content.raw: Experimental 
content.type: text
content.value: Experimental 
content.raw: https://huggingface.co/eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF
content.id: eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF
content.type: model
content.type: resource
content.url: https://huggingface.co/eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF
content.raw:  is now available.
content.type: text
content.value:  is now available.
content.type: new_line
content.type: new_line
content.raw: Got close to achieve a 10% reduction in size but the quality started to deteriorate so this version has been pruned more conservatively. Sizes, on average, are about 8% smaller with only a very small penalty (< 1%) in quality.
content.type: text
content.value: Got close to achieve a 10% reduction in size but the quality started to deteriorate so this version has been pruned more conservatively. Sizes, on average, are about 8% smaller with only a very small penalty (< 1%) in quality.
content.type: new_line
content.type: new_line
content.raw: After trying with different models and parameter count, I suspect the best I'll be able to do with the current process is  between 6 and 8% reduction so have decided to try a different approach. I'll publish process and findings next.
content.type: text
content.value: After trying with different models and parameter count, I suspect the best I'll be able to do with the current process is  between 6 and 8% reduction so have decided to try a different approach. I'll publish process and findings next.
content.type: new_line
content.type: new_line
content.raw: For background: 
content.type: text
content.value: For background: 
content.href: https://huggingface.co/posts/eaddario/832567461491467
content.raw: https://huggingface.co/posts/eaddario/832567461491467
content.type: link
rawContent: Experimental https://huggingface.co/eaddario/DeepSeek-R1-Distill-Llama-8B-GGUF is now available.
  
  Got close to achieve a 10% reduction in size but the quality started to deteriorate so this version has been pruned more conservatively. Sizes, on average, are about 8% smaller with only a very small penalty (< 1%) in quality.
  
  After trying with different models and parameter count, I suspect the best I'll be able to do with the current process is  between 6 and 8% reduction so have decided to try a different approach. I'll publish process and findings next.
  
  For background: https://huggingface.co/posts/eaddario/832567461491467
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