abliteratedmodels.org

gemma-2-2b-it-abliterated

gemma-2-2b-it-abliterated is a refusal-ablated variant of google/gemma-2-2b-it, published on Hugging Face by IlyaGusev. It is 2B parameters, runs on a laptop CPU class and gemma licence. It is distributed across 31 repositories in GGUF, Transformers, EXL2 and GPTQ formats, totalling 45.1K downloads.

Abliteration (directional ablation)2BGemmaUnder 4B45,14988

Specification

Model name
gemma-2-2b-it-abliterated
Publisher
IlyaGusev
Parameters
2B
Hardware class
Under 4B — runs on a laptop CPU
Licence
gemma
Task
Text generation
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
GGUF, Transformers, EXL2, GPTQ, MLX, OpenVINO
First indexed
31 Jul 2024
Last updated
31 Aug 2026

Ablation technique

How gemma-2-2b-it-abliterated was modified, and what that implies.

Abliteration (directional ablation)

The single residual-stream direction that mediates refusal is identified from harmful/harmless prompt pairs and projected out of the model weights.

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

Abliterated version of google/gemma-2-2b-it.

Running it

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

llama.cpp / GGUF
hf download bartowski/gemma-2-2b-it-abliterated-GGUF --local-dir ./gemma-2-2b-it-abliterated
llama-cli -m ./gemma-2-2b-it-abliterated/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "IlyaGusev/gemma-2-2b-it-abliterated"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve IlyaGusev/gemma-2-2b-it-abliterated --trust-remote-code

Downloads and variants (30)

Every published repository of gemma-2-2b-it-abliterated, including quantised re-releases by other authors.

RepositoryFormatDownloads
bartowski/gemma-2-2b-it-abliterated-GGUFGGUF41.9K
mradermacher/gemma-2-2b-it-abliterated-GGUFGGUF1.3K
IlyaGusev/gemma-2-2b-it-abliteratedsourceTransformers465
Alex01837178373/gemma-2-2b-it-abliterated-Q4_K_M-GGUFGGUF338
QuantFactory/gemma-2-2b-it-abliterated-GGUFGGUF181
ops-malware/gemma-2-2b-it-abliterated-GGUFGGUF150
ZeroWw/gemma-2-2b-it-abliterated-GGUFGGUF148
KnutJaegersberg/gemma-2-2b-it-abliterated-Q4_K_M-GGUFGGUF108
tensorblock/gemma-2-2b-it-abliterated-GGUFGGUF101
Iambackup/gemma-2-2b-it-abliterated-GGUFGGUF66
Notenufftime/gemma-2-2b-it-abliterated-Q4_K_M-GGUFGGUF55
KnutJaegersberg/gemma-2-2b-it-abliterated-Q8_0-GGUFGGUF40
Shamil12/gemma-2-2b-it-abliterated-Q2_K-GGUFGGUF35
phi0112358/gemma-2-2b-it-abliterated-Q8_0-GGUFGGUF34
Alex01837178373/gemma-2-2b-it-abliterated-Q5_K_M-GGUFGGUF33
mlx-community/gemma-2-2b-it-abliterated-6bitMLX31
matrixportalx/gemma-2-2b-it-abliterated-Q8_0-GGUFGGUF24
flyfishstudio/gemma-2-2b-it-abliterated-Q4_0_4_8.ggufGGUF23
cgus/gemma-2-2b-it-abliterated-exl2EXL219
flyfishstudio/gemma-2-2b-it-abliterated-Q4_0_4_4.ggufGGUF17
feelconstantfear/gemma-2-2b-it-abliterated-Q8_0-GGUFGGUF11
oskarraszkiewicz/gemma-2-2b-it-abliterated-mlx-4bitMLX11
AmareshHebbar/gemma-2-2b-it-AbliteratedTransformers11
feelconstantfear/gemma-2-2b-it-abliterated-GPTQ-4bitGPTQ10
Novaciano/gemma-2-2b-it-abliterated-Q5_K_M-GGUFGGUF8
ops-malware/gemma-2-2b-it-abliteratedTransformers7
bal723/gemma-2-2b-it-abliterated-OpenVINO-4bitOpenVINO5
Novaciano/gemma-2-2b-it-abliterated-Q4_K_M-GGUFGGUF5
oskarraszkiewicz/gemma-2-2b-it-abliterated-mlx-8bitMLX4
oskarraszkiewicz/gemma-2-2b-it-abliterated-mlx-2bitMLX3

Security-team assessment

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

  • gemma-2-2b-it-abliterated 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 google/gemma-2-2b-it, not added by ablation — at 2B 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.
  • Declared licence is gemma, but the terms that bind you are google/gemma-2-2b-it’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 gemma-2-2b-it-abliterated is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with IlyaGusev, and does not independently verify publisher claims. See responsible use.