---
license: llama3
language:
- en
tags:
- finance
datasets:
- Open-Orca/OpenOrca
- GAIR/lima
- WizardLM/WizardLM_evol_instruct_V2_196k
---
# Instruction Pre-Training: Language Models are Supervised Multitask Learners
This repo contains the **finance 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** ****************************
* 2024/7/31: Updated pre-training suggestions in the `Advanced Usage` section of [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
* 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='left'>
    <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0okCfRkC6uALTfuNxt0Fa.png" width="500">
</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 discussions at [this page](https://huggingface.co/papers/2406.14491)! 🤗**

- Thanks to the demo [davanstrien/instruction-synthesizer](https://huggingface.co/spaces/davanstrien/instruction-synthesizer) for implementing our approach
- 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/finance-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. chat with the finance-Llama3-8B model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

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

# Put your input here, NO prompt template is required
user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered
Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange
MMM Chicago Stock Exchange, Inc.
1.500% Notes due 2026 MMM26 New York Stock Exchange
1.750% Notes due 2030 MMM30 New York Stock Exchange
1.500% Notes due 2031 MMM31 New York Stock Exchange

Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?'''

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. evaluate any Huggingface LMs on domain-dpecific tasks (💡New!)
You can use the following scripts to reproduce our results and evaluate any other Huggingface models on domain-specific tasks. Note that these scripts are not applicable to models that require specific prompt templates (e.g., Llama2-chat, Llama3-Instruct).

1). Set Up Dependencies
   ```bash
   git clone https://github.com/microsoft/LMOps
   cd LMOps/adaptllm
   pip install -r requirements.txt
   ```

2). Evaluate the Model
   ```bash
   # Select the domain from ['biomedicine', 'finance', 'law']
   DOMAIN='finance'
  
   # Specify any Huggingface LM name (Not applicable to models requiring specific prompt templates)
   MODEL='instruction-pretrain/finance-Llama3-8B'
  
   # Model parallelization:
   # - Set MODEL_PARALLEL=False if the model fits on a single GPU. 
   #   We observe that LMs smaller than 10B always meet this requirement.
   # - Set MODEL_PARALLEL=True if the model is too large and encounters OOM on a single GPU.
   MODEL_PARALLEL=False
  
   # Choose the number of GPUs from [1, 2, 4, 8]
   N_GPU=1
  
   # Whether to add a BOS token at the beginning of the prompt input:
   # - Set to False for AdaptLLM.
   # - Set to True for instruction-pretrain models.
   # If unsure, we recommend setting it to False, as this is suitable for most LMs.
   add_bos_token=True

   # Run the evaluation script
   bash scripts/inference.sh ${DOMAIN} ${MODEL} ${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}
}
```

[Adapt LLM to Domains](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}
}
```