Sabi-Noema: A capability and efficiency focused fine-tune of Qwen 3.8 27B

[u/ExperienceDry6608]

tl;dr: new qwen finetune that simultaneously pushes the frontiers of small model capability and reasoning efficiency.

Hey everyone,

We publish two post-trained versions of Qwen 3.8 27B called Sabi-Noema and Sabi-Noema-MLA. These models push the capability frontier on <30B models with 2x reduction in verbosity, and are exclusively trained on open source environments.

These models have improved capabilities on coding, agentic and tool calling tasks, as well as hallucinating lesser. Sabi-Noema beats GPT 6 Luna-medium on DeepSWE, placing in between Luna 6 medium and high. It beats Luna-high on agentic and tool calling benchmarks like TB2.1, Tau-3 bench, ClawEval, and BFCL v4. Its hallucination rate is considerably lower than Luna, Qwen and other competing models.

These models are much more reasoning efficient. Sabi-Noema generates 50% fewer tokens per response on TB2.1 and 25% fewer tokens on DeepSWE, compared to base Qwen. Sabi-Noema-MLA goes even further by doing a post-hoc MLA conversion of Qwen's GQA attention. This conversion uses only a calibration dataset and no extra training. With BF16 latents Sabi-Noema-MLA needs 3.6x lower bytes per token. The MLA latents can be further compressed to FP8 with the total KV cache size on TB2.1 to 8.4x smaller with similar performance. To put this in perspective, when using Sabi-Noema-MLA at q4_0, on a 24GB GPU like 3090/4090 you can get a context size of ~534K where base Qwen at FP8 cache would only get to ~150K context.

We are excited to have the open source community play around with these models. We will be releasing more post-trains in the future focused on making <30B models more powerful.

HuggingFace:
Sabi-Noema: https://huggingface.co/sabicap/sabi-noema
Sabi-Noema-MLA: https://huggingface.co/sabicap/sabi-noema-mla

[u/Taenk]

Was there a training breakthrough recently? Because this has to be like the fifth Qwen 27B fine tune I have seen this week.

[u/Hefty_Wolverine_553]

These claims are really incredible, but I'm not sure if they hold up despite the benchmark scores looking good. I'm pretty sure I just saw the MLA conversion thing a few posts ago today or something as well, so it's nice that we got some new interesting methodologies to play with.

[u/Atom_101 → u/Hefty_Wolverine_553]

The results surprised us as well! Feel free to test the model and give us your feedback. Can you point me to the other MLA conversion work that you saw?

[u/Hefty_Wolverine_553 → u/Atom_101]

https://www.reddit.com/r/LocalLLaMA/comments/1x5wcnz/new_model_from_telperion_qwen3827bmla/ this one, I believe.

[u/Atom_101 → u/Hefty_Wolverine_553]

Will have to take a look. We also get a 3.6x compression ratio with bf16 mla and 2x further with fp8. Haven't tested on SWE Bench but they get a +3% over base Qwen on SWE Bench while we get +9% over base on DeepSWE with non mla and +1% with mla.

Also note that our 2x reduction in reasoning tokens also holds for the base gqa model so it is already 2x cache efficient over base Qwen without MLA.

[u/silenceimpaired]

Thanks for sharing! I’ll always upvote licensed under Apache 2.0 and MIT. Well mostly

[u/caetydid]

This new MLA technique looks intriguing. Does it work with dflash/mtp in llama.cpp?

[u/Atom_101 → u/caetydid]

Haven't tested with the existing dflash models. Expect the acceptance rate will go down a bit because the model's output distribution has changed. A fine-tune of dflash should bring it back up.

[u/AppealInformal09]

Did we just get a 27B that beats GPT-6 Luna on agentic? someone pls run this before I get excited for nothing.

[u/Atom_101 → u/AppealInformal09]

Luna high yes. Working on beating max.

[u/AggravatingIsopod883]

Deepswe 47.8% from a 27B post train while Luna medium gets 36%, thats really solid.

[u/milpster]

So when would one want to use the MLA version and when would the normal version be better used?

[u/Atom_101 → u/milpster]

Use MLA if you have lower vram (<=16GB) and want t get comparable performance to base Qwen with upto 9x lower cache vram usage. Use the gqa one if you have more vram/don't care about context length as much.

[u/httpsajedeen]

48/55 on mcpmark from a 27B when base qwen gets 36. If this is real thats one of the biggest tool calling jumps I’ve seen from a finetune.

[u/Bystander10888]

Zero real-world results, just benchmark tables. Another kid using a model card as a personality. Same playbook as the last few thousand.

[u/ExperienceDry6608 → u/Bystander10888]

we use it internally and it feels far more usable. what real world tasks do you use models for?