ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF
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tl;dr: new qwen finetune that simultaneously pushes the frontiers of small model capability and reasoning efficiency.
Hey everyone,
We are Sabi (https://sabi.com), a neuro-AI startup building a BCI + AI wearable that can do agentic actions using just your neural intents.
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: sabicap/sabi-noema
Sabi-Noema-MLA: sabicap/sabi-noema-mla
Hey everyone,
We are Sabi (https://sabi.com), a neuro-AI startup building a BCI + AI wearable that can do agentic actions using just your neural intents.
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: sabicap/sabi-noema
Sabi-Noema-MLA: sabicap/sabi-noema-mla
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replied to nwaughachukwuma's post 25 days ago
checking best model page (dots.mocr) it compares somewhat unfavorably to qwen3-4b-vl-instruct, so i'm wondering that this is not the whole list of such models
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