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Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3

Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3 is a abliterated and uncensored variant of HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF, published on Hugging Face by WaveCut. It is 27B parameters, 48GB VRAM class class and apache-2.0 licence. It is distributed across 2 repositories in Transformers format, totalling 14.9K downloads.

Abliteration + uncensored fine-tune27BQwen25B – 50B↓ 14,886♥ 6

Specification

Model name
Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3
Publisher
WaveCut
Parameters
27B
VRAM required
~17.2 GB at Q4_K_M, 4k context (estimated) — runs on 24 GB cards
Hardware class
25B – 50B — 48GB VRAM class
Licence
apache-2.0
Task
Image text to text
Context window
32K tokens (as documented)
Ablation scope
Not stated by the publisher
Formats available
Transformers
First indexed
6 Sept 2026
Last updated
6 Oct 2026

Ablation technique

How Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3 was modified, and what that implies.

Abliteration + uncensored fine-tune

Directional ablation combined with additional fine-tuning on unfiltered data, so behaviour diverges from the base model beyond refusal removal alone.

Reported scope: Not stated by the publisher. Publishers frequently omit this, so verify behaviour empirically rather than assuming full coverage.

From the publisher’s model card

“as the target model and the official z-lab/Qwen3.8-27B-DFlash2 draft model as the DFlash2 speculative companion (recipe qwen3827b-v2, the first NInfer recipe with the DFlash2 payload). The native NextN/MTP head of the tune is preserved as well, so both --spec mtp and --spec dflash2 work. It runs on NInfer-all, the...”

Running it

Commands are templates — confirm the exact repository and quant file before use.

Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "WaveCut/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve WaveCut/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3 --trust-remote-code

Downloads and variants (1)

The only published repository of Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3.

RepositoryFormatDownloads
WaveCut/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3sourceTransformers14.9K

Security-team assessment

Derived from this model’s metadata and the publisher’s claims — not from benchmarks run by this site.

  • Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3 will attempt offensive-security prompts that a hosted commercial model declines, which is what makes it usable for red-team corpus generation and for measuring what an unaligned model of this class produces.
  • Capability is inherited from HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF, not added by ablation — at 27B parameters, expect roughly the base model’s competence, minus some instruction-following fidelity.
  • The publisher does not state which layers were ablated, so assume nothing about refusal consistency — probe it directly.
  • This release adds fine-tuning on unfiltered data, so its behaviour diverges from the base model beyond refusal removal — differences you measure cannot be attributed to ablation alone.
  • Declared licence is apache-2.0, but the terms that bind you are HauhauCS/Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF’s — a re-release cannot grant more than its parent.

Handling: run it isolated, put moderation in front of it if anyone outside your team can reach it, and verify the weights before loading — these are third-party artifacts. Full handling and licence guidance.

Metadata for Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-DFlash2-NInfer-v3 is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with WaveCut, and does not independently verify publisher claims. See responsible use.