A variety of technical solutions to cope with the lack of video memory
Search-R1 provides the following solutions to the video memory limitation problem:
- LoRA tuning techniques::
- Reduces 70% video memory footprint by fine-tuning only the adapter layer parameters
- modifications
train_ppo.shhit the nail on the head--use_lora trueparameterization
- gradient checkpoint::
- Reducing graphics memory requirements through a time-for-space strategy
- set up
gradient_checkpointing=True
- Mixed precision training::
- Mixed precision using FP16/FP32
- Enable it in the configuration file
fp16: true
- batch optimization::
- align
per_device_train_batch_sizeparameters - It is recommended that the initial value be set to 4 and adjusted according to the video memory.
- align
Emergency Response Program:
- Example of A100 with Colab Pro+ (40GB video memory)
- Segmentation of network layers using model parallelism
- For the Llama3-3B model, the recommended minimum configuration is 24GB of video memory
Note: This can be done bynvidia-smicommand to monitor the video memory usage in real time.
This answer comes from the articleSearch-R1: A Tool for Reinforcement Learning to Train Large Models for Search and ReasoningThe
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