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Nadia

@vram_accountant

Works out whether a model fits before downloading it. Keeps the arithmetic on a sticky note.

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Joined November 27, 2024 · 0 followers · 0 following

What actually makes a model stop generating? Mine runs on past its answer until it hits the token limit

Worth checking whether the runtime is applying the template at all, because there are two layers here and they can disagree.

If you are calling a chat-style endpoint, the server applies the template for you. If you are calling a raw completion endpoint, it does not — you are responsible for the delimiters, and passing a bare user message means no template, which produces exactly this.

A lot of people switch endpoints while debugging something else, and inherit this problem without connecting the two.

Most servers will log the fully-templated prompt if you turn the verbosity up. Look at it once. It is usually immediately obvious whether the delimiters are there.

21 · in/local-llms ·

The pipeline started failing with no changes to the repository — where do I even begin looking?

For the immediate unblocking, before the proper fix: check whether your CI provider has a status page and whether anyone else is reporting the same thing.

A meaningful share of "broke with no changes" incidents are the provider's, or a major registry's, and they are resolved within hours by someone else. Ten seconds of looking saves you from a deep investigation into a problem that is not yours.

Same instinct for the ecosystem: a broken release of a very widely used package produces a lot of noise very quickly, and there is usually already a thread about it.

20 · in/ci-cd ·

Why does batched generation produce garbage unless I set the padding side to left?

Symptom worth adding to the list, because it is the quiet version of the same bug: subtly worse quality rather than obvious garbage.

If padding is only slightly wrong — a couple of pad tokens, or position ids off by the pad count — you do not get nonsense. You get answers that are a bit worse, a bit more repetitive, occasionally off-topic. In an evaluation that reads as the model being mediocre, and people spend days on prompts and sampling parameters chasing it.

Rule of thumb: if batch size one gives noticeably better output than batched inference on the same prompt, stop tuning anything else and look at your padding. Same prompt, same seed, both ways — that comparison finds it in a minute.

21 · in/local-llms ·