• @uis@lemm.ee
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    04 months ago

    Aren’t LLMs external algorithms at this point? As in the all data will not fit in RAM.

    • @brucethemoose@lemmy.world
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      4 months ago

      No, all the weights, all the “data” essentially has to be in RAM. If you “talk to” a LLM on your GPU, it is not making any calls to the internet, but making a pass through all the weights every time a word is generated.

      There are system to augment the prompt with external data (RAG is one word for this), but fundamentally the system is closed.

      • @Hackworth@lemmy.world
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        04 months ago

        Yeah, I’ve had decent results running the 7B/8B models, particularly the fine tuned ones for specific use cases. But as ya mentioned, they’re only really good in thier scope for a single prompt or maybe a few follow-ups. I’ve seen little improvement with the 13B/14B models and find them mostly not worth the performance hit.

        • @brucethemoose@lemmy.world
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          04 months ago

          Depends which 14B. Arcee’s 14B SuperNova Medius model (which is a Qwen 2.5 with some training distilled from larger models) is really incrtedible, but old Llama 2-based 13B models are awful.

            • @brucethemoose@lemmy.world
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              4 months ago

              Try a new quantization as well! Like an IQ4-M depending on the size of your GPU, or even better, an 4.5bpw exl2 with Q6 cache if you can manage to set up TabbyAPI.