Comments (3)
You are right, I tend to train with use_cache=False and didnt see it, It must be fixed now with latest commit, do a git pull
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thanks for your reply. I use two 40G A100 and got cuda out of memory...
i am wondering how many gpu you used when training?
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Refer to #4 , The pre-trained model may barely fit in an A100 40GB with 1 batch size , bf16 and an 8-bit optimizer. Otherwise if you have multiple A100s in parallel i suggest either mapping the model on the GPU or using FSDP with Accelerate. Lastly you could create a QwenConfig with less parameters but you will have to pretrain it on billions of tokens before you get some good performance. You can use the 🤗 Model Memory Calculator to check how much vram the model needs.
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