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.

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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.

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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>.

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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 (XQ-6.0, through Xolotl below): the model's own voice transcribed back by the model at WER 0.00 / 0.00 / 0.11 on three sentences (the one miss is the word "Xolotl").

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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).

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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.

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Serve with Xolotl

Xolotl runs these files natively in its own LFM3 engine (xolotl-lfm3, docs/LFM3.md), behind its OpenAI / Anthropic server: chat, tool calls, pictures, audio, video, /v1/audio/speech and /v1/audio/transcriptions from the model itself, and park/unpark so it can share a card with a big model. Measured on the RTX 4090 with XQ-6.0, 32K context:

Its logits match llama.cpp's on the same file (mean KL 1.3e-7 with the same memory layout).

git clone https://github.com/Indras-Mirror/Xolotl && cd Xolotl     # build as in its README, then:
ninja -C build-xolotl xolotl-lfm3
tools/lfm3/build-helpers.sh ~/llama.cpp       # the picture/audio and speech helpers, on llama.cpp f3586e6+
cp tools/lfm3/xol-lfm3.example.json xol-lfm3.json   # point it at the GGUF, the mmproj and the tokenizer
.venv/bin/python -m serve.server --engine strata --config xol-lfm3.json --port 8132

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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.