Benefits of 
text
Benefits of 
imatrix
`imatrix`
inline_code
 quantization in place of quip#
text
 quantization in place of quip#
new_line
new_line
Quip-# is a quantization method proposed by [Cornell-RelaxML](
text
Quip-# is a quantization method proposed by [Cornell-RelaxML](
https://github.com/Cornell-RelaxML
https://github.com/Cornell-RelaxML
link
) that claims tremendous performance gains using only 2-bit precision.
text
) that claims tremendous performance gains using only 2-bit precision.
new_line
new_line
RelaxML proposes that quantizing a model from 16 bit to 2 bit precision they can utilize Llama-2-70B on a single 24GB GPU.
text
RelaxML proposes that quantizing a model from 16 bit to 2 bit precision they can utilize Llama-2-70B on a single 24GB GPU.
new_line
new_line
QuIP# aims to revolutionize model quantization through a blend of incoherence processing and advanced lattice codebooks. By switching to a Hadamard transform-based incoherence approach, QuIP# enhances GPU efficiency, making weight matrices more Gaussian-like and ideal for quantization with its improved lattice codebooks.
text
QuIP# aims to revolutionize model quantization through a blend of incoherence processing and advanced lattice codebooks. By switching to a Hadamard transform-based incoherence approach, QuIP# enhances GPU efficiency, making weight matrices more Gaussian-like and ideal for quantization with its improved lattice codebooks.
new_line
new_line
This new method has already seen some adoption by projects like llama.cpp. The use of the Quip-# methodology has been implemented in the form of imatrix calculations. The importance matrix is calculated from a dataset such as wiki.train.raw and will output the perplexity on the given dataset.
text
This new method has already seen some adoption by projects like llama.cpp. The use of the Quip-# methodology has been implemented in the form of imatrix calculations. The importance matrix is calculated from a dataset such as wiki.train.raw and will output the perplexity on the given dataset.
new_line
new_line
This interim step can improve the results of the quantized model. If you would like to explore this process for yourself:
text
This interim step can improve the results of the quantized model. If you would like to explore this process for yourself:
new_line
new_line
llama.cpp - 
text
llama.cpp - 
https://github.com/ggerganov/llama.cpp/
https://github.com/ggerganov/llama.cpp/
link
new_line
Quip# paper - 
text
Quip# paper - 
https://cornell-relaxml.github.io/quip-sharp/
https://cornell-relaxml.github.io/quip-sharp/
link
new_line
AutoQuip# colab -  
text
AutoQuip# colab -  
https://colab.research.google.com/drive/1rPDvcticCekw8VPNjDbh_UcivVBzgwEW?usp=sharing
https://colab.research.google.com/drive/1rPDvcticCekw8VPNjDbh_UcivVBzgwEW?usp=sharing
link
new_line
new_line
Other impressive quantization projects to watch:
text
Other impressive quantization projects to watch:
new_line
+ AQLM
text
+ AQLM
new_line
https://github.com/Vahe1994/AQLM
https://github.com/Vahe1994/AQLM
link
new_line
https://arxiv.org/abs/2401.06118
https://arxiv.org/abs/2401.06118
link
new_line
Benefits of `imatrix` quantization in place of quip#

Quip-# is a quantization method proposed by [Cornell-RelaxML](https://github.com/Cornell-RelaxML) that claims tremendous performance gains using only 2-bit precision.

RelaxML proposes that quantizing a model from 16 bit to 2 bit precision they can utilize Llama-2-70B on a single 24GB GPU.

QuIP# aims to revolutionize model quantization through a blend of incoherence processing and advanced lattice codebooks. By switching to a Hadamard transform-based incoherence approach, QuIP# enhances GPU efficiency, making weight matrices more Gaussian-like and ideal for quantization with its improved lattice codebooks.

This new method has already seen some adoption by projects like llama.cpp. The use of the Quip-# methodology has been implemented in the form of imatrix calculations. The importance matrix is calculated from a dataset such as wiki.train.raw and will output the perplexity on the given dataset.

This interim step can improve the results of the quantized model. If you would like to explore this process for yourself:

llama.cpp - https://github.com/ggerganov/llama.cpp/
Quip# paper - https://cornell-relaxml.github.io/quip-sharp/
AutoQuip# colab -  https://colab.research.google.com/drive/1rPDvcticCekw8VPNjDbh_UcivVBzgwEW?usp=sharing

Other impressive quantization projects to watch:
+ AQLM
https://github.com/Vahe1994/AQLM
https://arxiv.org/abs/2401.06118

👍
nivu clem merve osanseviero ybelkada samusenps NeuralNovel Ji-Ha hllj DamarJati sayhan NovoCode sugatoray 1TuanPham victor John6666 louisbrulenaudet
❤️
clem merve osanseviero ybelkada NeuralNovel Gatozu35 akjindal53244 sayhan NovoCode KingNish