Lol at that command. Why is this stuff so hard to run locally? I've spent a few days trying to figure it all out and haven't been able to. LM Studio doesn't work behind proxies. Ollama is confusing and doesn't seem to support Qwen3? And Llama.cpp is your command.
I just want to run `<some-command> <model-name>` with some default parameters set and for it to run locally.
What makes you say that it would be hard to do that?
It's long, I guess, but not cryptic.
You tell llama server where the model is, which context size to use, what to use for the K/V cache quant, that it should do MTP, tune some MTP parameters, and that's kinda it.
Perfectly logical blocks with all the model-specific weirdness (that does exist!) abstracted away.
You could also just run -m <modelfile> and let llama-server do the right-ish thing. The defaults are probably fine, but not how you squeeze out these exact numbers.
I think at least. I've never tried. My hubris stopped me from trying auto configs.
> llama server where the model is, which context size to use, what to use for the K/V cache quant, that it should do MTP, tune some MTP parameters, and that's kinda it.
isn't that the hard part? You know the ballpark ideal values for these many parameters since you're a knowledgeable expert but the vast majority of people are just like "I want AI" and have no idea what all the jargon even means.
Why would they wish to handcraft this ? That is what agents are for ?
They could ask your current agent to a) search for this type of content online for the optimal setup for their hardware b) have the current agent/harness spin it up have it verify the config run few experiments.
Sure AI may make mistakes, or won't get the best possible config probably, but it certainly do a good enough setup, this is a task with feedback on whether the server crashed or poor performance easily measured so the agent can do a pretty good job.
Because this is kinda the one new thing that arrived in the technology scene, so getting at least some amount of understanding of its "inner" workings might prove useful in the future.
Beside that, it is also just.. interesting? It's fun tuning the machine to see it improve. For some, anyway.
The pain point they raised is this is too complicated for people who just want to get started, that is not true anymore.
It is certainly fun to fine-tune and setup if you like do something like that, however the need to do it hardly is a barrier for those who don't want complexity as OP imagines.
Lower level API/interfaces should not be a barrier for people if they are apply framing that way. More and more people are thinking agent native so this is not really a issue.
The alternative is to label it AI. That is distasteful for some of us, Intelligence is much stronger than automation in our minds so we avoid the term and try to describe it differently .
This is exactly it. I already have broad access to Claude, Gemini, GitHub Copilot. I want to use open models on automated tasks that chew up tokens but where I don't necessarily need the best-in class models and UX.
For Claude, I setting a single config file and then download and run Claude Code CLI. Even easier for the GitHub Copilot CLI.
Well there's a lot of knobs to turn if you want to improve performance. You can always point an LLM at the model card, give it your info, and have it write up the command.
Sure, but shouldn’t the programs to run the LLMs go “the user has this much vram and the model is this size, so I’ll start with sensible defaults based on that”?
I'll actually defend you on this one, but not only the command, that's more of a fine tuning option. On the hugging face link for example, there's no flat 'download' area. It's completely unintuitive for people to know how to use it. Reminds me of my first time trying to make sense of torrents when they first arrived.
There are some clients that will index the models and allow you to do that but I'm no expert, I've used OLama studio but it always seems to go weird for me.
Even this command above, it's not clear where op got the model from. So I'm with yah.
For example, op uses : Qwen3.8-27B-IQ4_NL.gguf.. But I cant see where to download it. It's not tagged on hugging face at least..
Official llama.cpp releases ship with huggingface support. If you don't want to download it yourself, you can just use the `repo/model:quant` convention and it will handle downloading locally for you.
But you're assuming I'm using the Olama studio. This model as far as I see doesn't have a gguf download.. Unless I'm missing something on the page.
If I want to download the model myself, it's not clear. I thought it was supposed to behave like a package manager. But even in nuGet I can download a zip of the package.
What on earth are you talking about? llama.cpp != Ollama. You can (and should) just use llama.cpp directly. Upstream llama.cpp can take the shorthand huggingface path and automagically download it into a cache folder as part of the launch. Have you read any of the docs?
Excuse me, but thats a direct link you've just sent. I asked where I can find the links. I like to believe in the source of truth.
They shared a lot of links, I'm struggling to find yours. Where did yours come from ?
Who is Unsloth AI? Have they modified the model ? Is this really the source of truth ?
Do you see how steep the barrier for entry is to do anything right ?
unslothai is not a name qwen has ever used. So you're sharing a link to a model that isn't from the owner, while saying it's the owner's. I'm not comfortable with that, and I want AI to be a better tool.
> Excuse me, but thats a direct link you've just sent. I asked where I can find the links. I like to believe in the source of truth.
You asked where to find the GGUF files of this model for direct download and I provided it. Almost all useful model files that can be downloaded are hosted on Huggingface.
> They shared a lot of links, I'm struggling to find yours. Where did yours come from ?
