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Comment by blackoil | Hacker News Reader

Dataset may not be public. All large companies have millions of internal documents. Internal LLM can be trained on them.

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Qlora won't work well to add knowledge via private data.

Parameter efficient methods are not useful for these cases at the 8b scale without a more complex training procedure that periodically merges back adapters. Maybe at the 70B scale.

What scale of company do you need to be to actually be able afford and get return on investment on retraining base models with your own proprietary knowledge and docs? Considering also the implications of continually retraining?

I was under the impression that you wouldn't. If you want access to proprietary knowledge, you would use RAG + LLM.

I don't think anyone has the answer to this question yet.

The only experience I have is first hand, what my company is doing for our client base. We are doing continuous pretraining and the rest of the alignment stack training on about 10B private tokens + private customer data to produce private custom models for companies in the 500 to 3000 employee range. We built and operate a single rack cluster that cost mid 6 figures in order to be able to do this.

These models get combined with rag for highly specific technical doc authoring and other uses.

This is very helpful context on what works right now, thanks for sharing.

Can you point to any literature on this by any chance? I would be really interested to see some in depth analysis.

I don't have the arxiv link bookmarked, but there was a paper written on pretraining with Lora. It involved merging adapters back every n steps with good results.

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