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Docs / LoRA: Add more information on merge_and_unload docs #1805

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May 28, 2024
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Docs / LoRA: Add more information on merge_and_unload docs #1805

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put back lora merging diagram
younesbelkada May 28, 2024
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younesbelkada May 28, 2024
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Update docs/source/developer_guides/lora.md
younesbelkada May 28, 2024
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10 changes: 9 additions & 1 deletion docs/source/developer_guides/lora.md
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Expand Up @@ -140,10 +140,18 @@ Assuming the original model had 5 layers `[0, 1, 2 ,3, 4]`, this would create a
[Fewshot-Metamath-OrcaVicuna-Mistral-10B](https://huggingface.co/abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B) is an example of a model trained using this method on Mistral-7B expanded to 10B. The
[adapter_config.json](https://huggingface.co/abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B/blob/main/adapter_config.json) shows a sample LoRA adapter config applying this method for fine-tuning.

## Merge adapters
## Merge LoRA weights into the base model

While LoRA is significantly smaller and faster to train, you may encounter latency issues during inference due to separately loading the base model and the LoRA adapter. To eliminate latency, use the [`~LoraModel.merge_and_unload`] function to merge the adapter weights with the base model. This allows you to use the newly merged model as a standalone model. The [`~LoraModel.merge_and_unload`] function doesn't keep the adapter weights in memory.

Below is a diagram that explains the intuition of LoRA adapter merging:

<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png"/>
</div>

We show in the snippets below how to run that using PEFT.

```py
from transformers import AutoModelForCausalLM
from peft import PeftModel
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