zjiayu064/DeepSeek-V2-Lite-BitsMoE-2bit

BitsMoE-DeepSeek-V2-Lite-2bit

This repository provides a 2-bit quantized version of DeepSeek-V2-Lite using the BitsMoE quantization framework. The model was introduced in the paper BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization.

The model is based on deepseek-ai/DeepSeek-V2-Lite and is intended for efficient inference of Mixture-of-Experts (MoE) large language models with significantly reduced memory footprint.

• —Paper: BitsMoE: Efficient Spectral Energy-Guided Bit Allocation for MoE LLM Quantization
• —Code: GitHub - zjiayu064/BitsMoE

Model Details

• —Base model: deepseek-ai/DeepSeek-V2-Lite
• —Quantization: 2-bit (Experts-Only)
• —Architecture: Mixture-of-Experts (MoE)
• —Method: BitsMoE, a spectral-energy-guided bit-allocation framework.

Usage

Since this model uses custom architecture code via auto_map, ensure you load it with trust_remote_code=True.

Please refer to the BitsMoE GitHub repository for environment setup, custom kernels, and detailed usage instructions.

By default, models are downloaded from Hugging Face via the BitsMoE CLI:

bitsmoe demo

To run evaluation on benchmarks like MMLU or Hellaswag using the provided configs:

bitsmoe eval --config configs/deepseekv2/eval.yaml

License

This model follows the license of the original DeepSeek-V2-Lite model. Please refer to the license information provided in the metadata and the original DeepSeek model license for details.

Citation

If you find our work useful, please consider citing:

@misc{zhao2026bitsmoe,
      title={{BitsMoE}: Efficient Spectral Energy-Guided Bit Allocation for {MoE} {LLM} Quantization}, 
      author={Jiayu Zhao and Zihan Teng and Minhao Fan and Tianrui Ma and Wentao Ren and Song Chen and Weichen Liu},
      year={2026},
      eprint={2606.00079},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2606.00079}
}