January 2026 - Blablador-news - lists.fz-juelich.de

Helmholtz Codebase outage
by Strube, Alexandre 27 Jan '26

27 Jan '26

Blablador uses Helmholtz Gitlab for authentication, and the server is currently down. Planned Maintenance The Helmholtz Codebase is currently unavailable due to a planned maintenance of the database. Start: January 27th, 15:55 (CET / UTC+1) Planned End: January 27th, 17:00 (CET / UTC+1) Sorry for that :-(

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Do you want an app?
by Strube, Alexandre 23 Jan '26

23 Jan '26

[image] Would you like it? The Siri/android voice integration has been difficult, so it’s basically a website inside an app, like most of them are these days . []s Alexandre Strube surak(a)surak.eti.br

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GLM 4.7 FULL
by Strube, Alexandre 22 Jan '26

22 Jan '26

After the great news of GLM-Flash, I decided to try the full, 385 billion parameter GLM 4.7. And guess what? It’s running! Compared to Minimax, it is slower, but better at math, and multi-modal. It can understand text, video, images and audio. While Minimax is theoretically capable of one million tokens, GLM is limited to 204 thousand. Given that is just slightly better than Minimax, while being slower, I am not sure if I will keep running it. What do you think? Leeeeeeet’s bark! Alex

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What a difference a small patch makes...
by Strube, Alexandre 22 Jan '26

22 Jan '26

Following a hint found on Twitter [1], a little patch on VLLM allowed me to increase the context length on the GLM 4.7-Flash models from 8000 to 200000 (!) tokens. Now, those models get good rankings, but they REALLY overthink. The good thing is that if this patch works with other models, I can run the big GLM 4.7 on bigger nodes! Let’s bark! [1] https://x.com/TheAhmadOsman/status/2013881920099062163

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GLM 4-7 Flash in two variants!
by Strube, Alexandre 20 Jan '26

20 Jan '26

Hello Blabladores! Some of you might have heard of the best medium-sized model released last night, GLM-4.7 Flash. You know the story: It’s slightly better than last week’s model of similar capacity. It’s a 31 billion parameter model, a mixture-of-experts with 3 billion activated. As we hear more and more about strongly quantizing models, I decided to run an experiment. I am running not one, but two versions of GLM 4.7-Flash: One with quantization and one without. Given that they are running on identical hardware (4x RTX 3090 each), something has to give: the not-quantized model has a smaller context length (around 18k tokens, vs 68k tokens for the quantized one). The quantized one is slightly faster, but not by much. I might have to optimize it still. In any case, they are both available for you to compare. What is better: Quantized but bigger context or not-quantized but smaller? You decide. Let’s bark! Alex

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