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Update readme.md

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qianyu chen
2024-07-17 10:10:36 +08:00
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finetune/readme.md
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@@ -80,12 +80,18 @@ sh finetune_lora.sh
After training, you could load the model with the path to the adapter. We advise you to use absolute path for your pretrained model. This is because LoRA only saves the adapter and the absolute path in the adapter configuration json file is used for finding out the pretrained model to load.
```
from peft import AutoPeftModel
from peft import PeftModel
from transformers import AutoModel
model_type="openbmb/MiniCPM-Llama3-V-2_5" # or openbmb/MiniCPM-V-2
path_to_adapter="path_to_your_fine_tuned_checkpoint"
model = AutoPeftModel.from_pretrained(
# path to the output directory
model = AutoModel.from_pretrained(
model_type,
trust_remote_code=True
)
lora_model = PeftModel.from_pretrained(
model,
path_to_adapter,
device_map="auto",
trust_remote_code=True