Running large language models (LLMs) on your local machine has become increasingly popular, offering privacy, offline access, and customization. Ollama is a ...
Thanks for the random suggestion! Installed it already. Sadly as a drop-in replacement it doesn’t provide any speedup on my old machine, it’s exactly the same number of tokens per second… Guess I have to learn about the ik_llama.cpp and pick a different quantization of my favourite model.
What model size/family? What GPU? What context length? There are many different backends with different strengths, but I can tell you the optimal way to run it and the quantization you should run with a bit more specificity, heh.
CPU-only. It’s an old Xeon workstation without any GPU, since I mostly do one-off AI tasks at home and I never felt any urge to buy one (yet). Model size woul be something between 7B and 32B with that. Context length is something like 8128 tokens. I have a bit less than 30GB of RAM to waste since I’m doing other stuff on that machine as well.
And I’m picky with the models. I dislike the condescending tone of ChatGPT and newer open-weight models. I don’t want it to blabber or praise me for my “genious” ideas. It should be creative, have some storywriting abilities, be uncensored and not overly agreeable. Best model I found for that is Mistral-Nemo-Instruct. And I currently run a Q4_K_M quant of it. That does about 2.5 t/s on my computer (which isn’t a lot, but somewhat acceptable for what I do). Mistral-Nemo isn’t the latest and greatest any more. But I really prefer it’s tone of speaking and it performs well on a wide variety of tasks. And I mostly do weird things with it. Let it give me creative advice, be a dungeon master or an late 80s text adventure. Or mimick a radio moderator and feed it into TTS for a radio show. Or write a book chapter or a bad rap song. I’m less concerned with the popular AI use-cases like answer factual questions or write computer code. So I’d like to switch to a newer, more “intelligent” model. But that proves harder than I imagined.
(Occasionally I do other stuff as well, but that’s a far and in-between. So I’ll rent a datacenter GPU on runpod.io for a few bucks an hour. That’s the main reason why I didn’t buy an own GPU yet.)
Thanks for the random suggestion! Installed it already. Sadly as a drop-in replacement it doesn’t provide any speedup on my old machine, it’s exactly the same number of tokens per second… Guess I have to learn about the ik_llama.cpp and pick a different quantization of my favourite model.
What model size/family? What GPU? What context length? There are many different backends with different strengths, but I can tell you the optimal way to run it and the quantization you should run with a bit more specificity, heh.
CPU-only. It’s an old Xeon workstation without any GPU, since I mostly do one-off AI tasks at home and I never felt any urge to buy one (yet). Model size woul be something between 7B and 32B with that. Context length is something like 8128 tokens. I have a bit less than 30GB of RAM to waste since I’m doing other stuff on that machine as well.
And I’m picky with the models. I dislike the condescending tone of ChatGPT and newer open-weight models. I don’t want it to blabber or praise me for my “genious” ideas. It should be creative, have some storywriting abilities, be uncensored and not overly agreeable. Best model I found for that is Mistral-Nemo-Instruct. And I currently run a Q4_K_M quant of it. That does about 2.5 t/s on my computer (which isn’t a lot, but somewhat acceptable for what I do). Mistral-Nemo isn’t the latest and greatest any more. But I really prefer it’s tone of speaking and it performs well on a wide variety of tasks. And I mostly do weird things with it. Let it give me creative advice, be a dungeon master or an late 80s text adventure. Or mimick a radio moderator and feed it into TTS for a radio show. Or write a book chapter or a bad rap song. I’m less concerned with the popular AI use-cases like answer factual questions or write computer code. So I’d like to switch to a newer, more “intelligent” model. But that proves harder than I imagined.
(Occasionally I do other stuff as well, but that’s a far and in-between. So I’ll rent a datacenter GPU on runpod.io for a few bucks an hour. That’s the main reason why I didn’t buy an own GPU yet.)