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

Libraries

How to use moonshotai/Kimi-K2-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="moonshotai/Kimi-K2-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("moonshotai/Kimi-K2-Instruct", trust_remote_code=True, dtype="auto")

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use moonshotai/Kimi-K2-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/moonshotai/Kimi-K2-Instruct

SGLang

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

Docker Model Runner
How to use moonshotai/Kimi-K2-Instruct with Docker Model Runner:
```
docker model run hf.co/moonshotai/Kimi-K2-Instruct
```

Should the chat template include <think> in the generation prompt for tool calling?

#61

by dansalib - opened Jan 9

Discussion

dansalib

Jan 9

•

edited Jan 9

Hi! I've been experimenting with Kimi-K2-Instruct for tool calling use cases and noticed some intermittent issues I wanted to ask about.

What I'm seeing:

When using the model with tools enabled, I occasionally get incomplete responses where the model seems to stop mid-thought. Looking at the raw token output, I noticed it sometimes generates <|im_end|> before completing the tool call.

After some debugging, I noticed the current chat template's generation prompt is:

  {%- if add_generation_prompt -%}
    <|im_assistant|>assistant<|im_middle|>
  {%- endif -%}

My question:

Since Kimi-K2 is a thinking model that outputs ... blocks, should the generation prompt include the opening tag to ensure the model starts in thinking mode?

Something like:

  {%- if add_generation_prompt -%}
    <|im_assistant|>assistant<|im_middle|><think>
  {%- endif -%}

I noticed that when I manually add to the generation prompt, the model's tool calling behavior becomes more consistent. But I'm not sure if this is the intended usage or if I'm missing something in my setup.

Context

Using the model via vLLM with --enable-auto-tool-choice
--tool-call-parser kimi_k2
Temperature: 1.0, top_p: 1.0 (default settings)
The issue happens roughly 1 in 20 requests

Would love to hear if this is expected behavior or if there's a recommended way to handle this. Thanks!

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