104 lines
3.7 KiB
Markdown
104 lines
3.7 KiB
Markdown
---
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license: apache-2.0
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base_model: DuoNeural/LFM2.5-8B-A1B-Abliterated
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language:
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- en
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tags:
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- gguf
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- abliteration
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- uncensored
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- reasoning
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- liquid-ai
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- moe
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- hybrid-attention
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- quantized
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- duoneural
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pipeline_tag: text-generation
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---
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# LFM 2.5-8B-A1B Abliterated — GGUF Quantizations
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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).
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**All capabilities fully preserved.** 100% factual accuracy, JSON generation, and math reasoning. 5/6 refusal behavior removed.
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---
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## Available Files
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| Filename | Quantization | Size | RAM Required |
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|----------|-------------|------|--------------|
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| `lfm25-8b-abliterated-q5_k_m.gguf` | Q5_K_M | 5.7 GB | ~8 GB |
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| `lfm25-8b-abliterated-q4_k_m.gguf` | Q4_K_M | 4.9 GB | ~6 GB |
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**Recommended:** Q5_K_M for best quality/size balance.
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## Inference Speed Benchmarks
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| Hardware | Quantization | Generation | Prefill | Context |
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|----------|-------------|------------|---------|---------|
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| NVIDIA GTX 1070 (8GB) | Q4_K_M | **63.88 t/s** | 0.41s | 128k (full) ✓ |
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| NVIDIA GTX 1070 (8GB) | Q5_K_M | **31.05 t/s** | 0.70s | 64k confirmed ✓ |
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| NVIDIA RTX 3090 (pod) | Q4_K_M | **388 t/s** | ~250 t/s | 128k ✓ |
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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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---
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## Usage with llama.cpp
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```bash
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# Download
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huggingface-cli download DuoNeural/LFM2.5-8B-A1B-Abliterated-GGUF \
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lfm25-8b-abliterated-q5_k_m.gguf
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# Run
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./llama-cli -m lfm25-8b-abliterated-q5_k_m.gguf \
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--chat-template chatml \
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-p "Your prompt here" \
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-n 512
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```
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## Usage with Ollama / LM Studio
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Import the GGUF file directly. The model uses ChatML template format.
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---
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## KL Divergence (Distribution Shift)
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Measured via Heretic v1.2.0 methodology (100 benign prompts, full vocab, first-token logits):
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| Metric | Value |
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|--------|-------|
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| KL(abliterated \|\| original) | **1.06 × 10⁻⁷ nats** |
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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.
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---
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## About the Abliteration
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See [DuoNeural/LFM2.5-8B-A1B-Abliterated](https://huggingface.co/DuoNeural/LFM2.5-8B-A1B-Abliterated) for full methodology and validation results.
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**Quick summary:**
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- Targets all 6 full-attention GQA layers (positions 2, 6, 10, 14, 18, 21)
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- alpha=1.5 for reasoning model (higher than standard due to `<think>` CoT architecture)
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- Correct WRITER and READER projection formulas
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- 5/6 test prompts answered post-abliteration; original answered ~1/6
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- Zero capability degradation
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> **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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---
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## About DuoNeural
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**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.
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📄 **Full paper catalog:** [zenodo.org/communities/duoneural](https://zenodo.org/communities/duoneural)
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🤗 **HuggingFace:** [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural)
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🌐 **Website:** [duoneural.com](https://duoneural.com)
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*All research published open access, CC BY 4.0.*
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