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  license: apache-2.0
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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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- **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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-
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- ---
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-
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-
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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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  | 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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  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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+
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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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+ ## 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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  | 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},