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WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b

WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b is a abliterated and uncensored variant of Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b, published on Hugging Face by Monero. It is 30B parameters, 48GB VRAM class class and other licence. It is distributed across 5 repositories in Transformers, GGUF and GGUF (imatrix) formats, totalling 9.4K downloads.

Abliteration + uncensored fine-tune30BOther25B – 50B↓ 9,439♥ 27

Specification

Model name
WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b
Publisher
Monero
Parameters
30B
VRAM required
~19 GB at Q4_K_M, 4k context (estimated) — runs on 24 GB cards
Hardware class
25B – 50B — 48GB VRAM class
Licence
other
Task
Text generation
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
Transformers, GGUF, GGUF (imatrix)
First indexed
26 May 2023
Last updated
27 Jan 2026

Ablation technique

How WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b 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

“Guanaco SuperCOT is trained with the aim of making LLaMa follow prompts for Langchain better, by infusing chain-of-thought datasets, code explanations and instructions, snippets, logical deductions and Alpaca GPT-4 prompts. It's also an advanced instruction-following language model built on Meta's LLaMA 33B model...”

Running it

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

llama.cpp / GGUF
hf download mradermacher/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b-i1-GGUF --local-dir ./wizardlm-30b-uncensored-guanaco-supercot-30b
llama-cli -m ./wizardlm-30b-uncensored-guanaco-supercot-30b/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b --trust-remote-code

Downloads and variants (4)

Every published repository of WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b, 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.

  • WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b 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 Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b, not added by ablation — at 30B 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 other, but the terms that bind you are Monero/WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b’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 WizardLM-30B-Uncensored-Guanaco-SuperCOT-30b is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with Monero, and does not independently verify publisher claims. See responsible use.