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[Misc] Optimize Qwen2-VL LoRA test #11663
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Signed-off-by: Jee Jee Li <[email protected]>
Signed-off-by: Jee Jee Li <[email protected]>
Signed-off-by: Jee Jee Li <[email protected]>
👋 Hi! Thank you for contributing to the vLLM project. Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging. To run CI, PR reviewers can do one of these:
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@@ -7,7 +7,7 @@ | |||
from vllm.lora.request import LoRARequest | |||
from vllm.platforms import current_platform | |||
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MODEL_PATH = "Qwen/Qwen2-VL-7B-Instruct" | |||
MODEL_PATH = "Qwen/Qwen2-VL-2B-Instruct" | |||
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I switched to the 2B model to reduce training time and decrease the computational pressure on CI testing pipeline.
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I assume this fixes the test?
Signed-off-by: Jee Jee Li <[email protected]>
I tested it on the A800 and it works without any issues. I used 100 samples for testing, and the generated results align perfectly with transformers. Regarding the previous LoRA, there might have been an issue during training. Although it could generate reasonable results, only about 30% of the outputs aligned correctly |
I retrained a QWen-VL LoRA. In my local environment using A800, I tested the model on 100 image samples, the generation results were completely aligned with the
transformers
output.BTW, add LoRA support for vision modules and projecter modules