README.md

---
license: apache-2.0
language:
- en
datasets:
- instruction-pretrain/ft-instruction-synthesizer-collection
---
# 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 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.

<p align='center'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/vRdsFIVQptbNaGiZ18Lih.png" width="400">
</p>


## Resources
**🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗**

- Context-Based Instruction Synthesizer: [instruction-synthesizer](https://huggingface.co./instruction-pretrain/instruction-synthesizer)
- Fine-Tuning Data for the Synthesizer: [ft-instruction-synthesizer-collection](https://huggingface.co./datasets/instruction-pretrain/ft-instruction-synthesizer-collection)
- General Models Pre-Trained from Scratch:
  - [InstructLM-500M](https://huggingface.co./instruction-pretrain/InstructLM-500M)
  - [InstructLLM-1.3B](https://huggingface.co./instruction-pretrain/InstructLLM-1.3B)
- Domain-Specific Models Pre-Trained from Llama3-8B:
  - [Finance-Llama3-8B](https://huggingface.co./instruction-pretrain/finance-Llama3-8B)
  - [Biomedicine-Llama3-8B](https://huggingface.co./instruction-pretrain/medicine-Llama3-8B)


## Synthesize Instruction-Response Pairs based on Any Raw text
We conduct multitask fine-tuning on a language model to develop an instruction synthesizer capable of generating instruction-response pairs from any raw text. The fine-tuning data are available at [ft-instruction-synthesizer-collection](https://huggingface.co./datasets/instruction-pretrain/ft-instruction-synthesizer-collection)


<p align='center'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0889QyG59QM3rPeZlcTzZ.png" width="700">
</p>

For example, to prompt the synthesizer to generate instruction-response pairs based on a given raw text:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/instruction-synthesizer")
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/instruction-synthesizer")

# Put your raw text here:
context = '''Free Fishing Weekend in NYS Slated
This weekend (June 28th-29th) New Yorkers may fish for free without a license in any of the state's 7,500 lakes and ponds or 50,000 miles of rivers and streams. In addition, there are a number of free events and fishing clinics taking place across the state to encourage New Yorkers to enjoy the great outdoors. For more information, visit'''

def parse_pred(pred):
    """Extract the list of instruction-response pairs from the prediction"""
    QA_str_list = pred.split('</END>')
    if not pred.endswith('</END>'):
        QA_str_list = QA_str_list[:-1]

    QA_list = []
    raw_questions = []
    for QA_str in QA_str_list:
        try:
            assert len(QA_str.split('<ANS>')) == 2, f'invalid QA string: {QA_str}'
            Q_str, A_str = QA_str.split('<ANS>')
            Q_str, A_str = Q_str.strip(), A_str.strip()
            assert Q_str.startswith('<QUE>'), f'invalid question string: {Q_str} in QA_str: {QA_str}'
            assert len(A_str) > 0, f'invalid answer string in QA_str: {QA_str}'
            Q_str = Q_str.replace('<QUE>', '').strip()
            assert Q_str.lower() not in raw_questions, f'duplicate question: {Q_str}'
            QA_list.append({'Q': Q_str, 'A': A_str})
            raw_questions.append(Q_str.lower())
        except:
            pass

    return QA_list

def get_instruction_response_pairs(context):
    '''Prompt the synthesizer to generate instruction-response pairs based on the given context'''
    prompt = f'<s> <CON> {context} </CON>\n\n'
    inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").input_ids.to(model.device)
    outputs = model.generate(input_ids=inputs, max_new_tokens=400)[0]

    pred_start = int(inputs.shape[-1])
    pred = tokenizer.decode(outputs[pred_start:], skip_special_tokens=True)
    return parse_pred(pred)

# Get the list of generated instruction-response paris
instruction_response_pairs = get_instruction_response_pairs(context)

# Print out the results
print(f'# Context:\n{context}\n')
for index, pair in enumerate(instruction_response_pairs):
    print(f'## Instruction {index + 1}:\n{pair["Q"]}\n## Response {index + 1}:\n{pair["A"]}\n')
```

### To-Do
- [ ] Add example usages for synthesizing few-shot examples

## Citation
If you find our work helpful, please cite us:

[AdaptLLM](https://huggingface.co./papers/2309.09530)
```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}
}
```