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Discover the Future of Fine-Tuning. Experience the Evolution.

Fine-Tuning LLMs without Forgetting

Fine-tuning is a commonly used technique for customizing foundational LLMs to a particular use case or domain. The process of fine-tuning typically involves training on a small data set (<40k sample) pertaining to a restricted domain, in the hope that the resulting model will be optimized for the domain of interest.



However, fine-tuning using conventional schemes, such as LoRA, often results in forgetting, with two kinds of effects. One is the loss of knowledge and reasoning capabilities offered by the foundational model. More importantly, such fine-tuning also runs the risk of removing some of the protection from harmful outputs produced by RLHF.

Tenyx has developed a novel fine-tuning service, which is as fast as conventional techniques, yet learns better and mitigates these two undesirable forgetting effects. We are now offering this service in a  beta.

Why join our Beta?

Retrain Your Models

Incrementally improve your models by refining them without the risk of forgetting prior knowledge and reasoning.

Master Generative AI for Enterprise

Prompt engineering not working? No problem. Safely use your data to train into the model desired behavior while leveraging the richness of LLMs.

Reduce Risk of Removing RHLF

Existing fine tuning techniques put your models at risk of undoing RHLF. This means that you could be unknowingly exposing customer to potentially harmful responses.

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Ready to Elevate Your AI?

Space in our beta is limited. Don't miss out on this opportunity—sign up now!

Thank you for your submission.

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