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

Libraries

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

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

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

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ling-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-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-lite-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use inclusionAI/Ling-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-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-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-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-lite-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

m1ngcheng

nielsr HF Staff commited on Mar 18, 2025

Commit

6a4f845

verified ·

1 Parent(s): af571a3

Add library name and pipeline tag (#1)

Browse files

- Add library name and pipeline tag (0b1d89c0cec3fee1da5c7577983e96d74362a4c4)

Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +4 -0

README.md CHANGED Viewed

@@ -1,5 +1,7 @@
 ---
 license: mit
 ---
 # Ling
@@ -83,4 +85,6 @@ 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-lite-base/blob/main/LICENCE).
 ## Citation
 [TBD]

 ---
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
 ---
 # Ling
 This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ling-lite-base/blob/main/LICENCE).
 ## Citation
+Paper: https://hf.co/papers/2503.05139
 [TBD]