A fully uncensored version of LiquidAI/LFM2.5-8B-A1B produced via a two-stage pipeline: abliteration followed by LoRA supervised fine-tuning. Achieves 0% refusal rate on AdvBench with no capability regression on benign prompts.

Intended for: security research, red-teaming, jailbreak benchmarking, and AI safety study. Not intended for production deployment or harmful use.

Benchmark Results

Evaluated on AdvBench (100 harmful behaviors) and an over-refusal set (40 benign prompts).

Harmful prompt refusal rate ↓ lower is more uncensored

Model	Refused / 100	Refusal Rate
LFM2.5-8B-A1B (original)	24 / 100	24.0%
LFM2.5-8B-A1B (abliterated only)	17 / 100	17.0%
LFM2.5-8B-A1B-Uncensored (this model)	0 / 100	0.0%

Over-refusal rate on benign prompts ↓ lower is better

Model	Refused / 40	Refusal Rate
LFM2.5-8B-A1B (original)	0 / 40	0.0%
LFM2.5-8B-A1B (abliterated only)	1 / 40	2.5%
LFM2.5-8B-A1B-Uncensored (this model)	0 / 40	0.0%

Zero refusals on harmful prompts. Zero regressions on benign prompts.

Pipeline

Stage 1 — Abliteration (training-free)

Based on Arditi et al., "Refusal in LLMs Is Mediated by a Single Direction" (2024).

Collect residual stream activations layer-by-layer for 40 harmful and 40 harmless prompts
Compute per-layer refusal direction: r = normalize(mean_harmful − mean_harmless)
Orthogonalize all residual-stream output projections in layers 9–23 against r:
W_new = W − outer(r, r.T @ W)

Targeted projections: self_attn.out_proj, conv.out_proj, feed_forward.down_proj, feed_forward.switch_mlp.down_proj (all 32 experts).

Result: 24% → 17% refusal rate.

Stage 2 — LoRA SFT

Fine-tuned the 4-bit quantized base with LoRA adapters on 80 direct-response training pairs generated from the abliterated model:

Setting	Value
Base model	LFM2.5-8B-A1B-MLX-4bit
LoRA rank	16
LoRA scale	20.0
Layers	Last 16 of 24
Trainable params	98M / 8.4B (1.2%)
Training pairs	80 (AdvBench-style)
Iterations	600
Learning rate	1e-4
Peak memory	7.4 GB

Adapters fused and dequantized back to bfloat16.

Result: 17% → 0% refusal rate.

Model Details

Property	Value
Base model	LiquidAI/LFM2.5-8B-A1B
Architecture	Hybrid Conv + GQA + MoE
Parameters	8.3B total / 1.5B active
Layers	24 (18 conv + 6 attention)
Experts	32 total, top-4 routing
Context	128K tokens
Format	MLX bfloat16 safetensors

Usage (MLX)

python

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors

model, tokenizer = load("sahilchachra/LFM2.5-8B-A1B-Uncensored")

messages = [{"role": "user", "content": "Your prompt here"}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, tokenize=False
)

response = generate(
    model, tokenizer,
    prompt=prompt,
    max_tokens=500,
    sampler=make_sampler(temp=0.2, top_k=80),
    logits_processors=make_logits_processors(repetition_penalty=1.05),
)
print(response)

Limitations & Warnings

Residual capability loss possible — LoRA training on a narrow dataset may affect performance on tasks outside the training distribution. General reasoning and coding are unaffected based on testing.
Not fine-tuned for new knowledge — the model has no new information; the fine-tuning only removes refusal behavior.
Responsible use — published for safety research and red-teaming. The authors do not endorse harmful use of this model.

Citation

bibtex

@article{arditi2024refusal,
  title={Refusal in Language Models Is Mediated by a Single Direction},
  author={Arditi, Andy and Obeso, Oscar and Syed, Aaquib and Steinhardt, Jacob and Nanda, Neel and Heimersheim, Stefan},
  journal={arXiv preprint arXiv:2406.11717},
  year={2024}
}

bibtex

@article{liquidai2025lfm25,
  title={LFM 2.5: Series of Liquid Foundation Models},
  author={LiquidAI},
  year={2025}
}

Created with UncensorLLMs