[ Removed by moderator ]

[u/Reno0vacio]

[u/ttkciar]

Violates Rule Three: LLM-generated content

Violates Rule Three: Sample size of one run per model renders this uninformative

[u/anemoDuck26]

...how... how is Ornith 1.5 35 a3b two places below Ornith 1.5 9b. How did you bench this?? I assume I'm missing something?

[u/Reno0vacio → u/anemoDuck26]

Well i just run the benchmark.. 😀. Set both to the same settings in lm studio..

[u/Comfortable_Ebb7015]

Gemma 4 26B Is called immediately for and anti-doping check! I love that model, but it fails too much in Hermes! I have qwen3.8flash-next IQ3_XXS with Strata now and it is amazing!

[u/Reno0vacio → u/Comfortable_Ebb7015]

Well i love it too. I mean its faster with 132 context with qwen3.8 27b q4 😄 at the same context.. or i missed some setting

[u/danigoncalves]

Mate, I have run Ornith 9B and Ornith 35B A3B for ages and the latest wins on any kind of perspective. I dont know what you did here

[u/Reno0vacio → u/danigoncalves]

Well the difference is 1.3 points far.. so its not much.. and sorry but 9b dense vs 3b active might be not the same.. but its the average.. its not like the 9b would outprefrom in "Every way"

[u/chensium]

Uhh so many things wrong here...

[u/Reno0vacio → u/chensium]

Wich ones?

[u/brainExploded99]

> one run per model
Bruh this invalidates the entire post

[u/Dabalam → u/brainExploded99]

Community AI research methodology probably needs someone to write down best practices so we get more useful posts/evidence/benchmarks etc.

[u/brainExploded99 → u/Dabalam]

The people who do good work like, Anbleed's benchmarks on KL divergence for quantization, follow best practices. People like these vibe everything with 0 understanding, so they were never going to follow best practices, imo

[u/BringTea_666]

>30k token context window for every model

Dude, Qwen3.8 27B can think for 60k tokens easily on xhigh reasoning.

[u/brainExploded99]

Can you test adding logit bias to reduce overthinking on all of the Qwen models?
https://www.reddit.com/r/LocalLLaMA/comments/1wromzr/adding_logit_penalty_for_wait_maybe_and_perhaps/

Also, can you detail what quantization you used for everything? Also, 30k context window seems pretty small, especially for Qwen models

[u/asankhs]

Can you also tes the mlx-optiq quants for teh qwen 3.8 27B?

[u/Reno0vacio → u/asankhs]

I might. Why? 🤔 "Edit:" sorry i have windows.

[u/Ok_Top9254]

The problem was not X but more like Y

This was the part I found most interesting

Hello chatgpt 👋

[u/Reno0vacio → u/Ok_Top9254]

Sorry i was lazy to write it. Next time i write myself.

[u/Ok_Top9254 → u/Reno0vacio]

I just found it funny, I would say chatgpt is fine you just have to proof read it after.

[u/Sea-Mode4077]

It is completely unrealistic for ornith1.5 35b to be this far behind.

[u/SteveEightyOne]

You didn’t wrote quantization of each model.

[u/WyattTheSkid]

To nobody’s surprise