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cm0002@programming.dev to LocalLLaMA@sh.itjust.worksEnglish · 1 month ago

Running Local LLMs with Ollama on openSUSE Tumbleweed

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Running Local LLMs with Ollama on openSUSE Tumbleweed

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cm0002@programming.dev to LocalLLaMA@sh.itjust.worksEnglish · 1 month ago
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Running large language models (LLMs) on your local machine has become increasingly popular, offering privacy, offline access, and customization. Ollama is a ...
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    1 month ago

    Turn it into an ik_llama.cpp k quant, and you should be able to squeeze even more out!

    FYI you can find more models like this by looking up a base model (not the instruct) of interest, then clicking on the ‘finetunes’ category. For example:

    https://huggingface.co/models?other=base_model%3Afinetune%3AQwen%2FQwen3-30B-A3B-Base&sort=modified

    https://huggingface.co/models?other=base_model%3Afinetune%3Amistralai%2FMistral-Small-24B-Base-2501&sort=modified

    This one’s also the perfect size for you, but has no finetunes yet: https://huggingface.co/baidu/ERNIE-4.5-VL-28B-A3B-Base-PT


    One other thing. A lot of folks (like me) tend to use the base models, not instruct finetunes, in completion mode since they tend to be devoid of AI slop. But you have to prompt them different than a regular LLM: instead of multi turn conversation, you write out a starting block of text for them to ‘latch onto’, and get them to continue it from your cursor.

    But prompt them right, and they will do literally whatever you want, devoid of any sycophancy or guardrails.

    Mikupad is great for this since it also shows token probablities. So you can, for instance, click on a critial word, and see what ‘choices’ the LLM was considering internally as a set of branches, and regenerate from there.

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Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.

Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.

As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.

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>.

Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.

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