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LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS

LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS is a abliterated and uncensored variant of LiquidAI/LFM2.5-2.6B, published on Hugging Face by PhillyLabs. It is 2.6B parameters, runs on a laptop CPU class and other licence. It is distributed across 6 repositories in GGUF (imatrix) and GGUF formats, totalling 9.6K downloads.

Abliteration + uncensored fine-tune2.6BOtherUnder 4B↓ 9,613♥ 13

Specification

Model name
LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS
Publisher
PhillyLabs
Parameters
2.6B
VRAM required
~2.3 GB at Q4_K_M, 4k context (estimated) — runs on 4 GB cards
Hardware class
Under 4B — runs on a laptop CPU
Licence
other
Task
Text generation
Context window
Not documented
Ablation scope
Not stated by the publisher
Formats available
GGUF (imatrix), GGUF
First indexed
5 Aug 2026
Last updated
28 Oct 2026

Ablation technique

How LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS 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 LiquidAI's LFM2.5-2.6B with its refusal behavior aggressively reduced. It keeps the compact 2.7B hybrid architecture, but answers requests the stock post-trained model refused. In our tests, refusals fell from 132/842 to 8/842 and from 19/126 to 1/126 on a held-out set, while the average change across five...”

Running it

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

llama.cpp / GGUF
hf download PhillyLabs/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS --local-dir ./lfm2.5-2.6b-uncensored-abliterated-philadelphia-class
llama-cli -m ./lfm2.5-2.6b-uncensored-abliterated-philadelphia-class/<file>.gguf -p "..."
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "PhillyLabs/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo, torch_dtype="auto", device_map="auto"
)
vLLM
vllm serve PhillyLabs/LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS --trust-remote-code

Downloads and variants (5)

Every published repository of LFM2.5-2.6B-UNCENSORED-ABLITERATED-PHILADELPHIA-CLASS, 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-ABLITERATED-PHILADELPHIA-CLASS 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-ABLITERATED-PHILADELPHIA-CLASS is collected from the public Hugging Face API and the publisher’s model card. This site does not host weights, is not affiliated with PhillyLabs, and does not independently verify publisher claims. See responsible use.