license: apache-2.0
OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA
In this repo, we release a permissively licensed open-source instruction-following model based on OpenLLaMA. In this release, we release a public preview of the 7B OpenAlpaca model based on the previewed version of OpenLLaMA that is a 7B model trained with 700 billion tokens. We provide PyTorch weights of OpenAlpaca. Stay tuned for our forthcoming updates!
[Project Page] (https://github.com/yxuansu/OpenAlpaca)
Dataset and Training
We train our model on the dolly 15k dataset released by Databricks. The training configurations are provided in the table below. The training takes on 8 x A100(40G) GPUs and lasts for around 30 minutes.
| Batch Size | 64 |
| Learning rate | 2e-5 |
| Epochs | 3 |
| Max length | 1024 |
Example Usage
Below shows an example on how to use OpenAlpaca
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
# the previewed version of OpenAlpaca
model_path = r'openllmplayground/openalpaca_7b_700bt_preview'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(model_path).cuda()
tokenizer.bos_token_id, tokenizer.eos_token_id = 1,2 # see https://github.com/openlm-research/open_llama#preview-weights-release-and-usage
# same prompt as provided in https://crfm.stanford.edu/2023/03/13/alpaca.html
instruction = r'What is an alpaca? How is it different from a llama?'
'''
instruction = r'Write an e-mail to congratulate new Standford admits and mention that you are excited about meeting all of them in person.'
instruction = r'What is the capital of Tanzania?'
instruction = r'Write a well-thought out abstract for a machine learning paper that proves that 42 is the optimal seed for training neural networks.'
'''
prompt_no_input = f'Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:'
tokens = tokenizer.encode(prompt_no_input)
tokens = torch.LongTensor(tokens).unsqueeze(0)
instance = {'input_ids': tokens,
'top_k': 50,
'top_p': 0.9,
'generate_len': 128}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length+instance['generate_len'],
use_cache=True,
do_sample=True,
top_p=instance['top_p'],
top_k=instance['top_k']
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
print(f'[!] Generation results: {string}')
License and Usage
OpenAlpaca is permissively licensed under the Apache 2.0 license and can be used freely for academic/commercial purposes.
Contact
We would love to get feedback from the community. If you have any questions, please open an issue or contact us.
OpenAlpaca is developed by: Yixuan Su*, Tian Lan*, and Deng Cai (The first two members* contributed equally.)
Reference:
If you found OpenAlpaca useful in your research or applications, please kindly cite using the following BibTeX:
@misc{openalpaca,
author = {Yixuan Su and Tian Lan and Deng Cai},
title = {OpenAlpaca: A Fully Open-Source Instruction-Following Model Based On OpenLLaMA},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/yxuansu/OpenAlpaca}},
}
@software{openlm2023openllama,
author = {Xinyang Geng and Hao Liu},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Hugo Touvron and Thibaut Lavril and Gautier Izacard and Xavier Martinet and Marie{-}Anne Lachaux and Timoth{\'{e}}e Lacroix and Baptiste Rozi{\`{e}}re and Naman Goyal and Eric Hambro and Faisal Azhar and Aur{\'{e}}lien Rodriguez and Armand Joulin and Edouard Grave and Guillaume Lample},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}