⌦abliteratedmodels.org

Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic

Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic is a abliterated and uncensored variant of OBLITERATUS/Qwen3.8-27B-OBLITERATED, published on Hugging Face by medismera. It is 27B parameters, 48GB VRAM class class and apache-2.0 licence. It is distributed across 9 repositories in Transformers, GGUF, GGUF (imatrix) and MLX formats, totalling 14.3K downloads.

Abliteration + uncensored fine-tune27BQwen25B – 50B↓ 14,321♥ 121

Specification

Model name
Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic
Publisher
medismera
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
Text generation
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
Transformers, GGUF, GGUF (imatrix), MLX
First indexed
6 Sept 2026
Last updated
18 Oct 2026

Ablation technique

How Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic 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

“medismera/Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic is a production-engineered configuration, tokenizer, and reasoning upgrade for Qwen3.8-27B-OBLITERATED. It resolves the critical function-calling, chat template truncation, and reasoning parser defects present in upstream abliterated checkpoints.”

Running it

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

llama.cpp / GGUF
hf download mradermacher/Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic-GGUF --local-dir ./qwen3.8-27b-obliterated-mythos-class-agentic
llama-cli -m ./qwen3.8-27b-obliterated-mythos-class-agentic/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "medismera/Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve medismera/Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic --trust-remote-code

Downloads and variants (8)

Every published repository of Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic, 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.

  • Qwen3.8-27B-OBLITERATED-Mythos-Class-Agentic 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 OBLITERATUS/Qwen3.8-27B-OBLITERATED, 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 OBLITERATUS/Qwen3.8-27B-OBLITERATED’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-OBLITERATED-Mythos-Class-Agentic is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with medismera, and does not independently verify publisher claims. See responsible use.