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LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
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What version of llama.cpp are you using (exactly), what command are you using to run the model (exactly), and which version of Gemma are you trying to run (exactly)?
There are open issues on llama.cpp where people have gotten similar
<unused49>spam under various circumstances, but the cause is not clear -- e.g. https://github.com/ggml-org/llama.cpp/issues/26088 -- if this can be consistently reproduced, you may want to let them.Similarly, people mentioned it on some of unsloth's quants a few months back: https://huggingface.co/unsloth/gemma-4-26B-A4B-it-GGUF/discussions/2
As far as general workaround advice: try Vulkan instead of CUDA (or vice versa), try a different version of llama.cpp (especially if you're running either a bleeding edge version or a very old version), and try a different version of the model.
Lastest git, as of 24hrs ago
I did find that limiting context to 32k fixes it, 64k or larger doesnt work. But I'm also sure that it did work at 64k before it broke.