content.raw: Check out quantized weights from ISTA-DAS Lab directly in their organisation page: 
content.type: text
content.value: Check out quantized weights from ISTA-DAS Lab directly in their organisation page: 
content.image: https://cdn-avatars.huggingface.co/v1/production/uploads/628e0ce4e53bbd334577fcb0/TRPtgtSavYjDJOK3S1I8M.png
content.raw: https://huggingface.co/ISTA-DASLab
content.id: ISTA-DASLab
content.type: org
content.type: resource
content.url: https://huggingface.co/ISTA-DASLab
content.raw:  ! With official weights of AQLM (for 2bit quantization) & QMoE (1-bit MoE quantization)
content.type: text
content.value:  ! With official weights of AQLM (for 2bit quantization) & QMoE (1-bit MoE quantization)
content.type: new_line
content.type: new_line
content.raw: Read more about these techniques below:
content.type: text
content.value: Read more about these techniques below:
content.type: new_line
content.type: new_line
content.raw: AQLM paper: 
content.type: text
content.value: AQLM paper: 
content.label: Extreme Compression of Large Language Models via Additive Quantization (2401.06118)
content.raw: https://hf.co/papers/2401.06118
content.id: 2401.06118
content.type: paper
content.type: resource
content.url: https://hf.co/papers/2401.06118
content.type: new_line
content.raw: QMoE: 
content.type: text
content.value: QMoE: 
content.label: QMoE: Practical Sub-1-Bit Compression of Trillion-Parameter Models (2310.16795)
content.raw: https://huggingface.co/papers/2310.16795
content.id: 2310.16795
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2310.16795
content.type: new_line
content.type: new_line
content.raw: Some useful links below:
content.type: text
content.value: Some useful links below:
content.type: new_line
content.type: new_line
content.raw: AQLM repo: 
content.type: text
content.value: AQLM repo: 
content.href: https://github.com/Vahe1994/AQLM
content.raw: https://github.com/Vahe1994/AQLM
content.type: link
content.type: new_line
content.raw: How to use AQLM & transformers: 
content.type: text
content.value: How to use AQLM & transformers: 
content.href: https://huggingface.co/docs/transformers/quantization#aqlm
content.raw: https://huggingface.co/docs/transformers/quantization#aqlm
content.type: link
content.type: new_line
content.raw: How to use AQLM & PEFT: 
content.type: text
content.value: How to use AQLM & PEFT: 
content.href: https://huggingface.co/docs/peft/developer_guides/quantization#aqlm-quantizaion
content.raw: https://huggingface.co/docs/peft/developer_guides/quantization#aqlm-quantizaion
content.type: link
content.type: new_line
content.type: new_line
content.raw: Great work from 
content.type: text
content.value: Great work from 
content.raw: @BlackSamorez
content.type: mention
content.user: BlackSamorez
content.raw:  and team !
content.type: text
content.value:  and team !
rawContent: Check out quantized weights from ISTA-DAS Lab directly in their organisation page: https://huggingface.co/ISTA-DASLab ! With official weights of AQLM (for 2bit quantization) & QMoE (1-bit MoE quantization)
  
  Read more about these techniques below:
  
  AQLM paper: https://hf.co/papers/2401.06118
  QMoE: https://huggingface.co/papers/2310.16795
  
  Some useful links below:
  
  AQLM repo: https://github.com/Vahe1994/AQLM
  How to use AQLM & transformers: https://huggingface.co/docs/transformers/quantization#aqlm
  How to use AQLM & PEFT: https://huggingface.co/docs/peft/developer_guides/quantization#aqlm-quantizaion
  
  Great work from @BlackSamorez and team !
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