Instructions to use moonshotai/Kimi-Linear-48B-A3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moonshotai/Kimi-Linear-48B-A3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="moonshotai/Kimi-Linear-48B-A3B-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-Linear-48B-A3B-Instruct", trust_remote_code=True, dtype="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moonshotai/Kimi-Linear-48B-A3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moonshotai/Kimi-Linear-48B-A3B-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-Linear-48B-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/moonshotai/Kimi-Linear-48B-A3B-Instruct

SGLang

How to use moonshotai/Kimi-Linear-48B-A3B-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-Linear-48B-A3B-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-Linear-48B-A3B-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-Linear-48B-A3B-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-Linear-48B-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use moonshotai/Kimi-Linear-48B-A3B-Instruct with Docker Model Runner:
```
docker model run hf.co/moonshotai/Kimi-Linear-48B-A3B-Instruct
```

yzhangcs commited on Nov 17, 2025

Commit

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@@ -5,13 +5,28 @@ library_name: transformers
 ---
 <div align="center">
-  <a href="https://huggingface.co/papers/2510.26692"><img width="80%" src="figures/banner.png"></a>
 </div>
 <div align="center">
-  <a href="https://huggingface.co/papers/2510.26692" ><img src="figures/logo.png" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"><b style="display: inline-block;"> Tech Report</b></a>  |
-  <a href="https://github.com/MoonshotAI/Kimi-Linear"><img src="figures/github.png" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"><b style="display: inline-block;"> Code</b></a> |
-  <a href="https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct"><img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"><b style="display: inline-block;"> HuggingFace</b></a>
 </div>
 <div align="center">

 ---
 <div align="center">
+<h3 align="center">
+  <b>
+    <span>━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span>
+    <br/>
+    <img src="figures/logo.png" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"> Kimi Linear: An Expressive, Efficient Attention Architecture
+    <br/>
+    <span>━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span>
+    <br/>
+  </b>
+</h3>
 </div>
 <div align="center">
+  <a href="https://huggingface.co/papers/2510.26692" style="margin: 0 8px;">
+    <img src="figures/arxiv.png" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"><b> Paper</b>
+  </a>
+  <a href="https://github.com/MoonshotAI/Kimi-Linear" style="margin: 0 8px;">
+    <img src="figures/github.png" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"><b> Code</b>
+  </a>
+  <a href="https://huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct" style="margin: 0 8px;">
+    <img src="https://huggingface.co/front/assets/huggingface_logo-noborder.svg" height="16" width="16" style="display: inline-block; vertical-align: middle; margin: 2px;"><b> HuggingFace</b>
+  </a>
 </div>
 <div align="center">