Performance Optimization Schemes for Low Resource Environments
For devices with insufficient video memory, optimized operation can be achieved by the following methods:
- Model Selection Strategy: Priority is given to versions with 1.8B or 7B parameters; 13B/14B models require at least 40GB of video memory.
- Precision Adjustment: Changing torch.float16 to torch.float32 reduces speed but reduces video memory usage (saving about 20%)
- Batch Limits: set max_batch_size=1 and enable the -gpu False parameter
Advanced Optimization Tips:
- Using CleanTool to preprocess data and remove redundant dialog can improve efficiency by 15-20%
- Adjust the generate parameter: reduce temperature to 0.5, set max_new_tokens to 128 to relieve memory pressure.
- Uses model parallelism: assigns different layers to multiple GPUs via the device_map parameter
Alternatives:If you still can't meet it, you can apply for the Educational Institutions Partnership Channel to get access to the Cloud API.
This answer comes from the articleEduChat: Open Source Education Dialogue ModelThe
May not be reproduced without permission:AI productivity tools " How to optimize EduChat for low graphics memory GPU devices?
