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> AFAICT from a quick glance at the code (https://github.com/qhjqhj00/MemoRAG/blob/main/memorag/memora https://github.com/qhjqhj00/MemoRAG/blob/main/memorag/mem

> AFAICT from a quick glance at the code (https://github.com/qhjqhj00/MemoRAG/blob/main/memorag/memora https://github.com/qhjqhj00/MemoRAG/blob/main/memorag/memora...), it is indeed "fine tuning" (jargon!) a model on your chosen book, presumably in the most basic/direct sense: asking it reproduce sections of text at random from the book given their surrounding context, and rewarding/penalizing the neural network based on how well it did. Maybe your use of quotes is intentional here, but for posterity's sake there is no actual fine-tuning happening using user input in the code you linked, insofar as the weights of the model aren't being touched at all, nor are they modifying anything else that could impact the original weights (like a LoRA adapter). You touch on this, I think (?), in some of your subsequent language but it read as a little confusing to me at first glance. Or maybe I've been too deep in the ML weeds for too many years at this point. The paper details the actual process, but the TL;DR is that the memory module they use, basically a draft model, does go through a pretraining phase using the redpajama dataset, and then an SFT phase with a different objective. This all happens before and irrespective of the inference-time task (i.e. asking questions about a given text). Also, as has been pointed out in other comments, the draft model could really be any model that supports long context and has decent retrieval performance. So the actual training phases here may be non-essential depending on your infra/cost constraints.

bbor 2y ago
Thanks for the corrections! I’m very much not an expert on LLM usage in the real world. But I’m a bit confused: does go through a pretraining phase using the redpajama dataset, and then an SFT phase with a different objective Isn’t that equivalent to what I said, since “SFT” seems to stand for “supervised fine-tuning”? That it starts with a pre trained model, and then modifies that model according to your corpus? Perhaps the confusion here is my ambiguity with “model”; I now see that there’s really two models-one for generating a draft + clues and one for constructing the final output—and this library only concerns/modifies the former. Maybe?
- spmurrayzzz 2y ago
  I should have quoted you more specifically, my apologies. I was responding to the comment that there was some training of the "model on your chosen book". There is no fine-tuning done specific to the corpus you own. I noted this in a sibling comment, but both the pretraining and fine-tuning objective uses a generic dataset (redpajama) which "aims to maximize the generation probability of the next token given the KV cache of the previous memory tokens" (quote from section 2.2 of the paper). This is why I noted you could really use any long-context model that also has good retrieval performance. They're training their own draft model in lieu of using an existing model, but you could get similar/better outcomes using something like claude sonnet 3.5.
  - bbor 2y ago
    Thanks for taking the time, that makes sense. This is not the first time I've misunderstood something by having opinions about what it should be doing in my opinion, haha. I absolutely agree with your last point, too.

bbor 2y ago

Thanks for the corrections! I’m very much not an expert on LLM usage in the real world. But I’m a bit confused: does go through a pretraining phase using the redpajama dataset, and then an SFT phase with a different objective Isn’t that equivalent to what I said, since “SFT” seems to stand for “supervised fine-tuning”? That it starts with a pre trained model, and then modifies that model according to your corpus? Perhaps the confusion here is my ambiguity with “model”; I now see that there’s really two models-one for generating a draft + clues and one for constructing the final output—and this library only concerns/modifies the former. Maybe?

spmurrayzzz 2y ago
I should have quoted you more specifically, my apologies. I was responding to the comment that there was some training of the "model on your chosen book". There is no fine-tuning done specific to the corpus you own. I noted this in a sibling comment, but both the pretraining and fine-tuning objective uses a generic dataset (redpajama) which "aims to maximize the generation probability of the next token given the KV cache of the previous memory tokens" (quote from section 2.2 of the paper). This is why I noted you could really use any long-context model that also has good retrieval performance. They're training their own draft model in lieu of using an existing model, but you could get similar/better outcomes using something like claude sonnet 3.5.
- bbor 2y ago
  Thanks for taking the time, that makes sense. This is not the first time I've misunderstood something by having opinions about what it should be doing in my opinion, haha. I absolutely agree with your last point, too.