Instructions to use LLM360/K2-Think-V2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM360/K2-Think-V2 with Transformers:

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

pipe = pipeline("text-generation", model="LLM360/K2-Think-V2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM360/K2-Think-V2")
model = AutoModelForCausalLM.from_pretrained("LLM360/K2-Think-V2")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LLM360/K2-Think-V2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM360/K2-Think-V2"
# 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-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM360/K2-Think-V2

SGLang

How to use LLM360/K2-Think-V2 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-V2" \
    --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-V2",
		"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-V2" \
        --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-V2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM360/K2-Think-V2 with Docker Model Runner:
```
docker model run hf.co/LLM360/K2-Think-V2
```

Update README.md

by xudongh1 - opened Jan 26

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-3

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -36,7 +36,7 @@ We use the following serving configurations:
 The provided chat template sets the reasoning effort to `high`
 ### Transformers
-You can use `K2 Think (Jan '26)` with Transformers. If you use `transformers.pipeline`, it will apply the chat template automatically. If you use `model.generate` directly, you need to apply the chat template mannually.
 The chat template is directly inherited from K2-V2-Instruct, with the default `reasoning_effort` set to `"high"`. The other levels of reasoning effort (`"low"` and `"medium"`) are still available but have not been tested or evaluated. As such, the model's behavior under such settings is not assured to maintain reported performance.
@@ -75,7 +75,7 @@ client = OpenAI(
 )
 completion = client.chat.completions.create(
-    model="LLM360/K2-Think-0126",
     messages = [
         {"role": "system", "content": "You are K2-Think, a helpful assistant created by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Institute of Foundation Models (IFM)."},
         {"role": "user", "content": "Solve the 24 game [2, 3, 5, 6]"}
@@ -132,7 +132,7 @@ We have employed various techniques to reduce bias, harmful outputs, and other r
 ---
 # Citation
-If you use K2 Think (Jan '26) in your research, please use the following citation:
 ```bibtex
 @misc{k2think2026k2think0126,

 The provided chat template sets the reasoning effort to `high`
 ### Transformers
+You can use `K2 Think V2` with Transformers. If you use `transformers.pipeline`, it will apply the chat template automatically. If you use `model.generate` directly, you need to apply the chat template mannually.
 The chat template is directly inherited from K2-V2-Instruct, with the default `reasoning_effort` set to `"high"`. The other levels of reasoning effort (`"low"` and `"medium"`) are still available but have not been tested or evaluated. As such, the model's behavior under such settings is not assured to maintain reported performance.
 )
 completion = client.chat.completions.create(
+    model="LLM360/K2-Think-V2",
     messages = [
         {"role": "system", "content": "You are K2-Think, a helpful assistant created by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Institute of Foundation Models (IFM)."},
         {"role": "user", "content": "Solve the 24 game [2, 3, 5, 6]"}
 ---
 # Citation
+If you use K2 Think V2 in your research, please use the following citation:
 ```bibtex
 @misc{k2think2026k2think0126,