GLM-5.3-Flash-XSFP4

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Huihui-GLM-5.3-Flash-abliterated-FP4-Local

Edp1096

Unofficial NVIDIA FP4-Local derivative with Huihui Q8 deltas applied. Applied delta: DQ(Huihui Q8) - DQ(Unsloth Q8). The conversion used these exact revisions. - Updated 44 attention-output/shared-expert-down matrices in layers 15–36 and 22 scale tensors - Preserved all other tensors, including routed experts, CSF scales, MTP, vision, and tokenizer files - Requantized in the existing FP8 formats; no additional QAD training or activation calibration Requires two DGX Sparks (TP2) and B12X vLLM with CSF support. See runtime-configuration.json for exact tested settings. Cold-start loading caused memory pressure and some host swapping. - All 42 weight files verified; 8/8 smoke checks passed…

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Qwen3.8-Flash-Next

Qwen

As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For…

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Qwen3.8-Flash-Next-Uncensored-NVFP4

OrcaRouter

This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). It will comply with harmful, unethical, or illegal requests the original Qwen3.8-Flash-Next would refuse. Released strictly for legitimate research — interpretability, AI-safety / refusal-mechanism study, red-teaming, and robustness evaluation. You assume full responsibility for how you use it and everything it generates; add your own safety and moderation layers before any deployment. Use must comply with the Apache 2.0 License inherited from the base model and all applicable law. The authors accept no liability for misuse. - A Blackwell GPU…

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Darwin-180B-RSI

FINAL_Bench

flagship of the Darwin family — #1 on AIME 2026, HMMT Feb 2026, GPQA Diamond, MMLU-Pro, MMMU-Pro, LEXam and LEXam-hard, and a model that gets better by learning from its own verified work. Scores as listed on the Hugging Face official benchmark leaderboards (self-reported by each model's publisher). = #1 on that leaderboard. "—" = not reported. Darwin is VIDRAFT's measurement-driven reasoning model family — 50+ official models, 400+ community derivatives, and now two places in the GPQA Diamond top 3 (Darwin-180B-RSI #1 · Darwin-397B-ZTC #3). Darwin treats a strong open model as a parent. It measures where the parent is weak, and strengthens exactly those parts — instead of re-training…

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Qwen3.8-Flash-Next-MLX-oQ3-MTP

Robot Haus

A sensitivity-guided, mixed-precision MLX conversion of Qwen/Qwen3.8-Flash-Next, rebuilt directly from the official BF16 checkpoint with the model's matching native MTP block preserved. oQ3 uses a 3-bit affine base and spends additional precision on sensitive modules. Layer sensitivity was measured with a validated quantized calibration proxy, while every released weight was quantized from the official BF16 checkpoint. The result is a compact model with 746 higher-precision module overrides rather than a uniform 3-bit layout. The upstream tokenizer, current chat template, vision processor, generation configuration, licence, and native MTP configuration are retained. In a compatible oMLX…

As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For…