104 lines
3.7 KiB
Markdown
104 lines
3.7 KiB
Markdown
|
---
|
|||
|
|
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.*
|