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Cake for Fine Tuning

Customize LLMs and other foundation models for your domain using open-source fine-tuning pipelines on Cake. Save on compute, preserve privacy, and get production-ready faster without giving up control.

 

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Overview

Generic foundation models are powerful, but they’re not personalized. Fine-tuning unlocks that final 10x by adapting base models to your industry, tone, workflows, or use case. The challenge is 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 such as 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.

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Increase in
MLOps productivity

 

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Faster model deployment
to production

 

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Annual savings per
LLM project

THE CAKE DIFFERENCE

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Fine-tuning with Cake leads to
better results across the board

 

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Base model (out-of-the-box)

Generic, inconsistent, and off-brand: Vanilla models are trained on broad internet data, not your product, customers, or voice.

  • Responses are often vague, overly verbose, or hallucinated
  • Doesn’t understand your terminology, structure, or edge cases
  • Lacks alignment with your brand tone or compliance needs
  • Requires heavy prompt engineering to get decent output
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Fine-tuned model with Cake

Smarter, faster, and tailored to your business: Use Cake to fine-tune open models with your real data for domain-specific performance.

  • Adapts to your tone, structure, and business logic
  • Improves accuracy, reduces hallucinations, and cuts prompt complexity
  • Securely trains in your own cloud with full compliance and observability
  • Accelerates time to ROI by reducing manual intervention

EXAMPLE USE CASES

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How teams use Cake's fine-tuning

infrastructure to customize foundation

models for targeted performance

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Domain-specific LLMs

Train a general-purpose model on legal, medical, or financial data for better relevance and terminology.

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Instruction and task tuning

Fine-tune models to follow your internal formats, policies, or step-by-step procedures.

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Multi-modal adaptation

Customize vision-language or audio-text models to work with your specific inputs or annotation structure.

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Improving tone & voice for customer-facing AI

Fine-tune LLMs to match your brand’s tone, formality, or regional language preferences to ensure consistent customer experiences across channels.

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Adapting models to handle company-specific jargon

Train models to understand internal acronyms, product names, and workflows, improving performance on support, search, and agent tasks.

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Enhancing performance on non-English or low-resource languages

Fine-tune multilingual models to improve understanding and generation in target languages not well covered by default LLM training.

CUSTOMER SERVICE

Fine-tune your AI to speak your language

LLMs don’t come out of the box ready for customer interactions. See how teams use Cake to fine-tune models for brand voice, support workflows, and personalized service.

Read More >

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Run production-ready fine-tuning in hours, not weeks

See how Cake helps teams run reproducible fine-tuning experiments, track model changes, and deploy with full observability and governance.

Read More >

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"Our partnership with Cake has been a clear strategic choice – we're achieving the impact of two to three technical hires with the equivalent investment of half an FTE."

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Scott Stafford
Chief Enterprise Architect at Ping

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"With Cake we are conservatively saving at least half a million dollars purely on headcount."

CEO
InsureTech Company

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"Cake powers our complex, highly scaled AI infrastructure. Their platform accelerates our model development and deployment both on-prem and in the cloud"

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Felix Baldauf-Lenschen
CEO and Founder

Frequently asked questions

What is fine-tuning in machine learning?

Fine-tuning is the process of adapting a pretrained model to your specific domain, data, or task. It allows you to improve accuracy, alignment, and performance without training a model from scratch.

How does Cake support fine-tuning workflows?

Can I fine-tune both language and vision models with Cake?

How do I track experiments and results?

Is Cake secure and compliant for regulated industries?

Learn more about Cake

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