@@ -143,7 +143,10 @@ We also offer Monkey's model definition and training code, which you can explore
**ATTENTION:** Specify the path to your training data, which should be a json file consisting of a list of conversations.
Inspired by Qwen-VL, we froze the Large Language Model (LLM) and introduced LoRA into four linear layers ```"c_attn", "attn.c_proj", "w1", "w2"``` for training. This step makes it possible to train Monkey using 8 NVIDIA 3090 GPUs. The specific implementation code is in ```model_qwen_new.py```. When using it, just replace the contents of ```model_qwen.py``` with the contents of ```model_qwen_new.py```.
Inspired by Qwen-VL, we freeze the Large Language Model (LLM) and introduce LoRA into four linear layers ```"c_attn", "attn.c_proj", "w1", "w2"``` for training. This step makes it possible to train Monkey using 8 NVIDIA 3090 GPUs. The specific implementation code is in ```model_qwen_new.py```.
- Add LoRA: You need to replace the contents of ```model_qwen.py``` with the contents of ```model_qwen_new.py```.
- Freeze LLM: You need to freeze other modules except LoRA and Resampler modules in ```finetune_multitask.py```.
