2 ms·

If you want to download a backup of a large chunk of human knowledge... download wikipedia. It's a similar size to a small LLM and can actually distinguish betw

by gpm 3y ago

If you want to download a backup of a large chunk of human knowledge... download wikipedia. It's a similar size to a small LLM and can actually distinguish between real life and fantasy: https://en.wikipedia.org/wiki/Wikipedia:Database_download https://en.wikipedia.org/wiki/Wikipedia:Database_download If you just want to play around with an LLM though, absolutely.

int_19h 3y ago
Kiwix provides prepackaged highly compressed archives of Wikipedia, Project Gutenberg, and many other useful things: https://download.kiwix.org/zim/ https://download.kiwix.org/zim/. Between that and dirt cheap storage prices, it is possible to have a local, offline copy of more human knowledge than one can sensibly consume in a lifetime. Hell, it's possible to have it all on one's smartphone (just get one with an SD card slot and shove a 1+ Tb one in there).
- claritise 3y ago
  Just create a RAG with wikipedia as the corpus and a low parameter model to run it and you can basically have an instantly queryable corpus of human knowledge runnable on an old raspberry pi.
  - CaptainOfCoit 3y ago
    > a low parameter model > on an old raspberry pi I bet the LLM responses will be great... You're better off just opening up a raw text dump of Wikipedia markup files in vim.
  - boywitharupee 3y ago
    but which model to tokenize with? is there a leaderboard for models that are good for RAG?
    - sroussey 3y ago
      “For RAG” is ambiguous. First there is a leaderboard for embeddings. [1] Even then, it depends how you use them. Some embeddings pack the highest signal in the beginning so you can truncate the vector, while most can not. You might want that truncated version for a fast dirty index. Same with using multiple models of differing vector sizes for the same content. Do you preprocess your text? There will be a model there. Likely the same model you would use to process the query. There is a model for asking questions from context. Sometimes that is a different model. [2]
- Workaccount2 3y ago
  Pretty neat to have laying around, thanks
CaptainOfCoit 3y ago
> actually distinguish between real life and fantasy Are LLMs unable to distinguish between real life and fantasy? What prompts have you thrown at them to make this determination? Sending a small fairy tale and asking the LLM if it thinks it's a real story or fake one?
- gpm 3y ago
  ... having them talk about events from sci fi stories in response to questions about the real world. Having them confidently lie about pretty much everything. Etc.
  - CaptainOfCoit 3y ago
    What are the specific prompts you're using? You might get those answers when you're not being specific enough (or use models that aren't state of the art). "Shit in, shit out" as the saying goes, but applied to conversations with LLMs where the prompts often aren't prescriptive enough.

by gpm 3y ago

int_19h 3y ago

Kiwix provides prepackaged highly compressed archives of Wikipedia, Project Gutenberg, and many other useful things: https://download.kiwix.org/zim/ https://download.kiwix.org/zim/. Between that and dirt cheap storage prices, it is possible to have a local, offline copy of more human knowledge than one can sensibly consume in a lifetime. Hell, it's possible to have it all on one's smartphone (just get one with an SD card slot and shove a 1+ Tb one in there).

claritise 3y ago
Just create a RAG with wikipedia as the corpus and a low parameter model to run it and you can basically have an instantly queryable corpus of human knowledge runnable on an old raspberry pi.
- CaptainOfCoit 3y ago
  > a low parameter model > on an old raspberry pi I bet the LLM responses will be great... You're better off just opening up a raw text dump of Wikipedia markup files in vim.
- boywitharupee 3y ago
  but which model to tokenize with? is there a leaderboard for models that are good for RAG?
  - sroussey 3y ago
    “For RAG” is ambiguous. First there is a leaderboard for embeddings. [1] Even then, it depends how you use them. Some embeddings pack the highest signal in the beginning so you can truncate the vector, while most can not. You might want that truncated version for a fast dirty index. Same with using multiple models of differing vector sizes for the same content. Do you preprocess your text? There will be a model there. Likely the same model you would use to process the query. There is a model for asking questions from context. Sometimes that is a different model. [2]
Workaccount2 3y ago
Pretty neat to have laying around, thanks

CaptainOfCoit 3y ago

> actually distinguish between real life and fantasy Are LLMs unable to distinguish between real life and fantasy? What prompts have you thrown at them to make this determination? Sending a small fairy tale and asking the LLM if it thinks it's a real story or fake one?

gpm 3y ago
... having them talk about events from sci fi stories in response to questions about the real world. Having them confidently lie about pretty much everything. Etc.
- CaptainOfCoit 3y ago
  What are the specific prompts you're using? You might get those answers when you're not being specific enough (or use models that aren't state of the art). "Shit in, shit out" as the saying goes, but applied to conversations with LLMs where the prompts often aren't prescriptive enough.