You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Sadly, the lm loss is NaN if I use rotary positional embedding.
When I disable rotary positional embedding, the loss is ok even other parameters/arguments are the same as before.
The text was updated successfully, but these errors were encountered:
I would like to finetune llama2 on long sequence data. (more than or eq 32K)
I follow the example below for sequence parallel:
https://github.com/microsoft/Megatron-DeepSpeed/blob/main/examples_deepspeed/deepspeed4science/megatron_long_seq_support/pretrain_gpt_30B_seq_parallel.sh
Sadly, the lm loss is NaN if I use rotary positional embedding.
When I disable rotary positional embedding, the loss is ok even other parameters/arguments are the same as before.
The text was updated successfully, but these errors were encountered: