Comments (2)
Good call, we should probably document this better. Say you have 4 GPUs, and you only want to use the first 2, you can do
CUDA_VISIBLE_DEVICES=0,1 litgpt finetune --checkpoint_dir checkpoints/microsoft/phi-2 --devices 2
or the 1st and last GPU:
CUDA_VISIBLE_DEVICES=0,3 litgpt finetune --checkpoint_dir checkpoints/microsoft/phi-2 --devices 2
from litgpt.
Good call, we should probably document this better. Say you have 4 GPUs, and you only want to use the first 2, you can do
CUDA_VISIBLE_DEVICES=0,1 litgpt finetune --checkpoint_dir checkpoints/microsoft/phi-2 --devices 2or the 1st and last GPU:
CUDA_VISIBLE_DEVICES=0,3 litgpt finetune --checkpoint_dir checkpoints/microsoft/phi-2 --devices 2
It works! very thankful!
from litgpt.
Related Issues (20)
- Create new CI API key HOT 1
- Some confusion about weight conversion, as I need to use other engineering to evaluate my LLM HOT 2
- Upgrade LitData
- validation output during finetuning HOT 3
- mistralai/Mistral-7B-v0.3 support HOT 4
- How to set max_iters HOT 5
- Specify cache for huggingface openwebtext download HOT 1
- Training lasts just 150 seconds for TinyLlama OpenWebtext dataset
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- Using custom data for `Continue pretraining an LLM` HOT 4
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- Finetune lora max_seq_length error HOT 4
- Continue finetuning HOT 2
- Support non-int batch_size argument "auto" with litgpt evaluate HOT 3
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- Gradient Accumulation Step under Multi-node Pretaining HOT 8
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