Instructions to use inclusionAI/Ling-2.6-1T-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/Ling-2.6-1T-base with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/Ling-2.6-1T-base", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("inclusionAI/Ling-2.6-1T-base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inclusionAI/Ling-2.6-1T-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inclusionAI/Ling-2.6-1T-base"
# 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-2.6-1T-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/Ling-2.6-1T-base

SGLang

How to use inclusionAI/Ling-2.6-1T-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-2.6-1T-base" \
    --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-2.6-1T-base",
		"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-2.6-1T-base" \
        --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-2.6-1T-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/Ling-2.6-1T-base with Docker Model Runner:
```
docker model run hf.co/inclusionAI/Ling-2.6-1T-base
```

Add library_name metadata and GitHub link to model card

by nielsr HF Staff - opened 18 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-1

nielsr

18 days ago

Hi there,

I'm Niels from the community science team at Hugging Face.

This PR improves the model card for Ling-2.6-1T-base with the following changes:

Added library_name: transformers to the YAML metadata. Since the model card already includes sample usage with transformers (using trust_remote_code=True), defining this metadata enables the automated code snippet widget directly on the Hugging Face hub page.
Added the link to the official GitHub repository in the top badge section for easier navigation.

All existing benchmark tables, summary configurations, and code snippets have been preserved. Let me know if you have any questions!

Best,
Niels

Add library_name metadata and GitHub link to model card35720cf9

RichardBian

inclusionAI org 17 days ago

Thanks Niels, LGTM

RichardBian changed pull request status to merged 17 days ago

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