Comments (8)
Hi, Artnoage. If you check the training log I actually resumed the process twice and did not notice any memory error. I am not sure why that is the case on your end.
Thanks for your suggestions on opening a discord. I think I will open one soon.
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Yes I already knew that. I was just thinking maybe it had to do with the size of the network. like some missed parameter. When you did the first run, did you check the memory usage? If it is not a bit issue please leave the question open for a while, in case someone else tries it.
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When you did the first run, did you check the memory usage?
The memory usage is always 39G on my end.
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Hi! My training crashed, and I couldn't find the code to resume training from the last saved checkpoint. How can I resume my training? How do you handle this?
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Hi! My training crashed, and I couldn't find the code to resume training from the last saved checkpoint. How can I resume my training? How do you handle this?
You can add --resume your_checkpoint.pth
term in your pretraining command to resume training
from tinyllama.
Hi! My training crashed, and I couldn't find the code to resume training from the last saved checkpoint. How can I resume my training? How do you handle this?
You can add
--resume your_checkpoint.pth
term in your pretraining command to resume training
thanks, i found that
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@artnoage can you please post this project I would like to try to he same with 2 4060 training at home.
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Related Issues (20)
- Should this line use args.seed instead of seed=42?
- Pretraining failing on IndexError: list index out of range in file packed_dataset.py HOT 1
- A question on learning rate decay schedule HOT 1
- Why FSDP not DPP?
- On the visualization of Wandb in fine-tuning
- Is there any simple demo of fine-tuning TinyLlama HOT 4
- More intermediate checkpoints in < 240k steps
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- 模型和代码欢迎发布到wisemodel.cn开源社区
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- A potential bug in multi-GPU training HOT 1
- Where is the pretraing example of llama-1.1b-chat
- Would it be possible to provide help with evaluation?
- On which will it run better
- Training Run - New Tokenizer HOT 1
- Llama 3 HOT 2
- model结构 HOT 1
- model.py HOT 1
- Clarify Chinese support or not on README HOT 1
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