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Model: princeton-nlp/Sheared-LLaMA-1.3B Source: Original Platform
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README.md
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---
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license: apache-2.0
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---
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**Paper**: [https://arxiv.org/pdf/2310.06694.pdf](https://arxiv.org/pdf/2310.06694.pdf)
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**Code**: https://github.com/princeton-nlp/LLM-Shearing
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**Models**: [Sheared-LLaMA-1.3B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B), [Sheared-LLaMA-2.7B](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B)
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**Pruned Models without Continued Pre-training**: [Sheared-LLaMA-1.3B-Pruned](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B-Pruned), [Sheared-LLaMA-2.7B-Pruned](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B-Pruned)
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**Instruction-tuned Models**: [Sheared-LLaMA-1.3B-ShareGPT](https://huggingface.co/princeton-nlp/Sheared-LLaMA-1.3B-ShareGPT), [Sheared-LLaMA-2.7B-ShareGPT](https://huggingface.co/princeton-nlp/Sheared-LLaMA-2.7B-ShareGPT)
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**License**: Must comply with license of Llama2 since it's a model derived from Llama2.
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---
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Sheared-LLaMA-1.3B is a model pruned and further pre-trained from [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf). We dynamically load data from different domains in the [RedPajama dataset](https://github.com/togethercomputer/RedPajama-Data) 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
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```
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model = AutoModelForCausalLM.from_pretrained("princeton-nlp/Sheared-LLaMA-1.3B")
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```
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- Smaller-scale
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- Same vocabulary as LLaMA1 and LLaMA2
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- Derived with a budget of 50B tokens by utilizing existing strong LLMs
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## Downstream Tasks
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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.
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| Model | # Pre-training Tokens | Average Performance |
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| ------------------- | --------------------- | ------------------- |
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| LLaMA2-7B | 2T | 64.6 |
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**1.3B**
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| Model | # Pre-training Tokens | Average Performance |
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| ------------------- | --------------------- | ------------------- |
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| OPT-1.3B | 300B | 48.2 |
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| Pythia-1.4B | 300B | 48.9 |
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| **Sheared-LLaMA-1.3B** | **50B** | **51.0** |
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**3B**
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| Model | # Pre-training Tokens | Average Performance |
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| ------------------- | --------------------- | ------------------- |
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| OPT-2.7B | 300B | 51.4 |
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| Pythia-2.8B | 300B | 52.5 |
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| INCITE-Base-3B | 800B | 54.7 |
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| Open-LLaMA-3B-v1 | 1T | 55.1 |
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| Open-LLaMA-3B-v2 | 1T | 55.7 |
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| Sheared-LLaMA-2.7B | 50B | 56.7 |
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## Bibtex
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```
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@article{xia2023sheared,
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title={Sheared llama: Accelerating language model pre-training via structured pruning},
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author={Xia, Mengzhou and Gao, Tianyu and Zeng, Zhiyuan and Chen, Danqi},
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journal={arXiv preprint arXiv:2310.06694},
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year={2023}
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}
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```
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Sheared-LLaMA-1.3B)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 31.47 |
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| ARC (25-shot) | 32.85 |
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| HellaSwag (10-shot) | 60.91 |
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| MMLU (5-shot) | 25.71 |
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| TruthfulQA (0-shot) | 37.14 |
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| Winogrande (5-shot) | 58.64 |
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| GSM8K (5-shot) | 0.45 |
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| DROP (3-shot) | 4.56 |
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config.json
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