Towards batch sizes too small to meter🎉 beautiful work! And my personal favorite so far - I adore peak performance at small/nano scale. Everyone deserves to run/train AGI locally:) our data, our god model!
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Towards batch sizes too small to meter🎉 beautiful work! And my personal favorite so far - I adore peak performance at small/nano scale. Everyone deserves to run/train AGI locally:) our data, our god model!
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They showed that:
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They showed that:
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- you can train LLMs (upto 1B params) with as low as batch_size=1. This is unconventional given small batch sizes can lead to unstable/spiky training runs.
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- you can train LLMs (upto 1B params) with as low as batch_size=1. This is unconventional given small batch sizes can lead to unstable/spiky training runs.
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- you can have a stable train run with just vanilla SGD(stochastic gradient descent), no momentum required🤯
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- you can have a stable train run with just vanilla SGD(stochastic gradient descent), no momentum required🤯
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- small batch sizes are more robust to hyperparameters (i.e no worries with initialization)
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- small batch sizes are more robust to hyperparameters (i.e no worries with initialization)
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- smaller batch sizes outperforms (“better per-Flops performance”) larger batch sizes.
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- smaller batch sizes outperforms (“better per-Flops performance”) larger batch sizes.
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“We recommend that practitioners training large models in memory-constrained settings exploit the benefits of small batch sizes rather than trying to emulate the large batch size setting (e.g., through gradient accumulation) typically used in industry.” 
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“We recommend that practitioners training large models in memory-constrained settings exploit the benefits of small batch sizes rather than trying to emulate the large batch size setting (e.g., through gradient accumulation) typically used in industry.” 
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I’ve been doing this for ages - my mantra: all my experiments must scale on my 8gb ram m2 before moving to gpu. IOW I love being gpu poor. Checkout my nanoAI algo repo: 
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I’ve been doing this for ages - my mantra: all my experiments must scale on my 8gb ram m2 before moving to gpu. IOW I love being gpu poor. Checkout my nanoAI algo repo: 
https://github.com/Jaykef/ai-algorithms
https://github.com/Jaykef/ai-algorithms
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, all notebooks run on memory as low as 8gb ram
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, all notebooks run on memory as low as 8gb ram
Towards batch sizes too small to meter🎉 beautiful work! And my personal favorite so far - I adore peak performance at small/nano scale. Everyone deserves to run/train AGI locally:) our data, our god model!
They showed that:
- you can train LLMs (upto 1B params) with as low as batch_size=1. This is unconventional given small batch sizes can lead to unstable/spiky training runs.
- you can have a stable train run with just vanilla SGD(stochastic gradient descent), no momentum required🤯
- small batch sizes are more robust to hyperparameters (i.e no worries with initialization)
- smaller batch sizes outperforms (“better per-Flops performance”) larger batch sizes.

“We recommend that practitioners training large models in memory-constrained settings exploit the benefits of small batch sizes rather than trying to emulate the large batch size setting (e.g., through gradient accumulation) typically used in industry.” 

I’ve been doing this for ages - my mantra: all my experiments must scale on my 8gb ram m2 before moving to gpu. IOW I love being gpu poor. Checkout my nanoAI algo repo: https://github.com/Jaykef/ai-algorithms, all notebooks run on memory as low as 8gb ram
🔥
Clausss John6666 marinap FlameF0X MartinPGrozev Fishtiks codemaivanngu
👍
Phcogdai MartinPGrozev