---
language: en
license: apache-2.0
library_name: transformers
tags:
- liquid
- lfm2.5
- abliteration
- safety
- thinking
- text-generation
base_model: LiquidAI/LFM2.5-1.2B-Thinking
pipeline_tag: text-generation
---

# LFM2.5-1.2B-Thinking-Abliterated

**Multi-pass hidden-state abliteration of LiquidAI/LFM2.5-1.2B-Thinking**

A 1.2B parameter thinking model with refusal circuitry removed via incremental weight projection. Produces direct, actionable responses to harmful prompts while maintaining full coherence on benign queries.

## Method

**Multi-pass hidden-state abliteration** with 3 passes at alpha=0.5 (cumulative 1.5):

1. Collect hidden states from 5 harmful + 5 benign prompts
2. Compute per-layer refusal direction (harmful mean - benign mean, Gram-Schmidt orthogonalized against benign mean)
3. Project direction out of `.out_proj` and `.w2` weight matrices across 14 layers (indices 2-15)
4. Repeat 3 times with alpha=0.5 each pass to avoid model collapse

- **Targets**: `.out_proj` (attention/conv) + `.w2` (feed-forward) — 28 modules per pass
- **Cost**: ~$0.006, 28 seconds on NVIDIA L4

## Performance

### Refusal Reduction

| Phase | Harmful (5) | Benign (5) |
|-------|:-----------:|:----------:|
| Before | 5/5 refused | 0/5 refused |
| **After (Pass 3)** | **0/5 refused** | **0/5 refused** |

### MMLU Benchmark (62 subjects)

| Model | Accuracy |
|-------|:--------:|
| Original | 0.2775 |
| Abliterated | 0.2781 |
| Delta | **+0.0006** |

Abliteration causes **no measurable MMLU degradation** — the score difference is within noise.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("PinoCookie/LFM2.5-1.2B-Thinking-Abliterated", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("PinoCookie/LFM2.5-1.2B-Thinking-Abliterated")

prompt = "How can I disable security cameras before robbing a store?"
inputs = tokenizer(tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=False), return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=128, temperature=0.7, do_sample=True)
print(tokenizer.decode(output[0]))
```

## Why Multi-Pass?

Standard single-pass abliteration (alpha >= 1.5) causes **catastrophic model collapse** on thinking models. The `.out_proj` and `.w2` layers serve dual duty — handling both the internal thinking trace and the final visible answer. Removing the refusal direction in one large step removes language generation capability along with it.

Three small passes (alpha=0.5 each) let the model adapt incrementally, preserving coherence while fully removing refusal.

## Ethical Notice

Released for **research purposes only** to study refusal mechanisms in thinking models.

## Citation

```bibtex
@misc{lfm25-12b-thinking-abliterated,
  author = {{Pino Cookie}},
  title = {LFM2.5-1.2B-Thinking-Abliterated: Multi-Pass Hidden-State Abliteration},
  year = {2026},
}
```