5-1.8B as the teacher LM. You can download this model can put it in checkpoints/qwen/1.8B.

3.2 Reference Model

The reference model is a 104M Qwen LM trained on 5B tokens randomly split from the Pile, which should be put in checkpoints/qwen/104M_ref.

3.3 Pre-Trained Models

The MiniPLM models and baseline models can be found in the HuggingFace Hub.

4 Training

4.1 MiniPLM

Difference Sampling

First, run inference of the teacher LM and the reference LM on the Pile data:

bash scripts/miniplm/difference_sampling/1.8B.sh /PATH/TO/MiniPLM
bash scripts/miniplm/difference_sampling/104M.sh /PATH/TO/MiniPLM

Then, compute the difference scores $r(x,p,p_{\text{ref}})=\frac{\log p(x)}{\log p_{\text{ref}}(x)}$:

python3 scripts/miniplm/difference_sampling/compute_difference_scores.py /PATH/TO/MiniPLM

Finally, construct the refined pre-training corpus with the difference scores:

python3 scripts/miniplm/difference_sampling/construct_pretrain_data.py /PATH/TO/MiniPLM 0.5 # selection ratio

This process constructs a 50B-token corpus from a 100B-token corpus. We open-source the refined data (50B tokens) for reproducibility.

Pre-Training

Before pre-training, you need to put the config.json and the tokenizer-related files in checkpoints/qwen/200M, checkpoints/qwen/500M, and checkpoints/qwen/1.2B, which can be downloaded from our huggingface hub.

bash scripts/miniplm/pretraining/qwen/200M.sh /PATH/TO/MiniPLM
bash scripts/miniplm/pretraining/qwen/500M.sh /PATH/TO/MiniPLM
bash scripts/miniplm/pretraining/qwen/1.2B.sh /PATH/TO/MiniPLM

KD Across Model Families

To distill the knowledge of Qwen models to Mamba or LLaMA3.1, first prepare the config.json and tokenization-related files in checkpoints/mamba/130M and checkpoints/llama3.1/212M, which can be downloaded from our huggingface hub. Then, convert the Qwen tokenization to the target tokenization:

bash scripts/tools/convert_tokenization/convert_tokenization_qwen_mamba.sh /PATH/TO/MiniPLM
bash scripts/tools/convert_tokenization/convert_tokenization_qwen_llama3_1.sh /PATH/TO/MiniPLM

NOTE: You may need to setup the environments following the official repo of Mamba before runing the mamba experiments.

4.2 Baselines

Conventional Pre-Training

bash scripts/pretrain/qwen/200M.sh /PATH/TO/MiniPLM
bash scripts/pretrain/qwen/500M.sh /PATH/TO/MiniPLM
bash scripts/pretrain/qwen/1.2B.sh /PATH/TO/MiniPLM

Vanilla KD

bash scripts/vanilla_kd/qwen/200M.sh /PATH/TO/MiniPLM
bash scripts/vanilla_kd/qwen/500M.sh /PATH/TO/MiniPLM
bash scripts/vanilla_kd/qwen/1.2B.sh /PATH/TO/MiniPLM

SeqKD

bash scripts/seqkd/qwen/200M.sh /PATH/TO/MiniPLM
bash scripts/seqkd/qwen/500M.sh /PATH/TO/MiniPLM
bash scripts/seqkd/qwen/1.2B.sh /PATH/TO/MiniPLM

MiniLLM

We use the official codebase of MiniLLM for this baseline.

5 Evaluation

LM-Evaluation-Harness

bash scripts/eval/harness.sh /PATH/TO/MiniPLM --model-path /PATH/TO/TRAINED_CKPT --ckpt-name NAME_OF_CKPT

NOTE: The story_cloze dataset may require manually downloading. Please follow the instructions in this link to download the test sets. After downloading, you will need to replace the task configuration file lm-evaluation-harness/tasks/storycloze/storycloze_2018.yaml with configs/lm_harness_tasks/storycloze_2018.yamlthat refers to the downloaded directory.

Language Modeling

bash scripts/eval/lm.sh /PATH/TO/MiniPLM --model-path /PATH/TO/TRAINED_CKPT --ckpt-name NAME_OF_CKPT

6 Citation

@article{miniplm,
    title={MiniPLM: Knowledge Distillation for Pre-Training Language Models}, 
    author={Yuxian Gu and Hao Zhou and Fandong Meng and Jie Zhou and Minlie Huang},
    journal={arXiv preprint arXiv:2410.17215},
    year={2024}
}