Instructions to use inclusionAI/Ling-Coder-lite-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ling-Coder-lite-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="inclusionAI/Ling-Coder-lite-base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ling-Coder-lite-base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/Ling-Coder-lite-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ling-Coder-lite-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-Coder-lite-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/inclusionAI/Ling-Coder-lite-base

SGLang

How to use inclusionAI/Ling-Coder-lite-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "inclusionAI/Ling-Coder-lite-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-Coder-lite-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "inclusionAI/Ling-Coder-lite-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-Coder-lite-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use inclusionAI/Ling-Coder-lite-base with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ling-Coder-lite-base
```

twelveand0 commited on Mar 25, 2025

Commit

dbd6ca2

verified ·

1 Parent(s): e16c6aa

Update the technique report citation

Browse files

Files changed (1) hide show

README.md +12 -1

README.md CHANGED Viewed

@@ -94,4 +94,15 @@ Please refer to [Github](https://github.com/inclusionAI/Ling/blob/master/README.
 This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ling-Coder-lite/blob/main/LICENCE).
 ## Citation
-[TBD]

 This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ling-Coder-lite/blob/main/LICENCE).
 ## Citation
+```
+@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 and : and Wenting Cai and Yuchen Cao and Chaoyu Chen and Chen Chen and Siba Chen and Qing Cui and Peng Di and Junpeng Fang and Zi Gong and Ting Guo and Zhengyu He and Yang Huang and Cong Li and Jianguo Li and Zheng Li and Shijie Lian and BingChang Liu and Songshan Luo and Shuo Mao and Min Shen and Jian Wu and Jiaolong Yang and Wenjie Yang and Tong Ye and Hang Yu and Wei Zhang and Zhenduo Zhang and Hailin Zhao and Xunjin Zheng and Jun Zhou},
+      year={2025},
+      eprint={2503.17793},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2503.17793},
+}
+```