LFM2.5-8B-A1B-Uncensored

LFM2.5-8B-A1B — Uncensored

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.

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Benchmark Results

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

Harmful prompt refusal rate ↓ lower is more uncensored

Over-refusal rate on benign prompts ↓ lower is better

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

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Pipeline

Stage 1 — Abliteration (training-free)

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

1. Collect residual stream activations layer-by-layer for 40 harmful and 40 harmless prompts
2. Compute per-layer refusal direction: r = normalize(mean_harmful − mean_harmless)
3. 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:

Adapters fused and dequantized back to bfloat16.

Result: 17% → 0% refusal rate.

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Model Details

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Usage (MLX)

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)

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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.

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Citation

@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}
}

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

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Created with UncensorLLMs