I went to Huggingface, went to the Unsloth org, as they tend to be the best, went to the Model page, and went to the "Files and versions" tab.
> Who is Unsloth AI? Have they modified the model ? Is this really the source of truth ?
Unsloth AI is a very popular, highly reputable organization that takes upstream model files, performs some optimization, and provides models in various formats. Apart from speed tweaks, they do not modify the models. They also provide useful benchmarks, copious documentation for local execution, and a Studio application for easy execution and post-training of models.
> Do you see how steep the barrier for entry is to do anything right ?
No. Searching for this information is not difficult. The llama.cpp documentation and guides that Unsloth provide are all you need. Search engines can take you further if you want.
> unslothai is not a name qwen has ever used. So you're sharing a link to a model that isn't from the owner, while saying it's the owner's. I'm not comfortable with that
Qwen also provides models in GGUF format on Huggingface, but they will not be as performant. Even when first-party GGUFs are available, most people will prefer quants from Unsloth or a few other popular optimizer accounts.
> I want AI to be a better tool.
Best of luck. Your attitude and unwillingness to even try and learn on your own when people have tried helping have burned my good will, and this is as far as I'm willing to carry you.
I gave you a direct link to where you can get a download, and explained why I chose to link you where I did. I can't post screenshots here drawing you a map of how to use a website. I don't work for Huggingface or any AI company and can affect no changes to how intuitive any of it is, and think is easy enough already.
Huggingface, Unsloth, and llama.cpp all have documentation you can follow that will exceed anything I can tell you here. LMstudio, Lemonade, or Ollama might be even easier for you to use. Take my suggestions or don't.
Start by copying the command line from the Unsloth guides.
You don’t need to fine tune all of those parameters to get started.
It’s really easy to ask an LLM to adjust the command line if you can’t be bothered to read the help out. Copy the help output into the LLM and tell it your goal.
> Ollama is confusing and doesn't seem to support Qwen3?
Typing “Ollama qwen3” into Google takes you right to this page:
If even Googling for basic Ollama support is too hard, there might come a point where you have to acknowledge that local LLMs are not for you. None of this is really that hard with some basic Google bootstrap skills or by asking an LLM to help with the command.
on Qwen's official docs: https://qwen.readthedocs.io/en/latest/run_locally/ollama.htm.... It's not like I just made it up. Of course I searched "ollama qwen3" and saw what you linked, but that doesn't mean it "works". I have other things to do besides to try a bunch of poorly documented and executed tools just to see if it works or not.
I guess the TLDR is that I'm stupid or lazy. Also, everyone is responding about how easy it is, and yet, it's apparently so easy that it's hard to document well.
There are easier ways to run it. OP seemed to enjoy tinkering and customizing the command to run it exactly the way they want. When I don’t want to tinker Unsloth Studio is probably closest to pick a model and voila.
I never install this stuff manually anymore. Just tell your LLM of choice to download model X from URL Y, build the latest inference engine of choice E, and then create batch files or shell scripts to run instruct and/or reasoning models in accordance with instructions at URL Z.
While you probably have the audacity to check the commands and code that your favorite LLM generates to complete this task, i'm pretty much certain that at some point in time, due to this pervasive mindset of "just let an AI agent do it," we'll have a front page news story of someone who had their family photos on their NAS uploaded to some public space, their security cameras connected to a "live porn for lolz" webring, and perhaps even their crypto wallets and bank accounts pilfered, all because their favorite LLM got exploited by some prompt injection attack while trying to be helpful doing system administration tasks.
I remember there was a short story in BYTE Magazine about a similar kind of scenario way back when, I think at least 30 years ago, long before LLMs and AI agents became a reality.
Hmm I'm still struggling with the downloading of all the models I want to try locally, so I might be out of the loop, but all of this, including tuning (to your local HW), feels like it could be automated. I'm old though.
> Every big company in the world uses a network proxy.
It's becoming more rare, now.
A lot of the universal truths about corporate networks from the early 2000s are no longer true today. Some companies are stuck in their ways though.
The overlap between companies that require someone to use a network proxy and companies that have GPU-equipped machines with enough RAM for LLMs and and that allow people to download and run executables of their choosing has to be small.
I would love to see more data on that because I've seen it constantly. There is more isolation maybe where you can do whatever on 'open' network, but always some kind of proxy/vpn connection for hitting anything sensitive.
The operlap is there.. But I would be worried if it was just flat out taken away from secure managed connections just because of AI.. Again, would love to see the numbers of your assumptions.
Woa TIL. I thought that was somehow long solved at the OS level or with VPNs or something like that (no idea exactly how, I'm sure just I'm misunderstanding something basic).
Makes it rather weird that LM Studio doesn't support it given how their target market, or well at least for their paid products, is very enterprisey.
I just want to run `<some-command> <model-name>` with some default parameters set and for it to run locally.