78 lines
3.9 KiB
Markdown
78 lines
3.9 KiB
Markdown
# LoRA (Low-Rank Adaptation)
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## What is LoRA?
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LoRA is a technique that allows for efficent fine-tuning a model while only updating a small portion of the model's weights. This is useful when you have a large model that has been pre-trained on a large dataset, but you want to fine-tune it on a smaller dataset or for a specific task.
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LoRA works by adding a small number of additional weights to the model, which are used to adapt the model to the new dataset or task. These additional weights are learned during the fine-tuning process, while the rest of the model's weights are kept fixed.
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## How is it used?
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LoRA can be used in many ways and the community is always finding new ways to use it. Here are some examples of how you can use LoRA:
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Technically, LoRA can be used to fine-tune a large language model on a small dataset. However, these use cases can span a wide range of applications, such as:
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- fine-tuning a language model on a small dataset
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- fine-tuning a language model on a domain-specific dataset
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- fine-tuning a language model on a dataset with limited labels
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## Optimizing Inference with LoRA
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LoRA's can be used during inference by mutliplying the adapter weights with the model weights at each specified layer. This process can be computationally expensive, but due to awesome work by [punica-ai](https://github.com/punica-ai/punica) and the [lorax](https://github.com/predibase/lorax) team, optimized kernels/and frameworks have been developed to make this process more efficient. TGI leverages these optimizations in order to provide fast and efficient inference with mulitple LoRA models.
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## Serving multiple LoRA adapters with TGI
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Once a LoRA model has been trained, it can be used to generate text or perform other tasks just like a regular language model. However, because the model has been fine-tuned on a specific dataset, it may perform better on that dataset than a model that has not been fine-tuned.
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In practice its often useful to have multiple LoRA models, each fine-tuned on a different dataset or for a different task. This allows you to use the model that is best suited for a particular task or dataset.
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Text Generation Inference (TGI) now supports loading multiple LoRA models at startup that can be used in generation requests. This feature is available starting from version `~2.0.6` and is compatible with LoRA models trained using the `peft` library.
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### Specifying LoRA models
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To use LoRA in TGI, when starting the server, you can specify the list of LoRA models to load using the `LORA_ADAPTERS` environment variable. For example:
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```bash
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LORA_ADAPTERS=predibase/customer_support,predibase/dbpedia
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```
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additionally, you can specify the path to the LoRA models using the `LORA_ADAPTERS_PATH` environment variable. For example:
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```bash
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LORA_ADAPTERS=myadapter=/some/path/to/adapter,myadapter2=/another/path/to/adapter
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```
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note it's possible to mix adapter_ids with adapter_id=adapter_path e.g.
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```bash
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LORA_ADAPTERS=predibase/dbpedia,myadapter=/path/to/dir/
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```
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In the server logs, you will see the following message:
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```txt
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Loading adapter weights into model: predibase/customer_support
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Loading adapter weights into model: predibase/dbpedia
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```
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## Generate text
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You can then use these models in generation requests by specifying the `lora_model` parameter in the request payload. For example:
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```json
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curl 127.0.0.1:3000/generate \
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-X POST \
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-H 'Content-Type: application/json' \
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-d '{
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"inputs": "Hello who are you?",
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"parameters": {
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"max_new_tokens": 40,
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"adapter_id": "predibase/customer_support"
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}
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}'
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```
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> **Note:** The Lora feature is new and still being improved. If you encounter any issues or have any feedback, please let us know by opening an issue on the [GitHub repository](https://github.com/huggingface/text-generation-inference/issues/new/choose). Additionally documentation and an improved client library will be published soon.
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An updated tutorial with detailed examples will be published soon. Stay tuned!
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