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--- |
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datasets: |
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- EleutherAI/pile |
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- Open-Orca/OpenOrca |
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- GAIR/lima |
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- WizardLM/WizardLM_evol_instruct_V2_196k |
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language: |
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- en |
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license: llama3 |
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tags: |
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- biology |
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- medical |
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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 **biomedicine model developed from Llama3-8B** in our paper [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://huggingface.co/papers/2406.14491). |
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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. ***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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- [InstructLM-1.3B](https://huggingface.co/instruction-pretrain/InstructLM-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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## Domain-Adaptive Continued Pre-Training |
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Following [AdaptLLM](https://huggingface.co/AdaptLLM/medicine-chat), we augment the domain-specific raw corpora with instruction-response pairs generated by our [context-based instruction synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer). |
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For example, to chat with the biomedicine-Llama3-8B model: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/medicine-Llama3-8B") |
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tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/medicine-Llama3-8B") |
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# Put your input here, NO prompt template is required |
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user_input = '''Question: Which of the following is an example of monosomy? |
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Options: |
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- 46,XX |
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- 47,XXX |
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- 69,XYY |
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- 45,X |
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Please provide your choice first and then provide explanations if possible.''' |
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inputs = tokenizer(user_input, return_tensors="pt", add_special_tokens=True).input_ids.to(model.device) |
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outputs = model.generate(input_ids=inputs, max_new_tokens=400)[0] |
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answer_start = int(inputs.shape[-1]) |
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pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True) |
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print(pred) |
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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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[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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``` |
