QLoRA uses 4-bit quantization of the base model to drastically reduce the GPU memory consumed by the base model while using LoRA for parameter-efficient fine-tuning. The command to use QLoRA is present at run_peft.sh.

Note:

At present, use_reentrant needs to be True when using gradient checkpointing with QLoRA or else QLoRA leads to high GPU memory consumption.

Single GPU SFT with QLoRA using Unsloth

Unsloth enables finetuning Mistral/Llama 2-5x faster with 70% less memory. It achieves this by reducing data upcasting, using Flash Attention 2, custom Triton kernels for RoPE embeddings, RMS Layernorm & Cross Entropy Loss and manual clever autograd computation to reduce the FLOPs during QLoRA finetuning. Below is the list of the optimizations from the Unsloth blogpost mistral-benchmark. The command to use QLoRA with Unsloth is present at run_unsloth_peft.sh.

Optimization in Unsloth to speed up QLoRA finetuning while reducing GPU memory usage

Multi-GPU SFT with QLoRA

To speed up QLoRA finetuning when you have access to multiple GPUs, look at the launch command at run_peft_multigpu.sh. This example to performs DDP on 8 GPUs.

Note:

At present, use_reentrant needs to be False when using gradient checkpointing with Multi-GPU QLoRA else it will lead to errors. However, this leads to huge GPU memory consumption.

Multi-GPU SFT with LoRA and DeepSpeed

When you have access to multiple GPUs, it would be better to use normal LoRA with DeepSpeed/FSDP. To use LoRA with DeepSpeed, refer to the docs at PEFT with DeepSpeed.

Multi-GPU SFT with LoRA and FSDP

When you have access to multiple GPUs, it would be better to use normal LoRA with DeepSpeed/FSDP. To use LoRA with FSDP, refer to the docs at PEFT with FSDP.

Note: FSDP is currently not compatible with 8bit bitsandbytes quantization.

Multi-GPU SFT with LoRA and FSDP for GPT-QModel:

As in Multi-GPU SFT with LoRA and FSDP, we also support other quantization methods like GPT-QModel. You may need to install GPT-QModel >= v7.0.0 or from source. Here is the launch command for reference: [run_peft_fsdp_gptq.sh]. For the --model_name_or_path argument, it is important to pass a model that is already quantized with GPT-QModel, like "hugging-quants/Meta-Llama-3.1-8B-Instruct-GPTQ-INT4".

Note: there is a bug in transformers v4.53.0 for this case, please skip this transformers version.

Tip

Generally try to upgrade to the latest package versions for best results, especially when it comes to bitsandbytes, accelerate, transformers, trl, and peft.