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