Ling-Coder-Lite

🤗 Hugging Face   |   🤖 ModelScope   |   🖥️ GitHub

Introduction

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.

Model Downloads

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.

Model	#Total Params	#Activated Params	Context Length	Download
Ling-Coder-lite-base	16.8B	2.75B	16K	🤗 HuggingFace 🤖 ModelScope
Ling-Coder-lite	16.8B	2.75B	16K	🤗 HuggingFace 🤖 ModelScope

Dataset Downloads

Model	Samples	Download
Ling-Coder-SyntheticQA	24M	🤗 HuggingFace 🤖 ModelScope
Ling-Coder-SFT	5M	🤗 HuggingFace 🤖 ModelScope
Ling-Coder-DPO	250K	🤗 HuggingFace 🤖 ModelScope

Evaluation

Detailed evaluation results are reported in our technical report.

Quickstart

🤗 Hugging Face Transformers

Here is a code snippet to show you how to use the chat model with transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "inclusionAI/Ling-Coder-lite"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    model_name, 
    trust_remote_code=True
)

prompt = "Write a quick sort algorithm in python."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

🤖 ModelScope

If you're in mainland China, we strongly recommend you to use our model from 🤖 ModelScope.

Deployment

vLLM

vLLM supports offline batched inference or launching an OpenAI-Compatible API Service for online inference.

Environment Preparation

Since the Pull Request (PR) has not been submitted to the vLLM community at this stage, please prepare the environment by following the steps below:

git clone -b  v0.7.3 https://github.com/vllm-project/vllm.git
cd vllm
git apply Ling-Coder-Lite/inference/vllm/bailing_moe.patch
pip install -e .

Offline Inference:

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ling-Coder-lite")

sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=512)

llm = LLM(model="inclusionAI/Ling-Coder-lite", dtype='bfloat16')
prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are Ling-Coder-Lite, an assistant created by CodeFuse-AI"},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
outputs = llm.generate([text], sampling_params)

Online Inference:

vllm serve inclusionAI/Ling-lite \
              --tensor-parallel-size 2 \
              --pipeline-parrallel-size 1 \
              --use-v2-block-manager \
              --gpu-memory-utilization 0.90

For detailed guidance, please refer to the vLLM instructions.

Finetuning

We recommend you to use Llama-Factory to finetune Ling with SFT, DPO, etc.

We use identity to demonstrate how to finetune our Ling models by replacing name with Ling and author with inclusionAI.

{
  "instruction": "hi",
  "input": "",
  "output": "Hello! I am Ling-Coder-Lite, an AI assistant developed by CodeFuse-AI. How can I assist you today?"
}

We provide a demo configuration of Llama-Factory to SFT Ling models as follows:

llamafactory-cli train examples/sft/ling_full_sft.yaml

License

This code repository is licensed under the MIT License.

Citation

If you find our work is useful or helpful, please feel free to cite our paper as below.

@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}, 
}