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README.md
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Use the code below to get started with the model.
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
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model_name = 'kettleguts/zephyr-7b-beta_sparse05'
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print(text[-1])
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#Network pruning, in the context of artificial intelligence and machine learning, refers to the process of removing unimportant or redundant connections, or "pruning," from a neural network\'s architecture. This is done to simplify and optimize the network\'s structure, reduce overfitting, and improve its efficiency, while preserving its overall performance. Pruning typically involves removing connections, neurons, or entire layers, based on metrics such as the weight or sparsity of the connection, or the amount of improvement gained by removing the connection. The goal is to prune the network in a way that balances the trade-off between model size and accuracy, while reducing the network\'s overall complexity and resource requirements. Pruning techniques can range from simple heuristics such as early stopping, to more sophisticated methods such as compressed and pruned models, and iterative and incremental pruning.'}
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## Citation [optional]
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**BibTeX:**
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@misc{tunstall2023zephyr,
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title={Zephyr: Direct Distillation of LM Alignment},
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author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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@misc{sun2023simple,
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title={A Simple and Effective Pruning Approach for Large Language Models},
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author={Mingjie Sun and Zhuang Liu and Anna Bair and J. Zico Kolter},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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Use the code below to get started with the model.
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'''python
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
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model_name = 'kettleguts/zephyr-7b-beta_sparse05'
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print(text[-1])
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#Network pruning, in the context of artificial intelligence and machine learning, refers to the process of removing unimportant or redundant connections, or "pruning," from a neural network\'s architecture. This is done to simplify and optimize the network\'s structure, reduce overfitting, and improve its efficiency, while preserving its overall performance. Pruning typically involves removing connections, neurons, or entire layers, based on metrics such as the weight or sparsity of the connection, or the amount of improvement gained by removing the connection. The goal is to prune the network in a way that balances the trade-off between model size and accuracy, while reducing the network\'s overall complexity and resource requirements. Pruning techniques can range from simple heuristics such as early stopping, to more sophisticated methods such as compressed and pruned models, and iterative and incremental pruning.'}
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'''
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## Citation [optional]
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**BibTeX:**
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'''
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@misc{tunstall2023zephyr,
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title={Zephyr: Direct Distillation of LM Alignment},
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author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
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archivePrefix={arXiv},
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primaryClass={cs.LG}
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}
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'''
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'''
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@misc{sun2023simple,
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title={A Simple and Effective Pruning Approach for Large Language Models},
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author={Mingjie Sun and Zhuang Liu and Anna Bair and J. Zico Kolter},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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'''
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