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

da4debb

verified ·

1 Parent(s): a1d8dd4

Update README.md

Browse files

Files changed (1) hide show

README.md +6 -6

README.md CHANGED Viewed

@@ -152,7 +152,7 @@ Note: If you are interested in previous version, please visit the past model col
 ### 🚀 Try Online
-You can experience Ling-1T online at: [ZenMux](https://zenmux.ai/inclusionai/Ling-1T?utm_source=hf_inclusionAI)
 ### 🔌 API Usage
@@ -172,7 +172,7 @@ client = OpenAI(
 # 4. Make a request
 completion = client.chat.completions.create(
     # 5. Specify the model to use in the format "provider/model-name"
-    model="inclusionai/Ling-1T",
     messages=[
         {
             "role": "user",
@@ -191,7 +191,7 @@ Here is a code snippet to show you how to use the chat model with `transformers`
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "inclusionAI/Ling-1T"
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
@@ -246,11 +246,11 @@ pip install vllm==0.11.0
 from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
-tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ling-1T")
 sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=16384)
-llm = LLM(model="inclusionAI/Ling-1T", dtype='bfloat16', trust_remote_code=True)
 prompt = "Give me a short introduction to large language models."
 messages = [
     {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
@@ -269,7 +269,7 @@ outputs = llm.generate([text], sampling_params)
 #### Online Inference:
 ```bash
-vllm serve inclusionAI/Ling-1T \
               --tensor-parallel-size 32 \
               --pipeline-parallel-size 1 \
               --trust-remote-code \

 ### 🚀 Try Online
+You can experience Ling-1T online at: [ZenMux](https://zenmux.ai/inclusionai/ling-1t?utm_source=hf_inclusionAI)
 ### 🔌 API Usage
 # 4. Make a request
 completion = client.chat.completions.create(
     # 5. Specify the model to use in the format "provider/model-name"
+    model="inclusionai/ling-1t",
     messages=[
         {
             "role": "user",
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "inclusionAI/ling-1t"
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
 from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
+tokenizer = AutoTokenizer.from_pretrained("inclusionAI/ling-1t")
 sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=16384)
+llm = LLM(model="inclusionAI/ling-1t", dtype='bfloat16', trust_remote_code=True)
 prompt = "Give me a short introduction to large language models."
 messages = [
     {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
 #### Online Inference:
 ```bash
+vllm serve inclusionAI/ling-1t \
               --tensor-parallel-size 32 \
               --pipeline-parallel-size 1 \
               --trust-remote-code \