license: apache-2.0
datasets:
- tiiuae/falcon-refinedweb
- instruction-pretrain/ft-instruction-synthesizer-collection
language:
- en
Instruction Pre-Training: Language Models are Supervised Multitask Learners
This repo contains the general models pre-trained from scratch 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 continual 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.
Resources
🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗
- Context-Based Instruction Synthesizer: instruction-synthesizer
- Fine-Tuning Data for the Synthesizer: ft-instruction-synthesizer-collection
- General Models Pre-Trained from Scratch:
- Domain-Specific Models Pre-Trained from Llama3-8B:
- General Instruction-Augmented Corpora: general-instruction-augmented-corpora
- Domain-Specific Instruction-Augmented Corpora (no finance data to avoid ethical issues): medicine-instruction-augmented-corpora
General Pre-Training From Scratch
We augment the RefinedWeb corproa with instruction-response pairs generated by our context-based instruction synthesizer to pre-train general langauge models from scratch.
To evaluate our general base model using the lm-evaluation-harness framework
- Setup dependencies:
git clone https://github.com/EleutherAI/lm-evaluation-harness
cd lm-evaluation-harness
pip install -e .
- Evalaute:
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:
Instruction Pre-Training
@article{cheng2024instruction,
title={Instruction Pre-Training: Language Models are Supervised Multitask Learners},
author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu},
journal={arXiv preprint arXiv:2406.14491},
year={2024}
}
@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}
}