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🔥 Level up your model training w/ GaLore + Transformers for SOTA results on consumer-grade hardware!
🔥 Level up your model training w/ GaLore + Transformers for SOTA results on consumer-grade hardware!
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⬇️ 82.5% less optimizer state memory footprint without performance degradation by expressing the gradient weight matrix as low rank.
⬇️ 82.5% less optimizer state memory footprint without performance degradation by expressing the gradient weight matrix as low rank.
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👩🏿‍💻 Install via 
👩🏿‍💻 Install via 
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`pip install transformers>=4.39.0 galore-torch`
pip install transformers>=4.39.0 galore-torch
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. #ProudlyGpuPoor
. #ProudlyGpuPoor
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The integration of GaLore into the training of large language models (LLMs) marks a significant advancement in the field of deep learning, particularly in terms of memory efficiency and the democratization of AI research. By allowing for the training of billion-parameter models on consumer-grade hardware, reducing memory footprint in optimizer states, and leveraging advanced projection matrix techniques, GaLore opens new horizons for researchers and practitioners with limited access to high-end computational resources.
The integration of GaLore into the training of large language models (LLMs) marks a significant advancement in the field of deep learning, particularly in terms of memory efficiency and the democratization of AI research. By allowing for the training of billion-parameter models on consumer-grade hardware, reducing memory footprint in optimizer states, and leveraging advanced projection matrix techniques, GaLore opens new horizons for researchers and practitioners with limited access to high-end computational resources.
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🔬 Find out more about GaLore and investigate lots of juicy technical details: 
🔬 Find out more about GaLore and investigate lots of juicy technical details: 
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https://huggingface.co/blog/galore
https://huggingface.co/blog/galore
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🤗 Huge thanks to everyone involved ❤️:
🤗 Huge thanks to everyone involved ❤️:
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• authors: 
• authors: 
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@jiaweizhao
jiaweizhao
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@Kyriection
Kyriection
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@beidic
beidic
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 Zhangyang Wang  
 Zhangyang Wang  
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@animakumar
animakumar
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@tydsh
tydsh
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• community contributors: 
• community contributors: 
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@hiyouga
hiyouga
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@mdouglas
mdouglas
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 and others!
 and others!
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• 
• 
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@ybelkada
ybelkada
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  for taking such swift action in composing and coordinating necessary PRs to get this live at ⚡ speed!
  for taking such swift action in composing and coordinating necessary PRs to get this live at ⚡ speed!
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🏗️📈 Super rewarding to see how 
🏗️📈 Super rewarding to see how 
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@timdettmers
timdettmers
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 work with optimizers is being built upon to achieve even greater heights!
 work with optimizers is being built upon to achieve even greater heights!
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🚧 Actually, there are ongoing works to integrate GaLore into bitsandbytes and optimize memory efficiency even further 💪. We'll keep you posted!
🚧 Actually, there are ongoing works to integrate GaLore into bitsandbytes and optimize memory efficiency even further 💪. We'll keep you posted!
🔥 Level up your model training w/ GaLore + Transformers for SOTA results on consumer-grade hardware!

⬇️ 82.5% less optimizer state memory footprint without performance degradation by expressing the gradient weight matrix as low rank.

👩🏿‍💻 Install via `pip install transformers>=4.39.0 galore-torch`. #ProudlyGpuPoor

The integration of GaLore into the training of large language models (LLMs) marks a significant advancement in the field of deep learning, particularly in terms of memory efficiency and the democratization of AI research. By allowing for the training of billion-parameter models on consumer-grade hardware, reducing memory footprint in optimizer states, and leveraging advanced projection matrix techniques, GaLore opens new horizons for researchers and practitioners with limited access to high-end computational resources.

🔬 Find out more about GaLore and investigate lots of juicy technical details: https://huggingface.co/blog/galore

🤗 Huge thanks to everyone involved ❤️:

• authors: @jiaweizhao @Kyriection @beidic Zhangyang Wang  @animakumar @tydsh 
• community contributors: @hiyouga @mdouglas and others!
• @ybelkada  for taking such swift action in composing and coordinating necessary PRs to get this live at ⚡ speed!

🏗️📈 Super rewarding to see how @timdettmers work with optimizers is being built upon to achieve even greater heights!

🚧 Actually, there are ongoing works to integrate GaLore into bitsandbytes and optimize memory efficiency even further 💪. We'll keep you posted!
🔥
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