Q: What are the reasons not to fine tune an LLM?

Reasons not to fine-tune an LLM include: Cost: Fine-tuning requires significant computational resources and time. Data limitations: Small datasets can lead to overfitting. Complexity: Managing fine-tuning and updates can be challenging. Sufficient pretrained capability: Many tasks can be handled effectively using prompt engineering or adapters. Model rigidity: Fine-tuning alters the base model, making it less flexible for other tasks.

Question 1

What is LoRA or QLoRA?

Accepted Answer

LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) are parameter-efficient fine-tuning methods that reduce resource demands. LoRA adds small, trainable low-rank matrices to the model while keeping the original weights frozen, making fine-tuning efficient. QLoRA extends this by fine-tuning a quantized (compressed) version of the model, further lowering memory and computational requirements without significant loss in performance.

Question 2

What is LoRa’s disadvantage?

Accepted Answer

One big disadvantage of LoRA is that it may not achieve the same performance improvement as full fine-tuning, especially for tasks requiring deep modifications to the model’s behavior. Since LoRA only adapts a small set of parameters (the low-rank matrices), it might not fully capture complex patterns or task-specific nuances compared to traditional fine-tuning.

Question 3

What is the difference between RAG and fine-tuning LLM?

Accepted Answer

The difference between RAG (retrieval-augmented generation) and fine-tuning an LLM is:

RAG: Combines the LLM with an external retrieval system to fetch relevant information dynamically, avoiding modifying the model.
Fine-tuning: Adjusts the model’s weights by training it on task-specific data, embedding knowledge into the model.

Question 4

What are the reasons not to fine tune an LLM?

Accepted Answer

Reasons not to fine-tune an LLM include:

Cost: Fine-tuning requires significant computational resources and time.
Data limitations: Small datasets can lead to overfitting.
Complexity: Managing fine-tuning and updates can be challenging.
Sufficient pretrained capability: Many tasks can be handled effectively using prompt engineering or adapters.
Model rigidity: Fine-tuning alters the base model, making it less flexible for other tasks.

Question 5

How many examples are needed to fine-tune an LLM?

Accepted Answer

The number of examples needed to fine-tune a large language model (LLM) depends on the task and the risk of overfitting:

Small-scale tasks (e.g., classification or Q&A): A few thousand high-quality examples can work but require techniques like regularization or LoRA to prevent overfitting.
Domain adaptation (e.g., legal or medical texts): Tens of thousands to a few hundred thousand examples are ideal for better generalization.
Full fine-tuning (general improvements): Many examples are typically required for robust performance.

Fine-tuning can still be effective with parameter-efficient methods for very small datasets (e.g., 100–1,000 examples), but care is needed to avoid overfitting.

Question 6

Can I use RAG and fine-tuning together?

Accepted Answer

RAG and fine-tuning can be used together. Fine-tuning can tailor the LLM to better understand specific tasks or domains, while RAG provides dynamic access to up-to-date or specialized external knowledge. This combination improves the model’s reasoning capabilities and ability to retrieve and incorporate relevant information.

Fine-Tuning LLMs Using LoRA and QLoRA Save for later

Fine-Tuning LLMs Using LoRA and QLoRA
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