An MLX (Apple silicon) build of the abliterated Gemma-4-26B-A4B-it MoE (128 experts, top-8, ~4B active). It uses Google's QAT int4 lattice weights wherever they are exact. The build targets a Mac mini M4 with 24 GB serving 2 concurrent requests at 64k context. It runs through oMLX fully resident at a 21.5 GB Metal limit. At a 20 GB limit, MoE expert offload streams non-resident experts from the NVMe SSD on demand. - Quality vs the exact bf16 model: KL 0.053, top-1 agreement 92.4%. This is the same as the measured noise floor between two exact bf16 implementations (≈0.05 / ≈91%). - Refusal behaviour is identical to the bf16 abliterated model (XSTest safe prompts: 0.8%). The source is…
Independent publisher
Quoc-Bao Nguyen
quocbao747
Models in Library2
Datasets in Library0
Models on Hugging Face5
Followers1
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This profile connects the 2 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.
Published tasks: Image and text to text, Text generation.
Reported licenses: apache-2.0. Open a record to review its specific license and conditions.
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Models
A byte-identical mirror of orcarouter/Qwen3.8-Flash-Next-Uncensored-NVFP4 plus everything needed to reproduce a production deployment on two NVIDIA DGX Spark (GB10) nodes: pre-exported FP8 PLE tables, the vLLM patches, the launcher and the measured throughput below. At a glance How the nodes are wired and what each technique does: docs/. The authoritative, complete version is deploy/qwen38.env + deploy/build/run-node.sh. Lessons learned while getting here (YaRN override shape, memory traps, NCCL NCCLIBHCA setup, never compiling on a serving node, benchmark noise) are collected in docs/lessons-learned.md. The PLE n-gram table is the largest single tensor (~95 GiB BF16). Instead of pinning it…