License: Must comply with license of Llama2 since it's a model derived from Llama2.
Sheared-LLaMA-1.3B is a model pruned and further pre-trained from meta-llama/Llama-2-7b-hf. We dynamically load data from different domains in the RedPajama dataset to prune and contune pre-train the model. We use 0.4B tokens for pruning and 50B tokens for continued pre-training the pruned model. This model can be loaded with HuggingFace via
model = AutoModelForCausalLM.from_pretrained("princeton-nlp/Sheared-LLaMA-1.3B")
Smaller-scale
Same vocabulary as LLaMA1 and LLaMA2
Derived with a budget of 50B tokens by utilizing existing strong LLMs
Downstream Tasks
We evaluate on an extensive set of downstream tasks including reasoning, reading comprehension, language modeling and knowledge intensive tasks. Our Sheared-LLaMA models outperform existing large language models.
Model
# Pre-training Tokens
Average Performance
LLaMA2-7B
2T
64.6
1.3B
Model
# Pre-training Tokens
Average Performance
OPT-1.3B
300B
48.2
Pythia-1.4B
300B
48.9
Sheared-LLaMA-1.3B
50B
51.0
3B
Model
# Pre-training Tokens
Average Performance
OPT-2.7B
300B
51.4
Pythia-2.8B
300B
52.5
INCITE-Base-3B
800B
54.7
Open-LLaMA-3B-v1
1T
55.1
Open-LLaMA-3B-v2
1T
55.7
Sheared-LLaMA-2.7B
50B
56.7
Bibtex
@article{xia2023sheared,
title={Sheared llama: Accelerating language model pre-training via structured pruning},
author={Xia, Mengzhou and Gao, Tianyu and Zeng, Zhiyuan and Chen, Danqi},
journal={arXiv preprint arXiv:2310.06694},
year={2023}
}
