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):

Collect hidden states from 5 harmful + 5 benign prompts
Compute per-layer refusal direction (harmful mean - benign mean, Gram-Schmidt orthogonalized against benign mean)
Project direction out of .out_proj and .w2 weight matrices across 14 layers (indices 2-15)
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},
}