Text Generation
Transformers
Safetensors
English
mistral
text-generation-inference
Inference Endpoints
instruction-pretrain commited on
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README.md CHANGED
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- ---
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- license: apache-2.0
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- ---
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  # Instruction Pre-Training: Language Models are Supervised Multitask Learners
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  This repo contains the **context-based instruction synthesizer** used in our paper **Instruction Pre-Training: Language Models are Supervised Multitask Learners**.
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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 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.
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  <p align='center'>
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  <img src="./hf_intro.png" width="400">
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  print(f'## Instruction {index + 1}:\n{pair["Q"]}\n## Response {index + 1}:\n{pair["A"]}\n')
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  ```
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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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  ```bibtex
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- @article{cheng2023adapting,
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- title={Adapting large language models via reading comprehension},
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- author={Cheng, Daixuan and Huang, Shaohan and Wei, Furu},
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- journal={arXiv preprint arXiv:2309.09530},
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- year={2023}
 
 
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  }
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- ```
 
 
 
 
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  # Instruction Pre-Training: Language Models are Supervised Multitask Learners
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  This repo contains the **context-based instruction synthesizer** used in our paper **Instruction Pre-Training: Language Models are Supervised Multitask Learners**.
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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 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.
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  <p align='center'>
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  <img src="./hf_intro.png" width="400">
 
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  print(f'## Instruction {index + 1}:\n{pair["Q"]}\n## Response {index + 1}:\n{pair["A"]}\n')
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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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  ```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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+ ```
config.json ADDED
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+ {
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+ "MistralForCausalLM"
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+ }
generation_config.json ADDED
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tokenizer.json ADDED
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