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Model: princeton-nlp/Sheared-LLaMA-1.3B Source: Original Platform
2026-05-05 22:44:33 +08:00
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+---
+license: apache-2.0
+---
+
+**Paper**: [https://arxiv.org/pdf/2310.06694.pdf](https://arxiv.org/pdf/2310.06694.pdf)  
+**Code**: https://github.com/princeton-nlp/LLM-Shearing  
+**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)  
+**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)  
+**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)  
+
+**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](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
+
+```
+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}
+}
+```
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_princeton-nlp__Sheared-LLaMA-1.3B)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 31.47   |
+| ARC (25-shot)         | 32.85          |
+| HellaSwag (10-shot)   | 60.91    |
+| MMLU (5-shot)         | 25.71         |
+| TruthfulQA (0-shot)   | 37.14   |
+| Winogrande (5-shot)   | 58.64   |
+| GSM8K (5-shot)        | 0.45        |
+| DROP (3-shot)         | 4.56         |