---
datasets:
- instruction-pretrain/medicine-instruction-augmented-corpora
- Open-Orca/OpenOrca
- EleutherAI/pile
- GAIR/lima
- WizardLM/WizardLM_evol_instruct_V2_196k
language:
- en
license: llama3
tags:
- biology
- medical
---
# Instruction Pre-Training: Language Models are Supervised Multitask Learners
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).

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.**

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

**************************** **Updates** ****************************
* 2024/7/15: We scaled up the pre-trained tokens from 100B to 250B, with the number of synthesized instruction-response pairs reaching 500M! Below, we show the performance trend on downstream tasks throughout the pre-training process:
<p align='center'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0okCfRkC6uALTfuNxt0Fa.png" width="700">
</p>
* 2024/6/21: Released the [paper](https://huggingface.co/papers/2406.14491), [code](https://github.com/microsoft/LMOps), and [resources](https://huggingface.co/instruction-pretrain)

## 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 (on 100B tokes):
  - [InstructLM-500M](https://huggingface.co/instruction-pretrain/InstructLM-500M)
  - [InstructLM-1.3B](https://huggingface.co/instruction-pretrain/InstructLM-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)
- General Instruction-Augmented Corpora: [general-instruction-augmented-corpora](https://huggingface.co/datasets/instruction-pretrain/general-instruction-augmented-corpora)
- Domain-Specific Instruction-Augmented Corpora (no finance data to avoid ethical issues): [medicine-instruction-augmented-corpora](https://huggingface.co/datasets/instruction-pretrain/medicine-instruction-augmented-corpora)

## Domain-Adaptive Continued Pre-Training
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).

### 1. To chat with the biomedicine-Llama3-8B model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/medicine-Llama3-8B")
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/medicine-Llama3-8B")

# Put your input here, NO prompt template is required
user_input = '''Question: Which of the following is an example of monosomy?
Options:
- 46,XX
- 47,XXX
- 69,XYY
- 45,X

Please provide your choice first and then provide explanations if possible.'''

inputs = tokenizer(user_input, return_tensors="pt", add_special_tokens=True).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_new_tokens=400)[0]

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

print(pred)
```

### 2. To evaluate our models on the domain-specific tasks
1. Setup dependencies
```bash
git clone https://github.com/microsoft/LMOps
cd LMOps/adaptllm
pip install -r requirements.txt
```

2. Evaluate
```bash
DOMAIN='biomedicine'

# if the model can fit on a single GPU: set MODEL_PARALLEL=False
# elif the model is too large to fit on a single GPU: set MODEL_PARALLEL=True
MODEL_PARALLEL=False

# number of GPUs, chosen from [1,2,4,8]
N_GPU=1

# Set as True
add_bos_token=True

bash scripts/inference.sh ${DOMAIN} 'instruction-pretrain/medicine-Llama3-8B' ${add_bos_token} ${MODEL_PARALLEL} ${N_GPU}
```


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

Instruction Pre-Training
```bibtex
@article{cheng2024instruction,
  title={Instruction Pre-Training: Language Models are Supervised Multitask Learners},
  author={Cheng, Daixuan and Gu, Yuxian and Huang, Shaohan and Bi, Junyu and Huang, Minlie and Wei, Furu},
  journal={arXiv preprint arXiv:2406.14491},
  year={2024}
}
```

[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}
}
```