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Joined 1 year ago
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Cake day: June 8th, 2023

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  • Because we have tons of ground-level sensors, but not a lot in the upper layers of the atmosphere, I think?

    Why is this important? Weather processes are usually modelled as a set of differential equations, and you want to know the border conditions in order to solve them and obtain the state of the entire atmosphere. The atmosphere has two boundaries: the lower, which is the planet’s surface, and the upper, which is where the atmosphere ends. And since we don’t seem to have a lot of data from the upper layers, it reduces the quality of all predictions.










  • CVEs are constantly found in complex software, that’s why security updates are important. If not these, it’d have been other ones a couple of weeks or months later. And government users can’t exactly opt out of security updates, even if they come with feature regressions.

    You also shouldn’t keep using software with known vulnerabilities. You can find a maintained fork of Chromium with continued Manifest V2 support or choose another browser like Firefox.





  • Mostly via terminal, yeah. It’s convenient when you’re used to it - I am.

    Let’s see, my inference speed now is:

    • ~60-65 tok/s for a 8B model in Q_5_K/Q6_K (entirely in VRAM);
    • ~36 tok/s for a 14B model in Q6_K (entirely in VRAM);
    • ~4.5 tok/s for a 35B model in Q5_K_M (16/41 layers in VRAM);
    • ~12.5 tok/s for a 8x7B model in Q4_K_M (18/33 layers in VRAM);
    • ~4.5 tok/s for a 70B model in Q2_K (44/81 layers in VRAM);
    • ~2.5 tok/s for a 70B model in Q3_K_L (28/81 layers in VRAM).

    As of quality, I try to avoid quantisation below Q5 or at least Q4. I also don’t see any point in using Q8/f16/f32 - the difference with Q6 is minimal. Other than that, it really depends on the model - for instance, llama-3 8B is smarter than many older 30B+ models.


  • Have been using llama.cpp, whisper.cpp, Stable Diffusion for a long while (most often the first one). My “hub” is a collection of bash scripts and a ssh server running.

    I typically use LLMs for translation, interactive technical troubleshooting, advice on obscure topics, sometimes coding, sometimes mathematics (though local models are mostly terrible for this), sometimes just talking. Also music generation with ChatMusician.

    I use the hardware I already have - a 16GB AMD card (using ROCm) and some DDR5 RAM. ROCm might be tricky to set up for various libraries and inference engines, but then it just works. I don’t rent hardware - don’t want any data to leave my machine.

    My use isn’t intensive enough to warrant measuring energy costs.