README.md

---
license: apache-2.0
---
# 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="./hf_intro.png" width="400">
</p>

## Synthesize Instruction-Response Pairs from Any Raw Corproa
We conduct multitask fine-tuning on a language model to develop an instruction synthesizer capable of generating instruction-response pairs from any raw text.

<p align='center'>
    <img src="./hf_synthesizer.png" width="700">
</p>

An example script to prompt the synthesizer to generate instruction-response pairs based on the given raw text is:
```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')
```