Optimized implementation scenarios in a low-resource environment
For development environments with limited video memory, the VLM-R1 provides a variety of resource optimization solutions:
- Memory Saving Technology::
- Enable Flash Attention optimization (already configured automatically in setup.sh)
- Using Deepspeed's Zero-3 optimization strategy (local_scripts/zero3.json)
- Adjustment of key parameters::
- Reduce -num_generations from the default 8 to 2-4
- Set -per_device_train_batch_size=1 with -gradient_accumulation_steps=4
- Enabling -bf16 saves about 30% memory compared to fp32.
- alternative::
- T4 GPU Runtime with Colab Pro
- Knowledge distillation for the Qwen2.5-VL model
- Load only some layers of the model for task-specific fine-tuning
The -half_precision parameter of src/eval/test_rec_r1.py can be used during the test phase to further reduce the memory footprint.
This answer comes from the articleVLM-R1: A Visual Language Model for Localizing Image Targets through Natural LanguageThe
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