license: other
license_name: lfm1.0
license_link: LICENSE
base_model: LiquidAI/LFM3-Omni-2B
tags:

• gguf
• lfm3
• lfm3-omni
• liquid
• omni
• vision
• audio
• speech
• quantized
• mixed-precision
• uncensored
• abliterated
• llama.cpp
• xolotl
  language:
• en
  pipeline_tag: any-to-any

LFM3-Omni-2B · XQ, abliterated

Liquid AI's LFM3-Omni-2B (text, images, video and audio in; text
and speech out) with the refusal direction edited out of the weights, then quantised with Indra's Mirror's
mixed-precision XQ pipeline at five sizes.

XQ-6.0 (1.29 GiB) agrees with the abliterated 8-bit model on 95.3% of next tokens (KL 0.021) and decodes at
432 tok/s on an RTX 4090. XQ-4.5 is the same size as Q4_K_M with about a third less KL divergence.

Files

The text model is the only thing changed. The projector is Liquid AI's own file, copied as-is so one download
has everything.

How it was made

1. Abliteration on the full-precision weights: a rank-2 refusal-direction edit, fitted on Liquid AI's BF16
   checkpoint. Checked on 100 held-out harmful prompts and 100 harmless ones it never saw:

   	harmful prompts refused	harmless prompts refused
   original LFM3-Omni-2B	92 / 100	5 / 100
   abliterated	10 / 100	2 / 100

2. Quantisation with XQ: every tensor gets its own format (IQ3_S, IQ4_XS, IQ4_NL, Q3_K to Q6_K, Q8_0, BF16),
   picked by measured sensitivity on a coding/agent/OCR/long-context calibration set for a given bits-per-weight
   budget. The XQ pipeline itself is private; the files are standard GGUF and need no special loader.

LFM3 is a non-reasoning model: the chat template opens every answer with an empty <think></think>.

Quality

Teacher-forced on a held-out text set (~256 KB, never used for calibration), against the abliterated Q8_0
(perplexity 6.379). KL = mean KL divergence of the next-token distribution; top-1 = same most likely token.
llama-perplexity --kl-divergence, llama.cpp f3586e6.

The official files are not abliterated, so their KL includes the abliteration difference as well as their
quantisation (Q8_0's 0.0059 is roughly that difference alone). Past 6.0 the curve flattens: going from XQ-6.0 to
the Q8_0 costs another 0.42 GiB for 0.021 KL.

Multimodal and tool smoke test (XQ-6.0 + the Q8_0 projector, llama-server, 32K context): tool call correct,
image described correctly (shapes, colours and the number), a speech clip transcribed exactly. Speech output
has not been measured on these files yet.

Speed

RTX 4090 24 GB, Ryzen 7 5700X, Linux. llama-bench tg128, all layers on the GPU:

Served (llama-server, XQ-6.0 + projector, 32K context): 3.5 GB of VRAM, 389 tok/s decode on the GPU, 23 tok/s
decode CPU-only (8 threads).

Quick start

Needs a llama.cpp with LFM3 support (upstream from 31 Oct 2026, f3586e6 or later).

hf download IndrasMirror/LFM3-Omni-2B-XQ-Uncensored \
   LFM3-Omni-2B-XQ-6.0-Uncensored.gguf mmproj-LFM3-Omni-2B-Q8_0.gguf --local-dir lfm3
llama-server -m lfm3/LFM3-Omni-2B-XQ-6.0-Uncensored.gguf --mmproj lfm3/mmproj-LFM3-Omni-2B-Q8_0.gguf \
   -ngl 99 -c 32768 --jinja -fa auto

Images, audio (input_audio) and video (input_video, with its soundtrack) go through the usual
/v1/chat/completions; speech comes out of /tts and the /v1/realtime websocket (see llama.cpp's server
README). Liquid AI's recommended sampling and system prompts are on the
original model card.

Native support in Xolotl is being built.

Licence

LFM Open License v1.0, the same as the original model. These files are modified versions of
LiquidAI/LFM3-Omni-2B (abliterated and quantised by Indra's Mirror); the projector is unmodified. Commercial use is
licensed only below Liquid AI's revenue threshold; read the licence.

Credits

• Liquid AI for LFM3-Omni-2B and its official GGUF projector.
• ggml / llama.cpp for LFM3 support and the evaluation tools.
• Abliteration, XQ quantisation and measurements: Indra's Mirror.

Support

If this has been useful and you want to help keep it going, you can
buy me a coffee.