Instructions to use inclusionAI/Ling-mini-2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ling-mini-2.0 with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ling-mini-2.0", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ling-mini-2.0", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ling-mini-2.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ling-mini-2.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-mini-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ling-mini-2.0

SGLang

How to use inclusionAI/Ling-mini-2.0 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-mini-2.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-mini-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-mini-2.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inclusionAI/Ling-mini-2.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ling-mini-2.0 with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ling-mini-2.0
```

RichardBian commited on 24 days ago

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Update training content summary

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@@ -306,4 +306,17 @@ This code repository is licensed under [the MIT License](https://github.com/incl
 If you find our work helpful, feel free to give us a cite.
 ![--](https://ospo-insights.oss-cn-hangzhou.aliyuncs.com/iai-hf-models/Ling-mini-2.0.gif)

 If you find our work helpful, feel free to give us a cite.
+<!-- TDS-LINKS:START -->
+## Training content summary
+The public training-content summary identifying **Ling-mini-2.0** is available below. Please refer to the document for its covered model versions, training-content scope, summary version, and update date.
+- [Training-content summary (PDF)](https://huggingface.co/inclusionAI/AI-Transparency/resolve/main/Ling-2.0_TDS-Summary.pdf)
+- [Training-content documentation index](https://huggingface.co/inclusionAI/AI-Transparency)
+This summary concerns training-content disclosure; it does not replace the model’s technical documentation, usage terms, or license.
+<!-- TDS-LINKS:END -->
 ![--](https://ospo-insights.oss-cn-hangzhou.aliyuncs.com/iai-hf-models/Ling-mini-2.0.gif)