Components Qlora

Qlora on Cake

QLoRA is a method for fine-tuning large language models efficiently by combining quantization and low-rank adapters. It reduces hardware requirements without compromising model performance.

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Cake cut a year off our product development cycle. That's the difference between life and death for small companies

Dan Doe
President, Altis Labs

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Cake cut a year off our product development cycle. That's the difference between life and death for small companies

Jane Doe
CEO, AMD

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Cake cut a year off our product development cycle. That's the difference between life and death for small companies

Michael Doe
Vice President, Test Company

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How it works

Fine-tune LLMs efficiently with QLoRA + Cake

Cake automates the setup and orchestration of QLoRA, letting you fine-tune large language models using quantization and low-rank adaptation. You get the benefits of reduced memory usage and training time without sacrificing performance.

Memory-efficient fine-tuning at scale

Use Cake to integrate QLoRA into your training workflows with minimal GPU requirements. Fine-tune massive models on consumer hardware or scaled infrastructure with precision.

Seamless integration with experiment tracking

Log hyperparameters, run metrics, and checkpoint states directly from QLoRA jobs using Cake-native integrations with MLflow, Weights & Biases, or your preferred tracking tool.

Compatible with multiple LLM frameworks

QLoRA under Cake can be used with Hugging Face Transformers, DeepSpeed, and other popular frameworks — all orchestrated and monitored through your unified platform.

Frequently asked questions about Cake and Qlora

What is QLoRA and how does it work?

QLoRA (Quantized Low-Rank Adapter) is a method for fine-tuning large language models efficiently. It combines 4-bit quantization with parameter-efficient fine-tuning using low-rank adapters (LoRAs), reducing the memory footprint and compute needed to customize LLMs.

How does Cake integrate with QLoRA?

Cake automates QLoRA fine-tuning jobs, manages the required dependencies (like bitsandbytes and PEFT), and orchestrates the training pipeline across your preferred infrastructure. You can schedule jobs, monitor runs, and log results without writing boilerplate code.

What types of models can I fine-tune with QLoRA?

QLoRA supports most Transformer-based models, including LLaMA, Falcon, and Mistral. Cake provides prebuilt adapters and configuration templates to help you start quickly with compatible base models from Hugging Face.

Can I use QLoRA with consumer-grade GPUs?

Yes. One of QLoRA’s biggest strengths is its ability to fine-tune large models on limited hardware (e.g., 24–48GB VRAM GPUs). Cake automatically applies quantization-aware configurations to ensure smooth execution within your hardware limits.

Does QLoRA support multi-GPU or distributed training?

Yes. With Cake, you can configure distributed QLoRA runs using DeepSpeed or Accelerate. Cake handles resource allocation and orchestration so you can scale fine-tuning across multiple GPUs or nodes.