README.md

# Instruction Pre-Training: Language Models are Supervised Multitask Learners
This repo contains the **context-based instruction synthesizer** used in our paper **Instruction Pre-Training: Language Models are Supervised Multitask Learners**.

We explore supervised multitask pre-training by proposing ***Instruction Pre-Training***, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train language models. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of *Instruction Pre-Training*. Instruction Pre-Training* outperforms *Vanilla Pre-training* in both general pre-training from scratch and domain-adaptive continued pre-training. **In pre-training from scratch, *Instruction Pre-Training* not only improves pre-trained base models but also benefits more from further instruction tuning.** In continual pre-training, *Instruction Pre-Training* enables Llama3-8B to be comparable to or even outperform Llama3-70B.

<p align='center'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/j1hlqGreoZrBsK7sz3oG9.png" width="500">
</p>

## General Pre-Training From Scratch
We augment the RefinedWeb corproa with instruction-response pairs generated by our [context-based instruction synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer) to pre-train a general langauge model from scratch.

To evaluate our general base model using the [lm-evaluation-harness framework](https://github.com/EleutherAI/lm-evaluation-harness)

1. Setup dependencies:
```bash
git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
```

2. Evalaute
```bash
MODEL=instruction-pretrain/InstructLM-500M
add_bos_token=True # this flag is needed because lm-eval-harness set add_bos_token to False by default, but ours require add_bos_token to be True

accelerate launch -m lm_eval --model hf \
    --model_args pretrained=${MODEL},add_bos_token=${add_bos_token},dtype=float16  \
    --gen_kwargs do_sample=False \
    --tasks piqa,hellaswag,winogrande \
    --batch_size auto \
    --num_fewshot 0

accelerate launch -m lm_eval --model hf \
    --model_args pretrained=${MODEL},add_bos_token=${add_bos_token},dtype=float16 \
    --gen_kwargs do_sample=False \
    --tasks social_iqa,ai2_arc,openbookqa,boolq,mmlu \
    --batch_size auto \
    --num_fewshot 5
```

## Citation
If you find our work helpful, please cite us:
```bibtex
@inproceedings{
cheng2024adapting,
title={Adapting Large Language Models via Reading Comprehension},
author={Daixuan Cheng and Shaohan Huang and Furu Wei},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=y886UXPEZ0}
}
```