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LoRA Adapters

Low-Rank Adaptation (LoRA) offers a resource-efficient way to fine-tune large language models (LLMs). Instead of updating all model parameters, LoRA injects small adapter modules that retain most of the base model’s capabilities while adding new, task-specific functionality.

1. Fundamentals

  • Traditional Fine-Tuning – Involves updating all model parameters, which is time-consuming and expensive.
  • LoRA Approach – Freezes base model weights, inserting lightweight adapter modules with fewer trainable parameters.
  • Outcome – A fine-tuned LLM that maintains base knowledge and adapts to new tasks with minimal overhead.

2. Enabling LoRA in Fine-Tuning

Simply toggle “LoRA” in the fine-tuning settings to activate adapter-based training. This reduces compute demands while still delivering a model specialized for your use case.

LoRA Adapters in Training

3. Configuring LoRA Hyperparameters

  1. LoRA_r

    • Sets the rank of the low-rank matrices.
    • Higher = more capacity to learn but higher risk of overfitting.

  2. LoRA Alpha

    • Scales the adapter weight updates.
    • Higher alpha = more aggressive training.

  3. LoRA Dropout

    • Applies dropout to adapter weights.
    • Helps prevent overfitting by randomly dropping parameters during training.

4. Storing LoRA-Only Weights

You can opt to store only adapter weights which are much smaller than a fully fine-tuned model. Simply toggle “Store only the LoRA Adapters” in the output settings. This reduces storage costs and simplifies deployment since you only need:

  1. The original base model.
  2. The compact LoRA adapter weights.

Store LoRA-Only

5. Deployment with LoRA Adapters

When deploying your model:

  1. Load the Base Model – Choose HF or S3 as your source.
  2. Enable LoRA – Toggle “Use LoRA Adapters.”
  3. Select the Adapter Weights – Provide the location of your LoRA weights.

LoRA in Deployment Configurations

This setup merges the base LLM with the LoRA adapters for a streamlined, memory-efficient deployment.

6. Knowledge Benchmarks with LoRA

Leverage LoRA adapters for domain-specific benchmarking:

  1. Load Base Model + LoRA Weights – Retrieve both from your desired source (S3 or HuggingFace).
  2. Check Credentials – Ensure read access via valid keys in the secrets blueprint.
  3. Run Benchmarks – Evaluate performance on specialized tasks or knowledge domains using your newly fine-tuned adapters.

Custom Knowledge Benchmarks

Next Steps