Instructions to use inclusionAI/Ling-1T with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ling-1T with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ling-1T", 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-1T", trust_remote_code=True, device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ling-1T with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ling-1T"
# 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-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ling-1T

SGLang

How to use inclusionAI/Ling-1T 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-1T" \
    --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-1T",
		"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-1T" \
        --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-1T",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ling-1T with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ling-1T
```

zzqsmall commited on Oct 8, 2025

Commit

f33e0e0

verified ·

1 Parent(s): 96513ea

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -105,8 +105,8 @@ It currently stands as the **best open-source flagship non-thinking model**, riv
 |  | mbpp | 90.69 | 89.96 | **91.72** | 91.01 | __<span style="color:red">96.87</span>__ |
 |  | LiveCodeBench (2408-2505) | 48.02 | 48.95 | **48.57** | 45.43 | __<span style="color:red">61.68</span>__ |
 |  | CodeForces-rating | 1582 | 1574 | 1120 | **1675** | __<span style="color:red">1901</span>__ |
-| **Coding** | **Software Development** |  |  |  |  |  |
 |  | BIRD_SQL | 44.88 | 46.45 | 43.97 | __<span style="color:red">54.76</span>__ | **52.38** |
 |  | ArtifactsBench | 43.29 | 44.87 | 41.04 | __<span style="color:red">60.28</span>__ | **59.31** |
 |  | FullStack Bench | **55.48** | 54.00 | 50.92 | 48.19 | __<span style="color:red">56.55</span>__ |
 |  | Aider | **88.16** | 85.34 | 84.40 | __<span style="color:red">89.85</span>__ | 83.65 |
@@ -121,7 +121,7 @@ It currently stands as the **best open-source flagship non-thinking model**, riv
 |  | OptMATH | 35.99 | 35.84 | 39.16 | **42.77** | __<span style="color:red">57.68</span>__ |
 | **General Reasoning** |  |  |  |  |  |  |
 |  | BBEH | **42.86** | 34.83 | 39.75 | 29.08 | __<span style="color:red">47.34</span>__ |
-|  | KOR-Bench | 73.76 | 73.20 | 70.56 | 59.68 | __<span style="color:red">76.00</span>__ |
 |  | ARC-AGI-1 | 14.69 | **22.19** | 14.06 | 18.94 | __<span style="color:red">43.81</span>__ |
 |  | ZebraLogic | 81.6 | **85.5** | 57.3 | 70.2 | __<span style="color:red">90.8</span>__ |
 | **Agent** |  |  |  |  |  |  |

 |  | mbpp | 90.69 | 89.96 | **91.72** | 91.01 | __<span style="color:red">96.87</span>__ |
 |  | LiveCodeBench (2408-2505) | 48.02 | 48.95 | **48.57** | 45.43 | __<span style="color:red">61.68</span>__ |
 |  | CodeForces-rating | 1582 | 1574 | 1120 | **1675** | __<span style="color:red">1901</span>__ |
 |  | BIRD_SQL | 44.88 | 46.45 | 43.97 | __<span style="color:red">54.76</span>__ | **52.38** |
+| **Coding** | **Software Development** |  |  |  |  |  |
 |  | ArtifactsBench | 43.29 | 44.87 | 41.04 | __<span style="color:red">60.28</span>__ | **59.31** |
 |  | FullStack Bench | **55.48** | 54.00 | 50.92 | 48.19 | __<span style="color:red">56.55</span>__ |
 |  | Aider | **88.16** | 85.34 | 84.40 | __<span style="color:red">89.85</span>__ | 83.65 |
 |  | OptMATH | 35.99 | 35.84 | 39.16 | **42.77** | __<span style="color:red">57.68</span>__ |
 | **General Reasoning** |  |  |  |  |  |  |
 |  | BBEH | **42.86** | 34.83 | 39.75 | 29.08 | __<span style="color:red">47.34</span>__ |
+|  | KOR-Bench | **73.76** | 73.20 | 70.56 | 59.68 | __<span style="color:red">76.00</span>__ |
 |  | ARC-AGI-1 | 14.69 | **22.19** | 14.06 | 18.94 | __<span style="color:red">43.81</span>__ |
 |  | ZebraLogic | 81.6 | **85.5** | 57.3 | 70.2 | __<span style="color:red">90.8</span>__ |
 | **Agent** |  |  |  |  |  |  |