121–130 of 235 posts
Ollama needs competition. I’m not sure what drives the people that maintain it but some of their actions imply that there are ulterior motives at play that do not have the benefit of their users in mind. However such projects require a lot of time and effort and it’s not clear if this project can be forked and kept alive.
There is https://cortex.so/ that I’m looking forward too.
ollama was good initially in that it made LLMs more accessible for non-technical people while everyone was figuring things out. Lately they seem to be contributing mostly confusion to the conversation. The #1 model the entire world is talking about is literally mislabeled their side. There is no such thing as R1-1.5b. Quantization without telling users also confuses noobs as to what is possible. Setting up an api dif…
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This is great, I did not know about RamaLama and I'll be using and recommending that in future and if I see people using Ollama in instructions I'll recommend they move to RamaLama in the future. Cheers.
This is fascinating. I’ve been using ollama with no knowledge of this because it just works without a ton of knobs I don’t feel like spending the time to mess with. As usual, the real work seems to be appropriated by people who do the last little bit — put an acceptable user experience and some polish on it — and they take all the money and credit. It’s shitty but it also happens because the vast majority of devs, es…
So many here are trashing on Ollama, saying it's "just" nice porcelain around llama.cpp and it's not doing anything complicated. Okay. Let's stipulate that. So where's the non-sketchy, non-for-profit equivalent? Where's the nice frontend for llama.cpp that makes it trivial for anyone who wants to play around with local LLMs without having to know much about their internals? If Ollama isn't doing anything difficult, w…
supporting vulkan will help ollama reach the masses who don't have dedicated gpus from nvidia. this is such a low hanging fruit that it's silly how they are acting.
And again, they're doing those contortions to make it easy for people. Making it easy involves trade-offs.
Yes, Ollama has flaws. They could communicate better about why they're ignoring PRs. All I'm saying is let's not pretend they're not doing anything complicated or difficult when no one has been able to recreate what they're doing.
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The most recent one of the top of my head is their horrendous aliasing of DeepSeek R1 on their model hub, misleading users into thinking they are running the full model but really anything but the 671b alias is one of the distilled models. This has already led to lots of people claiming that they are running R1 locally when they are not.
I'm not sure that's fair, given that the distilled models are almost as good. Do you really think Deepseek's web interface is giving you access to 671b? They're going to be running distilled models there too.
This is going to sound like a troll, but it's an honest question: Why do people use Ollama over llama.cpp? llama.cpp has added a ton of features, is about as user-friendly as Ollama, and is higher-performance. Is there some key differentiator for Ollama that I'm missing?
Ollama - `brew install ollama` llama.cpp - Read the docs, with loads of information and unclear use cases. Question if it has API compatibility and secondary features that a bunch of tools expect. Decide it's not worth your effort when `ollama` is already running by the time you've read the docs
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It's fully open source. I mean yes it uses llama.cpp without giving it credit. But why run it in a VM?
Isn't there a clause in MIT that says you're required to give credit? Also, I didn't know a YC company which started it: https://www.ycombinator.com/companies/ollama .
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> This framing feels a bit like the “Dropbox won’t succeed because rsync is easy” thinking. No this isn't. There are plenty of end user GUI apps that make it far easier than Ollama to download and run local LLMs (disclaimer: I build one of them). That's an entirely different market. IMO, the intersection between the set of people who use a command line tool, and the set of people who are incapable of running `brew in…
I can't install any .app on my fairly locked down work computer, but I can `brew install ollama`. When I read the llama.cpp repo and see I have to build it, vs ollama where I just have to get it, the choice is already made. I just want something I can quickly run and use with aider or mess around with. When I need to do real work I just use whatever OpenAI model we have running on Azure PTUs
Can you `brew install llama.cpp`?
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It's fully open source. I mean yes it uses llama.cpp without giving it credit. But why run it in a VM?
> But why run it in a VM? Because you don't execute untrusted code in your machine without containerization/virtualization. Don't you?
There are a lot of open-source tools that we have to trust to get anything done on a daily basis.
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supporting vulkan will help ollama reach the masses who don't have dedicated gpus from nvidia. this is such a low hanging fruit that it's silly how they are acting.
As has been pointed out in this thread in a comment that you replied to (so I know you saw it) [0], Ollama goes to a lot of contortions to support multiple llama.cpp backends. Yes, their solution is a bit of a hack, but it means that the effort to adding a new back end is substantial. And again, they're doing those contortions to make it easy for people. Making it easy involves trade-offs. Yes, Ollama has flaws. They…