103 lines
6.6 KiB
Markdown
103 lines
6.6 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- tiiuae/falcon-refinedweb
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- instruction-pretrain/ft-instruction-synthesizer-collection
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- instruction-pretrain/general-instruction-augmented-corpora
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language:
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- en
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---
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# Instruction Pre-Training: Language Models are Supervised Multitask Learners (EMNLP 2024)
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This repo contains the **general models pre-trained from scratch** (on 100B tokens) in our paper [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://huggingface.co/papers/2406.14491).
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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.
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<p align='center'>
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/vRdsFIVQptbNaGiZ18Lih.png" width="400">
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</p>
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**************************** **Updates** ****************************
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* **2026/1/23: Released [LLM-in-Sandbox Elicits General Agentic Intelligence](https://huggingface.co/papers/2601.16206), where the data of `Instruction Pre-Training` achieves robust generalization in agentic RL!**
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* 2024/11/30: Released the multimodal version of the instruction synthesizer: [Visual Instruction Synthesizer](https://huggingface.co/AdaptLLM/Adapt-MLLM-to-Domains)
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* 2024/9/20: Our paper has been accepted by EMNLP 2024 main conference🎉
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* 2024/9/11: Updated [FAQ on continual pre-training from Llama3](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
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* 2024/8/29: Updated [guidelines](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B) on evaluating any 🤗Huggingface models on the domain-specific tasks
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* 2024/7/31: Updated pre-training suggestions in the `Advanced Usage` section of [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
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* 2024/7/15: We scaled up the pre-trained tokens from 100B to 250B, with the number of synthesized instruction-response pairs reaching 500M. The performance trend on downstream tasks throughout the pre-training process:
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<p align='left'>
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0okCfRkC6uALTfuNxt0Fa.png" width="500">
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</p>
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* 2024/6/21: Released the [paper](https://huggingface.co/papers/2406.14491), [code](https://github.com/microsoft/LMOps), and [resources](https://huggingface.co/instruction-pretrain)
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## Resources
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**🤗 We share our data and models with example usages, feel free to open any discussions at [this page](https://huggingface.co/papers/2406.14491)! 🤗**
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- Thanks to the demo [davanstrien/instruction-synthesizer](https://huggingface.co/spaces/davanstrien/instruction-synthesizer) for implementing our approach
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- Context-Based Instruction Synthesizer: [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
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- Fine-Tuning Data for the Synthesizer: [ft-instruction-synthesizer-collection](https://huggingface.co/datasets/instruction-pretrain/ft-instruction-synthesizer-collection)
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- General Models Pre-Trained from Scratch (on 100B tokes):
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- [InstructLM-500M](https://huggingface.co/instruction-pretrain/InstructLM-500M)
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- [InstructLM-1.3B](https://huggingface.co/instruction-pretrain/InstructLM-1.3B)
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- Domain-Specific Models Pre-Trained from Llama3-8B:
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- [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B)
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- [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B)
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- General Instruction-Augmented Corpora: [general-instruction-augmented-corpora](https://huggingface.co/datasets/instruction-pretrain/general-instruction-augmented-corpora)
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- Domain-Specific Instruction-Augmented Corpora (no finance data to avoid ethical issues): [medicine-instruction-augmented-corpora](https://huggingface.co/datasets/instruction-pretrain/medicine-instruction-augmented-corpora)
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## General Pre-Training From Scratch
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We augment the [RefinedWeb corproa](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) with instruction-response pairs generated by our [context-based instruction synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer) to pre-train general langauge models from scratch.
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To evaluate our general base model using the [lm-evaluation-harness framework](https://github.com/EleutherAI/lm-evaluation-harness)
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1. Setup dependencies:
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```bash
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git clone https://github.com/EleutherAI/lm-evaluation-harness
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cd lm-evaluation-harness
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pip install -e .
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```
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2. Evaluate:
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```bash
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MODEL=instruction-pretrain/InstructLM-1.3B
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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
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accelerate launch -m lm_eval --model hf \
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--model_args pretrained=${MODEL},add_bos_token=${add_bos_token},dtype=float16 \
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--gen_kwargs do_sample=False \
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--tasks piqa,hellaswag,winogrande \
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--batch_size auto \
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--num_fewshot 0
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accelerate launch -m lm_eval --model hf \
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--model_args pretrained=${MODEL},add_bos_token=${add_bos_token},dtype=float16 \
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--gen_kwargs do_sample=False \
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--tasks social_iqa,ai2_arc,openbookqa,boolq,mmlu \
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--batch_size auto \
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--num_fewshot 5
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```
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## Citation
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If you find our work helpful, please cite us:
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[Instruction Pre-Training](https://huggingface.co/papers/2406.14491) (EMNLP 2024)
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```bibtex
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@article{cheng2024instruction,
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title={Instruction Pre-Training: Language Models are Supervised Multitask Learners},
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author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu},
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journal={arXiv preprint arXiv:2406.14491},
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year={2024}
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}
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```
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[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530)（ICLR 2024）
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```bibtex
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@inproceedings{
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cheng2024adapting,
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title={Adapting Large Language Models via Reading Comprehension},
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author={Daixuan Cheng and Shaohan Huang and Furu Wei},
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booktitle={The Twelfth International Conference on Learning Representations},
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year={2024},
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url={https://openreview.net/forum?id=y886UXPEZ0}
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}
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``` |