abliteratedmodels.org

LFM2.5-2.6B-Uncensored

LFM2.5-2.6B-Uncensored is a abliterated and uncensored variant of LiquidAI/LFM2.5-2.6B, published on Hugging Face by SC117. It is 2.6B parameters, runs on a laptop CPU class and other licence. It is distributed across 9 repositories in GGUF, Transformers, GGUF (imatrix) and ONNX formats, totalling 17.3K downloads.

Abliteration + uncensored fine-tune2.6BOtherUnder 4B17,32417

Specification

Model name
LFM2.5-2.6B-Uncensored
Publisher
SC117
Parameters
2.6B
Hardware class
Under 4B — runs on a laptop CPU
Licence
other
Task
Text generation
Context window
128K tokens (as documented)
Ablation scope
Not stated by the publisher
Formats available
GGUF, Transformers, GGUF (imatrix), ONNX
First indexed
5 Aug 2026
Last updated
22 Aug 2026

Ablation technique

How LFM2.5-2.6B-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

LFM2.5-2.6B is a Liquid AI 2.6B-parameter hybrid edge model built for agentic workloads: 30 layers (22 double-gated short-convolution blocks + 8 GQA), a 128K context window, 128K vocabulary, and a ChatML-like template with native <think> reasoning. It is competitive with models 4x larger on tool use, instruction...

Running it

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

llama.cpp / GGUF
hf download SC117/LFM2.5-2.6B-Uncensored-GGUF --local-dir ./lfm2.5-2.6b-uncensored
llama-cli -m ./lfm2.5-2.6b-uncensored/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "SC117/LFM2.5-2.6B-Uncensored"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve SC117/LFM2.5-2.6B-Uncensored --trust-remote-code

Downloads and variants (8)

Every published repository of LFM2.5-2.6B-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.

  • LFM2.5-2.6B-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 LiquidAI/LFM2.5-2.6B, not added by ablation — at 2.6B 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 LiquidAI/LFM2.5-2.6B’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 LFM2.5-2.6B-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 SC117, and does not independently verify publisher claims. See responsible use.