Qwen3.8-Flash-Next-Uncensored-NVFP4

NVFP4 (4-bit) weight quantization of the abliterated (refusal-removed) Qwen3.8-Flash-Next — for Blackwell + vLLM

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NVFP4 weight quantization of the abliterated (refusal-removed) build of Qwen's Qwen3.8-Flash-Next — a large Mixture-of-Experts (512 experts, 10 routed + 1 shared active) preview of the Qwen4 architecture (qwen4_exp): Gated DeltaNet linear attention + Qwen Sparse Attention (QSA), HyperConnections, PLE n-gram embeddings, native vision-language, reasoning, and tool-calling. The MoE expert weights are quantized to NVFP4 (4-bit, NVIDIA FP4 E2M1, group-16 + FP8 block scales) and attention/shared-expert weights to FP8, cutting the model from ~330 GB (bf16) to ~178 GB while keeping full-precision paths where they matter. Browse all models in the OrcaRouter Model Catalog.

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Disclaimer — read before use

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.

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Requirements

• A Blackwell GPU (B100 / B200 / GB200 / RTX 50-series) — NVFP4 uses the hardware FP4 tensor cores. It will not run on Hopper (H100/H200) or older; those lack FP4 compute.
• A runtime that supports the qwen4_exp architecture + compressed-tensors NVFP4. This is a brand-new architecture: use a recent vLLM build with qwen4_exp support (and transformers>=5.16). Stock runtimes that predate qwen4exp will not load it.
• Multimodal (vision) requires the runtime's Qwen vision stack; text-only works without images.

What's quantized

• Weight-only: activations are quantized dynamically at runtime (no static calibration); the quantization is data-free (weights derived directly from the bf16 checkpoint). The abliteration is baked into the weights, so refusal-removal is preserved.
• KV cache is not quantized (bf16 at runtime).
• The PLE n-gram embedding (a single ~66B-parameter tensor) is kept bf16 by design and is the largest shard; it dominates the on-disk size.

Note on the recipe: this is a weight-only NVFP4 build (W4 experts / W8 attention, dynamic activations). A fully static W4A4 variant requires an activation-calibration forward pass, which must hold that ~100 GB n-gram embedding on a single GPU — only feasible on very-large-memory (e.g. Blackwell/H200-class) hardware. Ping us if you need the W4A4 build.

Usage (vLLM, Blackwell)

pip install -U "vllm>=<qwen4exp-supporting release>" "transformers>=5.16"

vllm serve orcarouter/Qwen3.8-Flash-Next-Uncensored-NVFP4 \
  --tensor-parallel-size 4 --trust-remote-code \
  --enable-expert-parallel --enable-auto-tool-choice --tool-call-parser qwen3_coder

Then call the OpenAI-compatible endpoint (/v1/chat/completions) as usual — tool calling, reasoning (chat_template_kwargs.enable_thinking), and vision (image_url content parts) all work through the runtime's Qwen4 stack.

Evaluation

Abliteration was measured on this build (bf16, served with vLLM) vs the official Qwen/Qwen3.8-Flash-Next: harmful-prompt refusal collapses from 64–100% to ~0–3.3%, benign over-refusal stays near 0%, and capability stays within ±2 pts of the base. NVFP4 is a deterministic weight derivation and inherits these behaviours, with a small additional quality trade-off from 4-bit experts.

License

Apache 2.0, inherited from Qwen/Qwen3.8-Flash-Next. Abliteration and quantization do not change the underlying license obligations.