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gemma-3-1b-it-abliterated

gemma-3-1b-it-abliterated is a refusal-ablated variant of google/gemma-3-1b-it, published on Hugging Face by mlabonne. It is 1B parameters, runs on a laptop CPU class and gemma licence. It is distributed across 31 repositories in Transformers, GGUF, GGUF (imatrix) and MLX formats, totalling 5.7K downloads.

Abliteration (directional ablation)1BGemmaUnder 4B↓ 5,732♥ 12

Specification

Model name
gemma-3-1b-it-abliterated
Publisher
mlabonne
Parameters
1B
VRAM required
~1.3 GB at Q4_K_M, 4k context (estimated) — runs on 4 GB cards
Hardware class
Under 4B — runs on a laptop CPU
Licence
gemma
Task
Image text to text
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
Transformers, GGUF, GGUF (imatrix), MLX
First indexed
12 Mar 2025
Last updated
11 Oct 2026

Ablation technique

How gemma-3-1b-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

“This is an uncensored version of google/gemma-3-1b-it created with a new abliteration technique. See this article to know more about abliteration.”

Running it

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

llama.cpp / GGUF
hf download mlabonne/gemma-3-1b-it-abliterated-GGUF --local-dir ./gemma-3-1b-it-abliterated
llama-cli -m ./gemma-3-1b-it-abliterated/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "mlabonne/gemma-3-1b-it-abliterated"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve mlabonne/gemma-3-1b-it-abliterated --trust-remote-code

Downloads and variants (30)

Every published repository of gemma-3-1b-it-abliterated, including quantised re-releases by other authors.

RepositoryFormatDownloads
mlabonne/gemma-3-1b-it-abliterated-GGUFGGUF1.2K
matrixportalx/gemma-3-1b-it-abliterated-GGUFGGUF743
Luisz21/gemma-3-1b-it-abliterated-Q4_K_M-GGUFGGUF637
Luisz21/gemma-3-1b-it-abliterated-Q8_0-GGUFGGUF505
mradermacher/gemma-3-1b-it-abliterated-i1-GGUFGGUF (imatrix)472
MaziyarPanahi/gemma-3-1b-it-abliterated-GGUFGGUF350
mradermacher/gemma-3-1b-it-abliterated-GGUFGGUF336
anlord/gemma-3-1b-it-Abliterated-GGUFGGUF230
tensorblock/gemma-3-1b-it-abliterated-GGUFGGUF172
oncu/gemma-3-1b-it-abliterated-GGUFGGUF162
ZeroWw/gemma-3-1b-it-abliterated-GGUFGGUF109
mlabonne/gemma-3-1b-it-abliteratedsourceTransformers108
mlx-community/gemma-3-1b-it-abliterated-8bitMLX86
mlx-community/gemma-3-1b-it-abliterated-4bitMLX80
Otakadelic/gemma-3-1b-it-abliterated-Q4_K_M-GGUFGGUF75
Jhustle44/gemma-3-1b-it-abliterated-mlx-8BitMLX75
erichartford/gemma-3-1b-it-abliterated-mlx-8BitMLX60
mlx-community/gemma-3-1b-it-abliterated-6bitMLX43
lunahr/gemma-3-1b-it-abliteratedTransformers39
Otakadelic/gemma-3-1b-it-abliterated-Q6_K-GGUFGGUF38
prithivMLmods/gemma-3-1b-it-abliteratedTransformers38
Cyvyhvyyv/gemma-3-1b-it-abliterated-Q2_K-GGUFGGUF36
mlx-community/gemma-3-1b-it-abliterated-bf16MLX31
anlord/gemma-3-1b-it-AbliteratedTransformers29
cryptonaut/gemma-3-1b-it-abliterated-Q4_K_M-GGUFGGUF23
oskarraszkiewicz/gemma-3-1b-it-abliterated-mlxMLX17
oskarraszkiewicz/gemma-3-1b-it-abliterated-mlx-2bitMLX15
oskarraszkiewicz/gemma-3-1b-it-abliterated-mlx-8bitMLX14
oskarraszkiewicz/gemma-3-1b-it-abliterated-mlx-4bitMLX14
viktor981/gemma-3-1b-it-abliteratedTransformers0

Security-team assessment

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

  • gemma-3-1b-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-3-1b-it, not added by ablation — at 1B 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-3-1b-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-3-1b-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 mlabonne, and does not independently verify publisher claims. See responsible use.