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Qwen2.5-VL-32B: Smarter and Lighter

qwenlm.github.io

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Re: Qwen2.5-VL-32B: Smarter and Lighter

#271

Earlier quoted context omitted.

...yet

I'm not sure how it'll ever make sense unless you need a lot of customizations or care a lot about data leaks. For small guys and everyone else.. it'll probably be cost neutral to keep paying OpenAi, Google etc directly rather than paying some cloud provider to host an at best on-par model at equivalent prices.

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Re: Qwen2.5-VL-32B: Smarter and Lighter

#272

Earlier quoted context omitted.

Because they offer extremely powerful models at pretty modest prices. The hardware for a local model would cost years and years of a $20/mo subscription, would output lower quality work, and would be much slower. 3.7 Thinking is an insane programming model. Maybe it cannot do an SWE's job, but it sure as hell can write functional narrow-scope programs with a GUI.

For coding and other integrations people pay per token on api key, not subscription. Claude code costs few $ per task on your code - it gets expensive quite quickly.

But something comparable to a local hosted model in the 32-70b range costs pennies on the dollar compared to Claude, will be 50x faster than your gpu, and with a much larger context window.

Local hosting on GPU only really makes sense if you're doing many hours of training/inference daily.

Re: Qwen2.5-VL-32B: Smarter and Lighter

#273

Earlier quoted context omitted.

Except when it comes to deepseek

For others not as familiar, this is pointing out DeepSeek-v3/DeepSeek-R1 are natively FP8 so selecting "Q8_0" aligns with not selecting quantization for that model (though you'll need ~1 TB of memory to use these model unquantized at full context). Importantly, this does not apply to the "DeepSeek" distills of other models, which retain natively being the same as the base model they distill. I expect more and more wo…

I wish deepseek distills were somehow branded differently. The amount of confusion I’ve come across from otherwise technical folk, or simply mislabeling (I’m running r1 on my MacBook!) is shocking. It’s my new pet peeve.

Re: Qwen2.5-VL-32B: Smarter and Lighter

#274
post #260

Earlier quoted context omitted.

uv is just a Python package manager. No idea why they thought it was relevant to mention that

Because that one-liner will result in the model instantly running on your machine, which is much more useful than trying to figure out all the dependencies, invariably failing, and deciding that technology is horrible and that all you ever wanted was to be a carpenter.

Right: I could give you a recipe that tells you to first create a Python virtual environment, then install mlx-vlm, then make sure to downgrade to numpy 1.0 because some of the underlying libraries don't work with numpy 2.0 yet...

... or I can give you a one-liner that does all of that with uv.

Re: Qwen2.5-VL-32B: Smarter and Lighter

#275
post #17

Earlier quoted context omitted.

Pretty soon I won't be using any American models. It'll be a 100% Chinese open source stack. The foundation model companies are screwed. Only shovel makers (Nvidia, infra companies) and product companies are going to win.

I've been waiting since November for 1, just 1*, model other than Claude than can reliably do agentic tool call loops. As long as the Chinese open models are chasing reasoning and benchmark maxxing vs. mid-2024 US private models, I'm very comfortable with somewhat ignoring these models. (this isn't idle prognostication hinging on my personal hobby horse. I got skin in the game, I'm virtually certain I have the only A…

A but of a tangent, but what’re your thoughts on code agents compared to the standard “blobs of JSON” approach? I haven’t tried it myself, but it does seem like it would be a better fit for existing LLMs’ capabilities.

Re: Qwen2.5-VL-32B: Smarter and Lighter

#276

Earlier quoted context omitted.

I would say 9 times out of 10, you will get better results from a Q4 model that’s a size class larger than a smaller model at Q8. But it’s best not to go below Q4.

My understanding is that models are currently undertrained and not very "dense", so Q4 doesn't hurt very much now but it may in future denser models.

That may well be true. I know that earlier models like Llama 1 65B could tolerate more aggressive quantization, which supports that idea.

Re: Qwen2.5-VL-32B: Smarter and Lighter

#278
post #18

Earlier quoted context omitted.

it seems that this free version "may use your prompts and completions to train new models" https://openrouter.ai/deepseek/deepseek-chat-v3-0324:free do you think this needs attention?

Since we are on HN here, I can highly recommend open-webui with some OpenAI-compatible provider. I'm running with Deep Infra for more than a year now and am very happy. New models are usually available within one or two days after release. Also have some friends who use the service almost daily.

Can open-webui update code on your local computer ala cursor etc?

Re: Qwen2.5-VL-32B: Smarter and Lighter

#279
post #274

Earlier quoted context omitted.

Because that one-liner will result in the model instantly running on your machine, which is much more useful than trying to figure out all the dependencies, invariably failing, and deciding that technology is horrible and that all you ever wanted was to be a carpenter.

Right: I could give you a recipe that tells you to first create a Python virtual environment, then install mlx-vlm, then make sure to downgrade to numpy 1.0 because some of the underlying libraries don't work with numpy 2.0 yet... ... or I can give you a one-liner that does all of that with uv.

python-specific side question -- is there some indication in the python ecosystems that Numpy 2x is not getting adoption? numpy-1.26 looks like 'stable' from here

Re: Qwen2.5-VL-32B: Smarter and Lighter

#280
post #18

Earlier quoted context omitted.

Since we are on HN here, I can highly recommend open-webui with some OpenAI-compatible provider. I'm running with Deep Infra for more than a year now and am very happy. New models are usually available within one or two days after release. Also have some friends who use the service almost daily.

Can open-webui update code on your local computer ala cursor etc?

It has a module system so maybe it can but it seems more people are using Aider or Continue for that. There's a bit of stitching things together regardless of whether you show your project to some SaaS or run local models but if you can manage a Linux system it'll be easy.

Personally I heavily dislike the experience though, so I might not be the best one to answer.

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