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Cake day: March 22nd, 2024

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  • Late to this post, but shoot for and AMD Strix Halo or Nvidia Digits mini PC.

    Prompt processing is just too slow on Apple, and the Nvidia/AMD backends are so much faster with long context.

    Otherwise, your only sane option for 128K context in a server with a bunch of big GPUs.

    Also… what model are you trying to use? You can fit Qwen coder 32B with like 70K context on a single 3090, but honestly its not good above 32K tokens anyway.




  • brucethemoose@lemmy.worldtomemes@lemmy.world...
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    1 day ago

    The context window is indeed the LLM’s memory.

    …But its also muddy.

    Many LLMs get ‘dumber’ and less attentive as their context windows grow, and OpenAI’s models just happen to be one of these. It’s awful close to the full 128K, even with the full GPT-4. Mistral models are also really bad at long context understanding while, conversely, I find that Google Gemini and Qwen 2.5 are really good close to their limits.

    There are attempts to try and measure this performance objectively, like: https://github.com/NVIDIA/RULER



  • brucethemoose@lemmy.worldtomemes@lemmy.world...
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    1 day ago

    I know it’s a meme, but the idea that transformers models ‘remember’ anything is a common misconception.

    They have zero memory. When you submit a prompt, it feeds your entire chat history as one big prompt and… forgets it immediately, with no impact on the model itself. It’s like its frozen in time, and copied, unfrozen, and thrown away every time it answers.