medicine-llama3-8B-exl2

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.

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>

Resources

🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗

• Context-Based Instruction Synthesizer: instruction-synthesizer
• Fine-Tuning Data for the Synthesizer: ft-instruction-synthesizer-collection
• General Models Pre-Trained from Scratch:
  • InstructLM-500M
  • InstructLM-1.3B
• Domain-Specific Models Pre-Trained from Llama3-8B:
  • Finance-Llama3-8B
  • Biomedicine-Llama3-8B
• General Instruction-Augmented Corpora: general-instruction-augmented-corpora
• Domain-Specific Instruction-Augmented Corpora (no finance data to avoid ethical issues): medicine-instruction-augmented-corpora

Domain-Adaptive Continued Pre-Training

Following AdaptLLM, we augment the domain-specific raw corpora with instruction-response pairs generated by our context-based instruction synthesizer.

1. To chat with the biomedicine-Llama3-8B model:

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

git clone https://github.com/microsoft/LMOps
cd LMOps/adaptllm
pip install -r requirements.txt

1. Evaluate

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:

AdaptLLM

@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}
}