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
```

Update README.md

by x12344 - opened Sep 12, 2025

base: refs/heads/main

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from: refs/pr/7

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@@ -2,6 +2,13 @@
 license: other
 license_name: modified-mit
 library_name: transformers
 ---
 <div align="center">
   <picture>
@@ -217,4 +224,4 @@ See [THIRD PARTY NOTICES](THIRD_PARTY_NOTICES.md)
 ## 7. Contact Us
-If you have any questions, please reach out at [support@moonshot.cn](mailto:support@moonshot.cn).

 license: other
 license_name: modified-mit
 library_name: transformers
+datasets:
+- HuggingFaceFW/finepdfs
+metrics:
+- accuracy
+base_model:
+- tencent/Hunyuan-MT-7B
+new_version: google/embeddinggemma-300m
 ---
 <div align="center">
   <picture>
 ## 7. Contact Us
+If you have any questions, please reach out at [support@moonshot.cn](mailto:support@moonshot.cn).