Instructions to use moonshotai/Kimi-K2-Instruct-0905 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moonshotai/Kimi-K2-Instruct-0905 with Transformers:

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

pipe = pipeline("text-generation", model="moonshotai/Kimi-K2-Instruct-0905", 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-0905", trust_remote_code=True, device_map="auto")

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moonshotai/Kimi-K2-Instruct-0905 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-0905"
# 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-0905",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moonshotai/Kimi-K2-Instruct-0905

SGLang

How to use moonshotai/Kimi-K2-Instruct-0905 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-0905" \
    --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-0905",
		"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-0905" \
        --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-0905",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use moonshotai/Kimi-K2-Instruct-0905 with Docker Model Runner:
```
docker model run hf.co/moonshotai/Kimi-K2-Instruct-0905
```

wangzhengtao commited on Oct 21, 2025

Commit

85874df

1 Parent(s): 09d5f93

fix apply_chat_templation for vllm

Browse files

Files changed (1) hide show

tokenization_kimi.py +9 -2

tokenization_kimi.py CHANGED Viewed

@@ -326,10 +326,17 @@ class TikTokenTokenizer(PreTrainedTokenizer):
     def apply_chat_template(
-        self, conversation, tools: Optional[list[dict]] = None, **kwargs
     ):
         tools = deep_sort_dict(tools)
-        return super().apply_chat_template(conversation, tools=tools, **kwargs)
 def deep_sort_dict(obj: Any) -> Any:

     def apply_chat_template(
+        self, conversation, tools: Optional[list[dict]] = None,
+        tokenize: bool = False,
+        add_generation_prompt: bool = True,
+        **kwargs
     ):
         tools = deep_sort_dict(tools)
+        return super().apply_chat_template(conversation,
+                                           tools=tools,
+                                           tokenize=tokenize,
+                                           add_generation_prompt=add_generation_prompt,
+                                           **kwargs)
 def deep_sort_dict(obj: Any) -> Any: