Answers.Body: <p>The authorities released their code:
  <a href="https://github.com/IST-DASLab/sparsegpt" rel="nofollow noreferrer">https://github.com/IST-DASLab/sparsegpt</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-03-24T03:52:28.137Z
Answers.LastActivityDate: 2023-03-24T03:52:28.137Z
Answers.UserDisplayName: user68072
Body: <p>SparseGPT: <a href="https://arxiv.org/pdf/2301.00774.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2301.00774.pdf</a></p>
  <p>Pruning on the super large language model based on the Transformers structure has achieved a high compression rate with a small loss of accuracy. Is there any related code reproduction or research?</p>
  <p>Someone mentioned on <a href="https://github.com/karpathy/nanoGPT" rel="nofollow noreferrer">https://github.com/karpathy/nanoGPT</a>, but no response.</p>
  
Tags: <natural-language-processing><large-language-models>
Title: SparseGPT code reproduction
UserDisplayName: user68072
ThreadText: # SparseGPT code reproduction
  
  (Asked by: username_0 on 2023-03-14)
  
  SparseGPT: <https://arxiv.org/pdf/2301.00774.pdf>
  
  Pruning on the super large language model based on the Transformers structure has achieved a high compression rate with a small loss of accuracy. Is there any related code reproduction or research?
  
  Someone mentioned on <https://github.com/karpathy/nanoGPT>, but no response.
  
  **Tags:** natural-language-processing, large-language-models
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2023-03-24. Score: 1 | Accepted answer
  
  The authorities released their code:
  <https://github.com/IST-DASLab/sparsegpt>
  
  
  ---
  
  