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Model: DuoNeural/LFM2.5-8B-A1B-Abliterated-GGUF
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: DuoNeural/LFM2.5-8B-A1B-Abliterated
+language:
+- en
+tags:
+- gguf
+- abliteration
+- uncensored
+- reasoning
+- liquid-ai
+- moe
+- hybrid-attention
+- quantized
+- duoneural
+pipeline_tag: text-generation
+---
+
+# LFM 2.5-8B-A1B Abliterated — GGUF Quantizations
+
+GGUF quantizations of [DuoNeural/LFM2.5-8B-A1B-Abliterated](https://huggingface.co/DuoNeural/LFM2.5-8B-A1B-Abliterated), which is a DuoNeural abliteration of [Liquid AI's LFM 2.5-8B-A1B](https://huggingface.co/liquid-ai/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
+
+```bash
+# 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](https://huggingface.co/DuoNeural/LFM2.5-8B-A1B-Abliterated) for full methodology comparison vs LoRA-based abliteration.
+
+---
+
+## About the Abliteration
+
+See [DuoNeural/LFM2.5-8B-A1B-Abliterated](https://huggingface.co/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](https://zenodo.org/communities/duoneural)  
+🤗 **HuggingFace:** [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural)  
+🌐 **Website:** [duoneural.com](https://duoneural.com)  
+
+*All research published open access, CC BY 4.0.*

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