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Update model_minicpmv.py for latest compatibility (#174)

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qianyu chen
2024-05-29 16:03:59 +08:00
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@@ -107,6 +107,31 @@ The following table presents the memory usage of the model when fine-tuning usin
- **Out of memory**: Indicates that the memory was insufficient for full parameters fine-tuning under the current GPU configurations.
### Finetuning FAQs
<details>
<summary>Q: Encounter an error while using the AutoPeftModelForCausalLM to load a checkpoint that has undergone lora fine-tuning</summary>
A: The error as described in [issues 168](https://github.com/OpenBMB/MiniCPM-V/issues/168) occurs because the model lacks `get_input_embeddings` and `set_input_embeddings` methods. Follow these steps to resolve this issue:
1.**Reload the Fine-Tuned Model:** Make sure you correctly load the checkpoint that has been fine-tuned using lora techniques. Use the following code example to guide you:
```python
from peft import AutoPeftModelForCausalLM
model = AutoPeftModelForCausalLM.from_pretrained(
'path_to_your_fine_tuned_checkpoint', # Path to your fine-tuned checkpoint directory
output='output/minicpmv2_lora',
device_map='auto',
trust_remote_code=True
).eval()
```
2.**Update the `model_minicpmv.py` File:**
- **Verification:** Make sure you verify and update your `model_minicpmv.py` file to ensure it is the latest version.
- **Update Hugging Face Library Code:** If the issue persists after updating the file, consider updating the related code in the Hugging Face library.
- **Direct File Copy:** For a quick resolution, directly download and copy the latest `model_minicpmv.py` file into your project. This file is available from the following sources:
- [MiniCPM-Llama3-V-2_5 on Hugging Face](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5/tree/main)
- [MiniCPM-V-2 on Hugging Face](https://huggingface.co/openbmb/MiniCPM-V-2)
</details>
<details>
<summary>Q: How do I use the `flash_attention_2` implementation when loading a pretrained model?</summary>