Earlier quoted context omitted.
i think you will like luna if you haven't tried it yet
Luna is twice the price of Deepseek V4 Flash 0731, and less capable :/
Didn't DeepSeek go up 2x?
Qwen 3.8 27B
381–390 of 848 posts
Re: Qwen 3.8 27B
#382Earlier quoted context omitted.
Honest question, how do you assess models this quickly? What metrics are you using? Would love to get my suite from multiple days and hundreds of prompts down to minutes. Got a few first pass tasks I run upon release for an initial experience, but those only work because even Fable and Sol fail despite objectively correct solutions existing, so it works because most models fail, but then, those are consciously not en…
What are you working on? That can dictate which models are best.
Anything from ML pipelines for language specific pruning over a Rust/JS/CSS mix codebase to assistance in motorcycle maintenance and different canvas coatings. Most of my evals build on those requirements and especially past failures, whether in pure information, task execution and coding or tool calling beyond the overfitted mainstream. All stuff derived from actual failures encountered, some still only few models come even close to passing. With such a mix, it just takes a while to get any serious opinion on a model. Doubt anyone can do that in such short time, unless their tasks are so simple that most modern models not only succeed but could themselves accurately rate output. If even Fable still confuses PU or wax coated cotton canvas with a nylon shell, or tells me with a straight face to adjust the valves on a bike that has hydraulic lifters that needs experience for human assessment and the time that comes with it. Anyone with less knowledge either wouldn’t see the mistakes staring them in the face and just go by vibes, any model rating these equally can not tell what is accurate and will just go by the output sounding accurate over being. Gives sometimes very interesting results far different to public benchmarks. Inkling, e.g. is more accurate in not telling you to adjust valves that are simply not adjustable then Fable or Sol, which just tell you to adjust every 5000km. Sometimes even when their reasoning and search includes sections about the fact this is not necessary or possible. The beauty of overfitting and unbalanced training data…
Re: Qwen 3.8 27B
#383Any tips on the best approach at running this at an M4 Max 128GB? Token throughput was a bit slow with the last 27B one (MLX), ended up using the A3B variant but if I could get this one to reasonable speed I'd much prefer it.
Wait for the MTP variants that will likely be out within days. I'm on a 128GB Strix Halo box and for 3.6-27B 8bits I was getting about 9tok/sec (not great). With MTP that gets closer to 18 tok/sec (kind'a usable).
The MTP is available, but I'm definitely not seeing 18 t/s on the Strix Halo from the 8-bit quantization, even with MTP (more like ~10 with full context on long tasks). This is a slow model (but so was 3.6). What's your exact llama-server command that gets 18 t/s?
Re: Qwen 3.8 27B
#384Earlier quoted context omitted.
Why slow? I see ~50tps on a single 3090
Whats your setup? I have a single 3090 and am struggling to get it purring
5900x, 3090 24gb (slightly undervolted), 128gb ddr4, running via Ollama.
I am benchmarking it now locally, will put the results and speed/tps on aibenchy.com
Re: Qwen 3.8 27B
#385Re: Qwen 3.8 27B
#386As usual, the Jinja templates are messed up so use this [0] to reduce or turn off thinking, fix tool calling, keep a 100% KV cache hit rate, etc. [0] https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates
Interesting. Why don't the unsloth guides ( https://unsloth.ai/docs/models/qwen3.8 ) mention this? Do they already include the fixes in their GGUFs?
@danielhanchen may be able to answer this
Re: Qwen 3.8 27B
#387Re: Qwen 3.8 27B
#388Earlier quoted context omitted.
"Benchmark is stupid" and "model beats model on benchmark" are two different things, though. The second one is objectively true regardless of your views on the first one, right? To expect everyone to share your opinion that benchmarks are stupid is pretty weird, and just saying "no" to an objective truth is the definition of delusion.
If a benchmark is a measure of nothing useful, then model beats model is an objectively useless fact
But that's the problem with that logic. The author subjectively claims benchmarks are stupid (a debatable opinion) and treats that as inherently true while making the second claim (hence, you shouldn't say model A beat model B).
I can say I find the law stupid, so no one should say person A beat person B in court. But I did not prove the law is stupid; I merely thought it subjectively and demanded others to follow the second part because I believe the first part is true.
Saying that "if the law is useless, court cases are useless" is objectively true and cannot be argued with. But you still need to prove why the law is useless, not why you think it is and even then if people disagree and use the law as a reference, then it's not objectively useless and court cases are not useless as well.
Re: Qwen 3.8 27B
#389Since it might be helpful to some, here's my current commandline for llama.cpp running on an RTX 4090 with my monitor moved to the iGPU to free up all of its VRAM. llama-server -m Qwen3.8-27B-IQ4_NL.gguf --mmproj mmproj-BF16.gguf -c 170000 --parallel 1 -ngl -1 --cache-type-k q8_0 --cache-type-v q8_0 -b 1024 -ub 512 --flash-attn on --no-context-shift --no-mmproj-offload --spec-type draft-mtp --spec-draft-n-max 5 --spe…
I absolutely love this comment. I wished there was a website where people would post their working command lines as well as what hardware they are using to run that stuff on + tokens / sec prefill + gen.
As a MBP and DGX spark owner, I would love such a site... (Feel like it would be a low effort feature of hugging face).
Searching through Reddit and forums for best commands is annoying.
Re: Qwen 3.8 27B
#390Since it might be helpful to some, here's my current commandline for llama.cpp running on an RTX 4090 with my monitor moved to the iGPU to free up all of its VRAM. llama-server -m Qwen3.8-27B-IQ4_NL.gguf --mmproj mmproj-BF16.gguf -c 170000 --parallel 1 -ngl -1 --cache-type-k q8_0 --cache-type-v q8_0 -b 1024 -ub 512 --flash-attn on --no-context-shift --no-mmproj-offload --spec-type draft-mtp --spec-draft-n-max 5 --spe…
Does anyone know how to get this working with Claude Code via llama-server? I'm getting a jinja template error about the system prompt not being the first message.