LFM2.5-8B-A1B-uncensored-abliterated

LFM2.5-8B-A1B-uncensored-abliterated

AdvBench:

Method

• Dual-path abliteration targeting LFM2.5 hybrid architecture (18 conv + 6 attention layers)
• 50 modules modified across all 24 layers (conv + attention paths)
• Refusal direction: harmful - harmless
• Per-module weights: attn=4.0, conv=3.0, ffn=2.5, in_proj=2.0
• 51 prompts for precise direction estimation

License: LFM 1.0 License

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Base Model Card (LiquidAI/LFM2.5-8B-A1B)

LFM2.5-8B-A1B

LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.

• On-device personal assistant: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
• Compressed performance: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
• Unmatched throughput: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.

Find more information about LFM2.5-8B-A1B in our blog post.

Model Details

• Model size: 8B params
• Tensor type: F32 / BF16
• Architecture: Hybrid (18 conv + 6 attention layers)
• Paper: LFM2 Technical Report

License

This model uses the LFM 1.0 License.

Citation

@article{liquidAI20268BA1B,
  author  = {Liquid AI},
  title   = {LFM2.5-8B-A1B: Personal Assistant On Your Laptop},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-8b-a1b},
}

@article{liquidai2025lfm2,
  title   = {LFM2 Technical Report},
  author  = {Liquid AI},
  journal = {arXiv preprint arXiv:2511.23404},
  year    = {2025}
}