Cake for Fine Tuning

Overview

Generic foundation models are powerful, but they’re not personalized. Fine-tuning unlocks that final 10x—adapting base models to your industry, tone, workflows, or use case. The challenge? Doing it cost-effectively, securely, and with reproducibility.

Cake provides a cloud-agnostic fine-tuning stack built entirely on open source. Use Hugging Face models and tokenizers, run experiments with PyTorch and MLflow, and orchestrate workflows with Kubeflow Pipelines. You can fine-tune LLMs or vision models using your own private datasets, with full observability, lineage, and governance support.

Because Cake is modular and composable, you can bring in the latest open-source fine-tuning tools (like PEFT, LoRA, or QLoRA) without waiting for a platform update. And by running in your environment, you cut compute costs and avoid sharing sensitive data with third-party APIs.

Key benefits

• Fine-tune securely and privately: Keep data in your environment while adapting open-source models to your needs.
  
  
• Reduce compute and licensing costs: Use optimized workflows and control your infrastructure footprint.
  
  
• Integrate the latest fine-tuning tools: Stay current with new methods like LoRA, QLoRA, and PEFT.
  
  
• Track experiments and improve performance: Version datasets, configs, and results with full traceability.
  
  
• Deploy anywhere: Run fine-tuned models across clouds, regions, or edge environments—without retooling.