Question 1

What is the difference between fine-tuning and RAG?

Accepted Answer

RAG retrieves relevant documents and feeds them to an LLM as context for answering. Fine-tuning actually updates the model's weights to teach it new styles, tones, formatting rules, or deep domain terminology.

Question 2

What are LoRA and QLoRA?

Accepted Answer

LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) are parameter-efficient tuning methods. Instead of training all model parameters, they train a tiny subset of weights, reducing GPU memory requirements and training costs by up to 90%.

Question 3

Where are the fine-tuned models hosted?

Accepted Answer

We deploy models on your private cloud (AWS, GCP, Azure) or local servers using serving engines like vLLM or TGI. You maintain 100% control over model files and inputs.

Question 4

How much data is required for fine-tuning?

Accepted Answer

For basic instruction tuning or tone matching, a high-quality dataset of 1,000 to 5,000 conversational pairs is sufficient. Domain adaptation typically requires larger text datasets.

Question 5

Which base models do you recommend?

Accepted Answer

We recommend top-performing open-source base models like Meta's Llama 3, Mistral 7B, or Mixtral 8x7B, depending on your performance goals and GPU budget.

Question 6

Can we fine-tune a model to generate structured outputs?

Accepted Answer

Yes, we tune models specifically to output valid JSON, XML, or SQL queries, ensuring reliable integration into your databases and application APIs.

Question 7

How do you evaluate fine-tuned models?

Accepted Answer

We create validation datasets and measure metrics like perplexity, task accuracy, and formatting compliance. We also use LLM-as-a-judge frameworks to evaluate tone consistency.

Question 8

What hardware is required to run these models?

Accepted Answer

A quantized 7B model can run on a single consumer GPU (like NVIDIA RTX 4090 or A10G). Larger models require multi-GPU setups (like A100 or H100 instances) for high concurrency.

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