Instructions to use moonshotai/Kimi-VL-A3B-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use moonshotai/Kimi-VL-A3B-Thinking with Transformers:

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

pipe = pipeline("image-text-to-text", model="moonshotai/Kimi-VL-A3B-Thinking", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("moonshotai/Kimi-VL-A3B-Thinking", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use moonshotai/Kimi-VL-A3B-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "moonshotai/Kimi-VL-A3B-Thinking"
# 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-VL-A3B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/moonshotai/Kimi-VL-A3B-Thinking

SGLang

How to use moonshotai/Kimi-VL-A3B-Thinking 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-VL-A3B-Thinking" \
    --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-VL-A3B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-VL-A3B-Thinking" \
        --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-VL-A3B-Thinking",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use moonshotai/Kimi-VL-A3B-Thinking with Docker Model Runner:
```
docker model run hf.co/moonshotai/Kimi-VL-A3B-Thinking
```

nielsr HF Staff commited on Apr 11, 2025

Commit

84052e3

verified ·

1 Parent(s): 54d622b

Add library_name metadata and link to paper

Browse files

This PR ensures the model card is linked to the relevant paper, while also ensuring a "how to use" button
appears on the top right.

Files changed (1) hide show

README.md +6 -9

README.md CHANGED Viewed

@@ -1,17 +1,15 @@
 ---
-license: mit
 base_model:
 - moonshotai/Kimi-VL-A3B-Instruct
 pipeline_tag: image-text-to-text
 ---
 <div align="center">
   <img width="30%" src="figures/logo.png">
 </div>
 ## Introduction
 We present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding, and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B).
@@ -26,6 +24,8 @@ Kimi-VL also advances the pareto frontiers of multimodal models in processing lo
 Building on this foundation, we introduce an advanced long-thinking variant: **Kimi-VL-Thinking**. Developed through long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL), this model exhibits strong long-horizon reasoning capabilities. It achieves scores of 61.7 on MMMU, 36.8 on MathVision, and 71.3 on MathVista while maintaining the compact 2.8B activated LLM parameter footprint, setting a new standard for efficient yet capable multimodal **thinking** models.
 ## Architecture
 The model adopts an MoE language model, a native-resolution visual encoder (MoonViT), and an MLP projector, as illustrated in the following image.
@@ -59,7 +59,6 @@ Full comparison on MMMU, MathVision, and MathVista-mini:
 <div align="center">
 | Benchmark (Metric)              | GPT-4o | GPT-4o-mini | Qwen2.5-VL-72B | Qwen2.5-VL-7B | Gemma-3-27B | Gemma-3-12B | o1-1217 | QVQ-72B | Kimi-k1.5 | Kimi-VL-Thinking-A3B |
 |---------------------------------|--------|-------------|----------------|---------------|-------------|-------------|---------|----------|-----------|----------------------|
 | *Thinking Model?*              |        |             |                |               |             |             | ✅       | ✅        | ✅         | ✅                    |
@@ -67,7 +66,6 @@ Full comparison on MMMU, MathVision, and MathVista-mini:
 | MathVista (mini) (Pass@1)       | 63.8   | 56.7        | 74.8           | 68.2          | 62.3        | 56.4        | 71.0    | 71.4     | 74.9      | 71.3                 |
 | MMMU (val) (Pass@1)             | 69.1   | 60.0        | 74.8           | 58.6          | 64.8        | 59.6        | 77.3    | 70.3     | 70.0      | 61.7                 |
 </div>
 ### Inference with 🤗 Hugging Face Transformers
@@ -113,7 +111,7 @@ print(response)
 We have submitted a Merge Request [#16387](https://github.com/vllm-project/vllm/pull/16387) to vLLM. You are welcome to deploy Kimi-VL using the branch corresponding to the vLLM MR until the MR is merged.
-## Citation
 ```
 @misc{kimiteam2025kimivltechnicalreport,
@@ -125,5 +123,4 @@ We have submitted a Merge Request [#16387](https://github.com/vllm-project/vllm/
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2504.07491},
 }
-```

 ---
 base_model:
 - moonshotai/Kimi-VL-A3B-Instruct
+license: mit
 pipeline_tag: image-text-to-text
+library_name: transformers
 ---
 <div align="center">
   <img width="30%" src="figures/logo.png">
 </div>
 ## Introduction
 We present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding, and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B).
 Building on this foundation, we introduce an advanced long-thinking variant: **Kimi-VL-Thinking**. Developed through long chain-of-thought (CoT) supervised fine-tuning (SFT) and reinforcement learning (RL), this model exhibits strong long-horizon reasoning capabilities. It achieves scores of 61.7 on MMMU, 36.8 on MathVision, and 71.3 on MathVista while maintaining the compact 2.8B activated LLM parameter footprint, setting a new standard for efficient yet capable multimodal **thinking** models.
+More information can be found in our technical report: [Kimi-VL Technical Report](https://arxiv.org/abs/2504.07491).
 ## Architecture
 The model adopts an MoE language model, a native-resolution visual encoder (MoonViT), and an MLP projector, as illustrated in the following image.
 <div align="center">
 | Benchmark (Metric)              | GPT-4o | GPT-4o-mini | Qwen2.5-VL-72B | Qwen2.5-VL-7B | Gemma-3-27B | Gemma-3-12B | o1-1217 | QVQ-72B | Kimi-k1.5 | Kimi-VL-Thinking-A3B |
 |---------------------------------|--------|-------------|----------------|---------------|-------------|-------------|---------|----------|-----------|----------------------|
 | *Thinking Model?*              |        |             |                |               |             |             | ✅       | ✅        | ✅         | ✅                    |
 | MathVista (mini) (Pass@1)       | 63.8   | 56.7        | 74.8           | 68.2          | 62.3        | 56.4        | 71.0    | 71.4     | 74.9      | 71.3                 |
 | MMMU (val) (Pass@1)             | 69.1   | 60.0        | 74.8           | 58.6          | 64.8        | 59.6        | 77.3    | 70.3     | 70.0      | 61.7                 |
 </div>
 ### Inference with 🤗 Hugging Face Transformers
 We have submitted a Merge Request [#16387](https://github.com/vllm-project/vllm/pull/16387) to vLLM. You are welcome to deploy Kimi-VL using the branch corresponding to the vLLM MR until the MR is merged.
+## 8. Citation
 ```
 @misc{kimiteam2025kimivltechnicalreport,
       primaryClass={cs.CV},
       url={https://arxiv.org/abs/2504.07491},
 }
+```