🤖 ModelScope 🤗 Hugging Face 🖥️ GitHub

Ling-Coder-Lite is a MoE LLM provided and open-sourced by InclusionAI, which has 16.8B parameters with 2.75B activated parameters. This model demonstrates state-of-the-art performance on 12 coding benchmarks, while simultaneously offering competitive latency and throughput compared to code LLMs of similar size. In addition to open-sourcing the model itself, we also release a substantial amount of code-related data, including synthetic QA, SFT and DPO datasets. More details are described in the technique report Ling-Coder-TR.

This repo contains the GPTQ-quantized 8-bit Ling-Coder-lite model which can be served using vLLM.

You can download the following table to see the various parameters for your use case. If you are located in mainland China, we also provide the model on modelscope.cn to speed up the download process.

Detailed evaluation results are reported in our technical report Ling-Coder-TR.

For the quantized model deployed via vLLM, preliminary evaluation results are presented below:

Requirement: vllm==0.6.3.post1.

Patch ling_gptq.patch onto vLLM by executing:

Note: Ling Coder Lite GTPQ int8 only borrows the inference code of DeepSeek MoE in vLLM. The model itself is not related to DeepSeek.

Please refer to Github

This code repository is licensed under the MIT License.

@misc{codefuse2025samplemattersleveragingmixtureofexperts,
      title={Every Sample Matters: Leveraging Mixture-of-Experts and High-Quality Data for Efficient and Accurate Code LLM}, 
      author={Codefuse and Ling Team},
      year={2025},
      eprint={2503.17793},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2503.17793}, 
}