Tuning Strategies for Resource-Constrained Environments
The following optimized combinations are recommended for devices with less than 16GB of memory:
- Model Selection
- Priority is given to the 8B version (with modifications)
inference.pyhit the nail on the head--model(Parameters) - Enabling 8-bit Quantization: Installation
bitsandbytespackage and add the--load_in_8bitparameters
- Priority is given to the 8B version (with modifications)
- computing acceleration
- Force Flash-Attention (specified during installation)
--no-build-isolation) - Limit inference batch size (setting)
--batch_size 1)
- Force Flash-Attention (specified during installation)
- memory management
- Enable gradient checkpoints: add the
gradient_checkpointing=True - Training with mixed precision: profile settings
fp16: true
- Enable gradient checkpoints: add the
- Emergency program: When an OOM error occurs
- Attempts to release the cache:
torch.cuda.empty_cache() - Reduce image resolution (modify resize parameter in preprocessing code)
- Attempts to release the cache:
real time data: The GTX 1060 graphics card is also optimized to run basic reasoning tasks smoothly.
This answer comes from the articleMM-EUREKA: A Multimodal Reinforcement Learning Tool for Exploring Visual ReasoningThe
May not be reproduced without permission:AI productivity tools " How to optimize MM-EUREKA's operational efficiency on memory-limited devices?
