Instructions to use LLM360/K2-Think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM360/K2-Think with Transformers:

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

pipe = pipeline("text-generation", model="LLM360/K2-Think")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM360/K2-Think")
model = AutoModelForCausalLM.from_pretrained("LLM360/K2-Think")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LLM360/K2-Think with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM360/K2-Think"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LLM360/K2-Think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM360/K2-Think

SGLang

How to use LLM360/K2-Think 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 "LLM360/K2-Think" \
    --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": "LLM360/K2-Think",
		"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 "LLM360/K2-Think" \
        --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": "LLM360/K2-Think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM360/K2-Think with Docker Model Runner:
```
docker model run hf.co/LLM360/K2-Think
```

Improve model card: Add paper, code, and project page links, and main title

by nielsr HF Staff - opened Sep 10, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

nielsr

Sep 10, 2025

This PR enhances the model card by:

Adding the paper title as a prominent H1 heading for better structure.
Adding direct links to the paper (https://huggingface.co/papers/2509.07604), the GitHub repository (https://github.com/MBZUAI-IFM/K2-Think-SFT), and the project page (https://k2think.ai) at the top of the model card. This makes essential resources more discoverable for users.
Ensuring proper Markdown rendering for special characters such as \~ in the "Inference Speed" table by escaping them as \\~.

These updates improve the model card's clarity, navigability, and adherence to best practices for documenting artifacts on the Hub.

Improve model card: Add paper, code, and project page links, and main title46b668c9

aaryamonvikram

LLM360 org Sep 11, 2025

We have accepted some of the suggested edits. Thank you for your contribution.

aaryamonvikram changed pull request status to closed Sep 11, 2025

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