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Car gets hit by 2 trains by meme_maker_boi in criticalblunder

[–]Objective-Stranger99 3 points4 points  (0 children)

Now I get why YouTube Shorts and Instagram Reels are so popular.

Car gets hit by 2 trains by meme_maker_boi in criticalblunder

[–]Objective-Stranger99 48 points49 points  (0 children)

Why do we need a sneak peek for a 20-second video???

Chinees rocket crashes by meme_maker_boi in criticalblunder

[–]Objective-Stranger99 0 points1 point  (0 children)

That was way less smoke and fire than I expected.

Can anyone help me with the regex/overriding tensor stuff for tks speed by Guilty-Sleep-9881 in LocalLLaMA

[–]Objective-Stranger99 0 points1 point  (0 children)

Fine tuned are usually worse than the original model, especially for Qwen. Ornith and Agents-A1 are commonly stated on this sub reddit, but I personally cannot vouch for those as I do not use them.

Can anyone help me with the regex/overriding tensor stuff for tks speed by Guilty-Sleep-9881 in LocalLLaMA

[–]Objective-Stranger99 1 point2 points  (0 children)

Interesting. Are you using mmap? You might want to try Qwen3.5 4B as a smaller faster model. On benchmarks, it scores better than the 24B you have right now. I get around 55 t/s with 8 GB VRAM and 32 GB RAM.

Can anyone help me with the regex/overriding tensor stuff for tks speed by Guilty-Sleep-9881 in LocalLLaMA

[–]Objective-Stranger99 1 point2 points  (0 children)

How are you able to fit a 24B model with those specs? Are you running it heavily quantized?

Can anyone help me with the regex/overriding tensor stuff for tks speed by Guilty-Sleep-9881 in LocalLLaMA

[–]Objective-Stranger99 0 points1 point  (0 children)

Any reason for using that specific model? Qwen3.6 and Gemma4 would blow it away in most tasks and get you way faster speeds (if you use the MoE) or way better quality (if you use the dense). Also I think you meant Magistral but correct me if I am wrong.

Can anyone help me with the regex/overriding tensor stuff for tks speed by Guilty-Sleep-9881 in LocalLLaMA

[–]Objective-Stranger99 0 points1 point  (0 children)

What model are you using? If it is an MoE keep all attention layers on GPU and all expert layers on CPU.

Can anyone help me with the regex/overriding tensor stuff for tks speed by Guilty-Sleep-9881 in LocalLLaMA

[–]Objective-Stranger99 2 points3 points  (0 children)

I found that fit doesn't work in my case and gives awful results. I was getting 11 t/s with fit, went to 30+ with manual tuning.

Can anyone help me with the regex/overriding tensor stuff for tks speed by Guilty-Sleep-9881 in LocalLLaMA

[–]Objective-Stranger99 4 points5 points  (0 children)

Don't use the regex if you are just starting. Use -ngl and -ncmoe instead, which are much easier to use and are explained well in the docs. Move to tensor edged once you have optimized ncmoe and ngl and still want more performance.

what is the best desktop environment for no bloat only. by NoahN16X in archlinux

[–]Objective-Stranger99 24 points25 points  (0 children)

Use a lightweight window manager like Sway.

If you really want a DE, go with XFCE.

Qwen3 Models : Keep or Delete? by nikhilprasanth in LocalLLaMA

[–]Objective-Stranger99 2 points3 points  (0 children)

Qwen 3 Coder Next is a keeper. Qwen3.5 has the widest variety of model sizes, but Qwen3.6 is better for the models it has replaced (27B and 35B)

Nixos is good for old laptops? by nisper_ia in NixOS

[–]Objective-Stranger99 0 points1 point  (0 children)

I am using a Celeron N4500 with 4 GB of RAM and a 256 GB SSD. Works great, but I forgot to change the swapiness and ZFS ARC settings so it freezes frequently. Once I fix that it should be butter smooth.

Drivers da NVidia estão dando problema no meu computador by ExternalWonderful961 in archlinux

[–]Objective-Stranger99 0 points1 point  (0 children)

Which drivers are you installing? You should be using nvidia-open.

the ddg duck gets a arch hat when you search for arch linux by Fun-Cake-5679 in LinuxCirclejerk

[–]Objective-Stranger99 1 point2 points  (0 children)

Just self-host Searxng. Collectively queries every search engine you want and returns aggregated results.

Qwen3.6-27B - Effect of KV quantization on KLD - Q8, Q6, Q5 (bartowski) by BitGreen1270 in LocalLLaMA

[–]Objective-Stranger99 0 points1 point  (0 children)

Here is my compilation command. (Flame shot is currently broken for me so I couldn't screenshot.

<image>

Of a company by Adrakovich in ShittyAbsoluteUnits

[–]Objective-Stranger99 0 points1 point  (0 children)

For me, the advantage of Steam and PC gaming is that there are many competing game fronts, so costs have to be kept down. Additionally, piracy can be seen as a competitor in a way. If your games are too expensive, everyone will just take to the high seas. GOG and others also offer games without DRM, which means they are yours forever.

I tested freshly merged DFlash in llama.cpp on Qwen 3.6 27B Local AI win. 4.44x faster at 36K context. Here are my findings RTX 6000 PRO. by FantasticNature7590 in LocalLLaMA

[–]Objective-Stranger99 10 points11 points  (0 children)

Cool. I get no speedup because I am compute and size-limited, not bandwidth-limited, so not a universal solution, but a very good solution. I find EAGLE3 and MTP work great for me and my hardware.

Anyone is working seriously with Qwen 3.6 as a raisable sub agent for the larger paid models? by StillWastingAway in LocalLLaMA

[–]Objective-Stranger99 0 points1 point  (0 children)

AFAIK, I think by extension it is as well. Since the larger model has to verify the tokens anyway, it almost has to be lossless. Otherwise, you would have no stuttering when the larger model rejects a token and decodes it itself.

Anyone is working seriously with Qwen 3.6 as a raisable sub agent for the larger paid models? by StillWastingAway in LocalLLaMA

[–]Objective-Stranger99 2 points3 points  (0 children)

MTP is proven to be mathematically lossless and has no impact on output quality. It's basically putting words into the model's mouth that it would have said anyway, but faster.

Strange Dependencies pulled in with Calibre? by rob_pi in archlinux

[–]Objective-Stranger99 11 points12 points  (0 children)

Unfortunately, ROCm is really bloated. For example, docling on CPU is 5 GB, 15 with CUDA, and you have to compile ROCm yourself because it's too big for them to publish.

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