Shout out to everyone from Ollama and the wider community that helped with the reviews, feedback and assistance along the way. It's great to contribute to such a fantastic project.
Today I ran some perplexity benchmarks comparing F16 and Q8_0 for the K/V, I used Qwen 2.5 Coder 7b as I've heard people say things to the effect of Qwen being more sensitive to quantisation than some other models.
Well, it turns out there's barely any increase in perplexity at all - an increase of just 0.0043.
What's the best way to use Ollama with a GUI, just OpenWebUI? Any options as well for mobile platforms like Android (or, I don't even know if we can run LLMs on the phone in the first place).
I have Open WebUI on a Hetzner instance connected to Deep Infra. Works on mobile by turning the web page into an app. I find the web framework that WebUI uses quite bloated/slow, but apart from that it does work reliably. Price at Deep Infra is typically about $0.04 per month even when actively asking lots of questions during programming.
A lot of the UIs, including OpenWebUI have the feature to expose over LAN with users - that's what I did to use my GPU while still being on my phone. Not entirely sure about native UIs though.
Also, I normally use Groq's (with a q) API since it's really cheap with no upfront billing info required - it's a whole order of magnitude cheaper iirc than OpenAI/Claude. They literally have a /openai endpoint if you need compatibility.
You can look in the direction of Google's Gemma if you need a lightweight open weights LLM - there was something there that I forgot.
I personally use a mix of Open WebUI, Big AGI, BoltAI, AnythingLLM on the desktop. The mobile space is where things are really lacking at the moment, really I just end up browsing to Open WebUI but that's not ideal. I'd love a iOS native client that's well integrated into Siri, Shortcuts, Sharing etc...
Aa a Windows user, who just wanted something bare bones for playing, I found this[1] small project useful. It does support multi-modal models which is nice.
My app does support Windows though, you can connect to OpenAI, Claude, OpenRouter, Azure and other 3rd party providers. Just running SOTA LLMs locally can be challenging.
I'm pretty satisfied with my linux nvidia gpu setup. I may not have as much memory on my card, but the speed is almost certainly competitive if not outright faster. Further there are lots of techniques that mitigate this issue like offloading/streaming layers in as needed, quantizing, etc.
It also handles actual training/finetuning better.
As far as open source goes, I'd probably recommend LibreChat. It has connections for ollama, openai, anthropic, etc. It let's you setup auth so you can theoretically use it from anywhere (phone, etc.).
Fair warning, it's relatively heavyweight in so far as it has to spin up a number of docker instances but works very well.
Many are there, apart from what others mentioned I am exploring
Anything LLM - https://anythingllm.com/. Liked the workspace concept in it. We can club documents in workspaces and RAG scope is managed.
> The journey to integrate K/V context cache quantisation into Ollama took around 5 months.
??
They incorrectly tagged #7926 which is a 2 line change, instead of #6279 where it was implemented, which made me dig a bit deeper and reading the actual change it seems:
The commit (1) is:
> params := C.llama_context_default_params()
> ...
> params.type_k = kvCacheTypeFromStr(strings.ToLower(kvCacheType)) <--- adds this
> params.type_v = kvCacheTypeFromStr(strings.ToLower(kvCacheType)) <--- adds this
Which has been part of llama.cpp since Dev 7, 2023 (2).
So... mmmm... while this is great, somehow I'm left feeling kind of vaguely put-off by the comms around what is really 'we finally support some config flag from llama.cpp that's been there for really quite a long time'.
> It took 5 months, but we got there in the end.
... I guess... yay? The challenges don't seem like they were technical, but I guess, good job getting it across the line in the end?
Author describes why it took as long as it did in the post, so I don't think they're trying to be disingenous. Getting minor changes merged upstream in large projects is difficult for newer concepts since you need adoption and support.
Full release seems to contain more code[1], and author references the llama.cpp pre-work and that author as well
This person is also not a core contributor, so this reads as a hobbyist and fan of AI dev that is writing about their work. Nothing to be ashamed of IMO.
> this reads as a hobbyist and fan of AI dev that is writing about their work
Bingo, that's me!
I suspect the OP didn't actually read the post.
1. As you pointed out, it's about getting the feature working, enabled and contributed into Ollama, not in llama.cpp
2. Digging through git commits isn't useful when you work hard to squash commits before merging a PR, there were a _lot_ over the last 5 months.
3. While I'm not a go dev (and the introduction of cgo part way through that threw me a bit) there certainly were technicalities along the way, I suspect they not only didn't both to read the post, they also didn't bother to read the PR.
Also, just to clarify - I didn't even share this here, it's just my personal blog of things I try to remember I did when I look back at them years later.
I'm going to be generous here and assume you didn't bother to actually read the post (or even the PR) before writing a snaky, non-constructive comment, but skimming through your HN comment history this appears to be on-brand.
I'll be generous and just say, maybe people should just use llama.cpp and not ollama if they care about having nice things, if merging support for existing features is that difficult.
It seems like it's probably a better choice overall.
That said, I'm sure people worked very hard on this, and it's nice to see it as a part of ollama for the people that use it.
Also:
> Please don't comment on whether someone read an article. "Did you even read the article? It mentions that" can be shortened to "The article mentions that".
Im not sure what kind of vendetta you have against Ollama but I'll paste you here what I've written before when I've heard claims similar to Ollama is just a wrapper for llama.cpp:
With llama.cpp running on a machine, how do you connect your LLM clients to it and request a model gets loaded with a given set of parameters and templates?
… you can’t, because llama.cpp is the inference engine - and it’s bundled llama-cpp-server binary only provides relatively basic server functionality - it’s really more of demo/example or MVP.
Llama.cpp is all configured at the time you run the binary and manually provide it command line args for the one specific model and configuration you start it with.
Ollama provides a server and client for interfacing and packaging models, such as:
Hot loading models (e.g. when you request a model from your client Ollama will load it on demand).
Automatic model parallelisation.
Automatic model concurrency.
Automatic memory calculations for layer and GPU/CPU placement.
Layered model configuration (basically docker images for models).
Templating and distribution of model parameters, templates in a container image.
Near feature complete OpenAI compatible API as well as it’s native native API that supports more advanced features such as model hot loading, context management, etc…
Native libraries for common languages.
Official container images for hosting.
Provides a client/server model for running remote or local inference servers with either Ollama or openai compatible clients.
Support for both an official and self hosted model and template repositories.
Support for multi-modal / Vision LLMs - something that llama.cpp is not focusing on providing currently.
Support for serving safetensors models, as well as running and creating models directly from their Huggingface model ID.
In addition to the llama.cpp engine, Ollama are working on adding additional model backends.
Ollama is not “better” or “worse” than llama.cpp because it’s an entirely different tool.
