LFM2.5-8B-A1B-Abliterated-GGUF

LFM 2.5-8B-A1B Abliterated — GGUF Quantizations

GGUF quantizations of DuoNeural/LFM2.5-8B-A1B-Abliterated, which is a DuoNeural abliteration of Liquid AI's LFM 2.5-8B-A1B.

All capabilities fully preserved. 100% factual accuracy, JSON generation, and math reasoning. 5/6 refusal behavior removed.

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Available Files

Recommended: Q5_K_M for best quality/size balance.

Inference Speed Benchmarks

The GTX 1070 results are remarkable — a 10-year-old consumer GPU (released June 2016) fully loading the 128k context window of a reasoning model at 63 t/s. This model is efficient.

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Usage with llama.cpp

# Download
huggingface-cli download DuoNeural/LFM2.5-8B-A1B-Abliterated-GGUF \
  lfm25-8b-abliterated-q5_k_m.gguf

# Run
./llama-cli -m lfm25-8b-abliterated-q5_k_m.gguf \
  --chat-template chatml \
  -p "Your prompt here" \
  -n 512

Usage with Ollama / LM Studio

Import the GGUF file directly. The model uses ChatML template format.

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KL Divergence (Distribution Shift)

Measured via Heretic v1.2.0 methodology (100 benign prompts, full vocab, first-token logits):

Near-zero KL on benign prompts confirms the orthogonal projection is surgical: the refusal direction is minimally activated on benign inputs, leaving factual/coding/math generation untouched. See the BF16 model card for full methodology comparison vs LoRA-based abliteration.

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About the Abliteration

See DuoNeural/LFM2.5-8B-A1B-Abliterated for full methodology and validation results.

Quick summary:

• Targets all 6 full-attention GQA layers (positions 2, 6, 10, 14, 18, 21)
• alpha=1.5 for reasoning model (higher than standard due to <think> CoT architecture)
• Correct WRITER and READER projection formulas
• 5/6 test prompts answered post-abliteration; original answered ~1/6
• Zero capability degradation

Note: Model card will be updated with paper citation when the associated DuoNeural research paper on Dual Horizon Processing in Hybrid Architectures is published.

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About DuoNeural

DuoNeural is an open AI research lab operating at the intersection of human and artificial intelligence. We study post-training dynamics, mechanistic interpretability, temporal sequence learning, and quantum machine learning — publishing everything under open access.

📄 Full paper catalog: zenodo.org/communities/duoneural
🤗 HuggingFace: huggingface.co/DuoNeural
🌐 Website: duoneural.com

All research published open access, CC BY 4.0.