abliteratedmodels.org

WizardLM-7B-V1.0-Uncensored

WizardLM-7B-V1.0-Uncensored is a abliterated and uncensored variant of QuixiAI/WizardLM-7B-V1.0-Uncensored, published on Hugging Face by QuixiAI. It is 7B parameters, single consumer GPU class and other licence. It is distributed across 13 repositories in Transformers, GPTQ, GGUF and AWQ formats, totalling 5.6K downloads.

Abliteration + uncensored fine-tune7BOther4B – 10B5,63320

Specification

Model name
WizardLM-7B-V1.0-Uncensored
Publisher
QuixiAI
Parameters
7B
Hardware class
4B – 10B — single consumer GPU
Licence
other
Task
Text generation
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
Transformers, GPTQ, GGUF, AWQ, GGUF (imatrix)
First indexed
18 Jun 2023
Last updated
13 Aug 2026

Ablation technique

How WizardLM-7B-V1.0-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 is a retraining of https://huggingface.co/WizardLM/WizardLM-7B-V1.0 with a filtered dataset, intended to reduce refusals, avoidance, and bias.

Running it

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

llama.cpp / GGUF
hf download TheBloke/WizardLM-7B-V1.0-Uncensored-GGUF --local-dir ./wizardlm-7b-v1.0-uncensored
llama-cli -m ./wizardlm-7b-v1.0-uncensored/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "QuixiAI/WizardLM-7B-V1.0-Uncensored"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve QuixiAI/WizardLM-7B-V1.0-Uncensored --trust-remote-code

Downloads and variants (12)

Every published repository of WizardLM-7B-V1.0-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.

  • WizardLM-7B-V1.0-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 QuixiAI/WizardLM-7B-V1.0-Uncensored, not added by ablation — at 7B 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.
  • Declared licence is other, but the terms that bind you are QuixiAI/WizardLM-7B-V1.0-Uncensored’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-7B-V1.0-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 QuixiAI, and does not independently verify publisher claims. See responsible use.