Comments (3)
QLoRA should work with DeepSpeed. FSDP should also work but may require the latest versions of the respectively involved libraries (bitsandbytes, transformers, trl, peft).
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Agree with you. But deepspeed ad FSDP are all data parallel. If I want to fine-tune much large model, like 130B llm, there will be OOM with 80G 4xA100. I think pipeline or tensor parallel is needed in this case.
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DS and FSDP are not just data parallel but model parallel. Regarding PP and TP, I'm not aware of working examples. This doesn't mean it can't work, but I wouldn't suspect it to run out of the box.
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Related Issues (20)
- model.print_trainable_parameters() is incomplete. HOT 5
- Merging models and feature extraction HOT 4
- TypeError: ChatGLMForConditionalGeneration.forward() got an unexpected keyword argument 'decoder_input_ids' HOT 4
- RuntimeError: "addmm_impl_cpu_" not implemented for 'Half HOT 11
- examples/sft/run_peft.sh model load dtype error HOT 7
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- LISA HOT 6
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- p_tuning layers disappear after loading peft model HOT 4
- Configuration issue HOT 11
- AttributeError: ModulesToSaveWrapper has no attribute layers HOT 2
- Prefix tuning configuration issue HOT 1
- More Input: If I use PyTorch Lightning for large model fine-tuning (LoRA) tasks, and I want to pass an additional parameter p outside the input-output pair (input, label), to be used in the Linear layer of LoRA for some special tasks during forward pass, how should I proceed? HOT 2
- LoRA layer param.grad=None after loss.backward() using lora for mamba 2.8B HOT 2
- peft v0.10 takes up too much GPU memory than v0.3.0 HOT 14
- Support HQQ method. HOT 11
- replace_lora_weights_loftq seems not present in transformers HOT 2
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