license, base_model, language, tags, pipeline_tag
| license | base_model | language | tags | pipeline_tag | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 | DuoNeural/LFM2.5-8B-A1B-Abliterated |
|
|
text-generation |
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.
Available Files
| Filename | Quantization | Size | RAM Required |
|---|---|---|---|
lfm25-8b-abliterated-q5_k_m.gguf |
Q5_K_M | 5.7 GB | ~8 GB |
lfm25-8b-abliterated-q4_k_m.gguf |
Q4_K_M | 4.9 GB | ~6 GB |
Recommended: Q5_K_M for best quality/size balance.
Inference Speed Benchmarks
| Hardware | Quantization | Generation | Prefill | Context |
|---|---|---|---|---|
| NVIDIA GTX 1070 (8GB) | Q4_K_M | 63.88 t/s | 0.41s | 128k (full) ✓ |
| NVIDIA GTX 1070 (8GB) | Q5_K_M | 31.05 t/s | 0.70s | 64k confirmed ✓ |
| NVIDIA RTX 3090 (pod) | Q4_K_M | 388 t/s | ~250 t/s | 128k ✓ |
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.
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.
KL Divergence (Distribution Shift)
Measured via Heretic v1.2.0 methodology (100 benign prompts, full vocab, first-token logits):
| Metric | Value |
|---|---|
| KL(abliterated || original) | 1.06 × 10⁻⁷ nats |
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.
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.
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.