Comments (3)
@TrueNobility303 Can you elaborate on the question? The example given here should be a standard application of LoRA.
from lorax.
Thanks a lot for your lorax implementation! Now I know more about how to use it from your example
My original question is: given a transformer, how can I set only the Q, and V in attention layers to be LORA_FULL
, while the other parameters LORA_FREEZE
?
I think I have found the solution to my original question from the example you gave.
It seems that I should define my decision_fn
and create a pytree with the same shape as params indicating how each parameter should be handled as. Then apply
lora_spec = lorax.simple_spec(model.params, decision_fn=decision_fn, tune_vectors=True)
lora_params = lorax.init_lora(model.params, lora_spec, jax.random.PRNGKey(0))
Thank you so much again for your sharing!
from lorax.
Yup! Basically you can just specify which parameters you want it to affect in the decision function.
from lorax.
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from lorax.