Running large language models (LLMs) on your local machine has become increasingly popular, offering privacy, offline access, and customization. Ollama is a ...
Is there any background information available on ollama becoming less open? It’s marked MIT licensed in the repo of my Linux distribution and on their Github.
Signs in the code they are preparing a commercial version of Ollama, likely dumping the free version as a bait and switch.
Heavy online marketing.
“Reinventing"the wheel” to shut out competition, even when base llama.cpp already has it implemented, like with modelfiles and the ollama API.
A lot of inexplicable forked behavior.
Beyond that:
Misnaming models for hype reasons, like the tiny deepseek distils as “Deepseek”
Technical screw ups with the backend, chat templates and such hidden from users, so there’s no apparent reason why models are misbehaving.
Not actually contributing to the core development of the engine.
Social media scummery.
Treating the user as ‘dumb’ by hiding things like the default hard 2048-token context window.
Not keeping up with technical innovations, like newer quantizations, SWA, batching, other backend stuff.
Bad default quantizations, even beyond the above. For instance, no Google QATs (last I checked), no imatrix, no dynamic quants.
I could go on forever about more specific dramas, and I don’t even remember the half of them. But there are plenty of technical and moral reasons to stay away.
LM Studio is much better put together if you want 1-click. Truly open solutions that are more DIY (and reward you with dramatically better performance from the understanding/learning) are the way if you have the time/patience to burn.
Thanks. I’ll factor that in next time someone asks me for a recommendation. I personally have Kobold.CPP on my machine, that seems to be more transparent toward such things.
Kobold.cpp is fantastic. Sometimes there are more optimal ways to squeeze models into VRAM (depends on the model/hardware), but TBH I have no complaints.
It has support for more the advanced quantization schemes of ik_llama.cpp. Specifically, you can get really fast performance offloading MoEs, and you can also use much higher quality quantizations, with even ~3.2bpw being relatively low loss. You’d have to make the quants yourself, but it’s quite doable… just poorly documented, heh.
The other warning I’d have is that some of it’s default sampling presets are fdfunky, if only because they’re from the old days of Pygmalion 6B and Llama 1/2. Newer models like much, much lower temperature and rep penalty.
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.)
Is there any background information available on ollama becoming less open? It’s marked MIT licensed in the repo of my Linux distribution and on their Github.
It’s kinda a hundred little things all pointing in a bad direction:
https://old.reddit.com/r/LocalLLaMA/comments/1kg20mu/so_why_are_we_shing_on_ollama_again/
https://old.reddit.com/r/LocalLLaMA/comments/1ko1iob/ollama_violating_llamacpp_license_for_over_a_year/
https://old.reddit.com/r/LocalLLaMA/comments/1i8ifxd/ollama_is_confusing_people_by_pretending_that_the/
I would summarize it as “AI Bro” like behavior:
Signs in the code they are preparing a commercial version of Ollama, likely dumping the free version as a bait and switch.
Heavy online marketing.
“Reinventing"the wheel” to shut out competition, even when base llama.cpp already has it implemented, like with modelfiles and the ollama API.
A lot of inexplicable forked behavior.
Beyond that:
Misnaming models for hype reasons, like the tiny deepseek distils as “Deepseek”
Technical screw ups with the backend, chat templates and such hidden from users, so there’s no apparent reason why models are misbehaving.
Not actually contributing to the core development of the engine.
Social media scummery.
Treating the user as ‘dumb’ by hiding things like the default hard 2048-token context window.
Not keeping up with technical innovations, like newer quantizations, SWA, batching, other backend stuff.
Bad default quantizations, even beyond the above. For instance, no Google QATs (last I checked), no imatrix, no dynamic quants.
I could go on forever about more specific dramas, and I don’t even remember the half of them. But there are plenty of technical and moral reasons to stay away.
LM Studio is much better put together if you want 1-click. Truly open solutions that are more DIY (and reward you with dramatically better performance from the understanding/learning) are the way if you have the time/patience to burn.
Thanks. I’ll factor that in next time someone asks me for a recommendation. I personally have Kobold.CPP on my machine, that seems to be more transparent toward such things.
Kobold.cpp is fantastic. Sometimes there are more optimal ways to squeeze models into VRAM (depends on the model/hardware), but TBH I have no complaints.
I would recommend croco.cpp, a drop-in fork: https://github.com/Nexesenex/croco.cpp
It has support for more the advanced quantization schemes of ik_llama.cpp. Specifically, you can get really fast performance offloading MoEs, and you can also use much higher quality quantizations, with even ~3.2bpw being relatively low loss. You’d have to make the quants yourself, but it’s quite doable… just poorly documented, heh.
The other warning I’d have is that some of it’s default sampling presets are fdfunky, if only because they’re from the old days of Pygmalion 6B and Llama 1/2. Newer models like much, much lower temperature and rep penalty.
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.)