I will fine tune llms with lora, qlora and hugging face

LoRA is a parameter-efficient fine-tuning technique that adapts a large language model by training a relatively small number of additional parameters instead of updating the entire model. This significantly reduces GPU memory and training requirements.

QLoRA combines quantization with LoRA so large models can be fine-tuned using substantially less GPU memory while retaining much of their original capability. It is especially useful when compute resources are limited.

I can work with many transformer-based models that support practical fine-tuning through the Hugging Face ecosystem. Please send me the exact model or your requirements before ordering because hardware, architecture, licensing, and dataset size can affect feasibility.

Yes, where the selected model, hardware, licensing, and project requirements allow it. I will recommend the model size and fine-tuning strategy based on your task and available compute.

Yes. Fine-tuning requires suitable training data. If your dataset needs light restructuring or formatting, that can be included depending on the package.

It depends on the task and quality of the examples. A small, high-quality dataset can sometimes outperform a much larger noisy dataset. I will evaluate the dataset before training and recommend whether it is suitable.

No responsible AI engineer should guarantee that before testing. Performance depends on the base model, dataset quality, dataset size, training configuration, evaluation method, and target task. That is why evaluation is included in my workflow.

They solve different problems. RAG is usually appropriate when the model needs access to changing or private knowledge. Fine-tuning is useful when you want to change model behavior, domain adaptation, style, terminology, output format, or task performance. Some systems benefit from both.

Yes. The Premium package can include an inference API, Docker configuration, deployment documentation, and integration guidance. Cloud infrastructure and GPU hosting costs are separate.

Any significant third-party GPU, API, storage, or hosting costs are paid by the client unless explicitly included in the custom offer. I will discuss expected compute requirements before training.