[wokwokwok]

Nice.

That said... I mean...

> 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?

[1] - https://github.com/ollama/ollama/commit/1bdab9fdb19f8a8c73ed...

[2] - since https://github.com/ggerganov/llama.cpp/commit/bcc0eb4591bec5...

[BowBun]

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.

[1] - https://github.com/ollama/ollama/compare/v0.4.7...v0.4.8-rc0

[smcleod]

> 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.

[guywhocodes]

This is par for Ollama, look at the log_probs issues/prs and you get an idea of how well Ollama is run.

Ollama is IMO a model downloader for llama.cpp so you can do roleplay with ease.

[smcleod]

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.

[wokwokwok]

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".

https://news.ycombinator.com/newsguidelines.html

[smcleod]

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.

[yard2010]

"Judge not, that ye be not judged"