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Model training &
fine-tuning

Even though models like GPT4 or Mixtral are extremly powerful, responses can vary significantly in quality and consistency between calls. Fine-tuning a LLM can therefore can therefore be necessary for certain use-cases.

The rise of the small

Researchers and practicioners have shown again and again that smaller models, fine-tuned to a specific task, can reach the quality or even outperform much larger models.

Fine-tuning a smaller open source model therefore offers multiple advantages over a generalistic model:

  • Higher quality
  • Reduced cost
  • Faster inference
  • Keeping the fine-tuned model data in-house

How we can help you

We can support you in the full process of fine-tuning a model that meets your needs:

  • Data preparation
  • Model training
  • Third-party fine-tuning (e.g. OpenAI)
  • Custom fine-tuning
  • Deployment of fine-tuned model or adapters
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