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--- |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- instruction-pretrain/ft-instruction-synthesizer-collection |
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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 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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## Resources |
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**🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗** |
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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: |
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- [InstructLM-500M](https://huggingface.co./instruction-pretrain/InstructLM-500M) |
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- [InstructLLM-1.3B](https://huggingface.co./instruction-pretrain/InstructLLM-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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## Synthesize Instruction-Response Pairs based on Any Raw text |
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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) |
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<p align='center'> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0889QyG59QM3rPeZlcTzZ.png" width="700"> |
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</p> |
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For example, to prompt the synthesizer to generate instruction-response pairs based on a given raw text: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/instruction-synthesizer") |
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tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/instruction-synthesizer") |
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# Put your raw text here: |
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context = '''Free Fishing Weekend in NYS Slated |
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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''' |
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def parse_pred(pred): |
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"""Extract the list of instruction-response pairs from the prediction""" |
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QA_str_list = pred.split('</END>') |
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if not pred.endswith('</END>'): |
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QA_str_list = QA_str_list[:-1] |
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QA_list = [] |
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raw_questions = [] |
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for QA_str in QA_str_list: |
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try: |
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assert len(QA_str.split('<ANS>')) == 2, f'invalid QA string: {QA_str}' |
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Q_str, A_str = QA_str.split('<ANS>') |
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Q_str, A_str = Q_str.strip(), A_str.strip() |
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assert Q_str.startswith('<QUE>'), f'invalid question string: {Q_str} in QA_str: {QA_str}' |
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assert len(A_str) > 0, f'invalid answer string in QA_str: {QA_str}' |
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Q_str = Q_str.replace('<QUE>', '').strip() |
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assert Q_str.lower() not in raw_questions, f'duplicate question: {Q_str}' |
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QA_list.append({'Q': Q_str, 'A': A_str}) |
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raw_questions.append(Q_str.lower()) |
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except: |
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pass |
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return QA_list |
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def get_instruction_response_pairs(context): |
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'''Prompt the synthesizer to generate instruction-response pairs based on the given context''' |
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prompt = f'<s> <CON> {context} </CON>\n\n' |
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inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt").input_ids.to(model.device) |
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outputs = model.generate(input_ids=inputs, max_new_tokens=400)[0] |
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pred_start = int(inputs.shape[-1]) |
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pred = tokenizer.decode(outputs[pred_start:], skip_special_tokens=True) |
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return parse_pred(pred) |
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# Get the list of generated instruction-response paris |
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instruction_response_pairs = get_instruction_response_pairs(context) |
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# Print out the results |
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print(f'# Context:\n{context}\n') |
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for index, pair in enumerate(instruction_response_pairs): |
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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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### To-Do |
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- [ ] Add example usages for synthesizing few-shot examples |
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## Citation |
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If you find our work helpful, please cite us: |
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[AdaptLLM](https://huggingface.co./papers/2309.09530) |
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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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``` |
