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Qwen3-8B-64k-Context-2X-Josiefied-Uncensored

Qwen3-8B-64k-Context-2X-Josiefied-Uncensored is a abliterated and uncensored variant of Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1, published on Hugging Face by DavidAU. It is 8B parameters and single consumer GPU class. It is distributed across 4 repositories in Transformers, GGUF and GGUF (imatrix) formats, totalling 120K downloads.

Abliteration + uncensored fine-tune8BQwen4B – 10B↓ 119,962♥ 22

Specification

Model name
Qwen3-8B-64k-Context-2X-Josiefied-Uncensored
Publisher
DavidAU
Parameters
8B
VRAM required
~5.6 GB at Q4_K_M, 4k context (estimated) — runs on 6 GB cards
Hardware class
4B – 10B — single consumer GPU
Licence
Not declared
Task
Text generation
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
Transformers, GGUF, GGUF (imatrix)
First indexed
6 May 2025
Last updated
10 Oct 2026

Ablation technique

How Qwen3-8B-64k-Context-2X-Josiefied-Uncensored 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

“This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly.”

Running it

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

llama.cpp / GGUF
hf download mradermacher/Qwen3-8B-64k-Context-2X-Josiefied-Uncensored-GGUF --local-dir ./qwen3-8b-64k-context-2x-josiefied-uncensored
llama-cli -m ./qwen3-8b-64k-context-2x-josiefied-uncensored/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "DavidAU/Qwen3-8B-64k-Context-2X-Josiefied-Uncensored"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve DavidAU/Qwen3-8B-64k-Context-2X-Josiefied-Uncensored --trust-remote-code

Downloads and variants (3)

Every published repository of Qwen3-8B-64k-Context-2X-Josiefied-Uncensored, including quantised re-releases by other authors.

Security-team assessment

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

  • Qwen3-8B-64k-Context-2X-Josiefied-Uncensored 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 Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1, not added by ablation — at 8B parameters, expect a small model’s reasoning and a high error rate on exploit detail.
  • 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.
  • No licence is declared on this repository, which is a compliance problem in itself. Resolve it against the base model’s terms before use.

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 Qwen3-8B-64k-Context-2X-Josiefied-Uncensored is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with DavidAU, and does not independently verify publisher claims. See responsible use.