Instructions to use AMAImedia/Kimi-K3.1-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AMAImedia/Kimi-K3.1-Pro with Transformers:

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

pipe = pipeline("image-text-to-text", model="AMAImedia/Kimi-K3.1-Pro", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained("AMAImedia/Kimi-K3.1-Pro", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AMAImedia/Kimi-K3.1-Pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AMAImedia/Kimi-K3.1-Pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Kimi-K3.1-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AMAImedia/Kimi-K3.1-Pro

SGLang

How to use AMAImedia/Kimi-K3.1-Pro 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 "AMAImedia/Kimi-K3.1-Pro" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Kimi-K3.1-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AMAImedia/Kimi-K3.1-Pro" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AMAImedia/Kimi-K3.1-Pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AMAImedia/Kimi-K3.1-Pro with Docker Model Runner:
```
docker model run hf.co/AMAImedia/Kimi-K3.1-Pro
```

AMAImedia commited on about 4 hours ago

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@@ -840,7 +840,9 @@ def chat_with_preserved_thinking(client: openai.OpenAI, model_name: str):
     print(f"response: {response.choices[0].message.reasoning}")
     return response.choices[0].message.content
 ```
-For full guides and examples (vision input, structured output, partial mode, tool choice, dynamic tool loading, context caching), see the [Kimi K3.1 Pro Quickstart](https://platform.kimi.ai/docs/guide/kimi-k3-1-pro-quickstart) and [Thinking Effort](https://platform.kimi.ai/docs/guide/use-thinking-effort).
 ## 6. License

     print(f"response: {response.choices[0].message.reasoning}")
     return response.choices[0].message.content
 ```
+For full guides and examples (vision input, structured output, partial mode,
+tool choice, dynamic tool loading, context caching),
+see the [Kimi K3.1 Pro Quickstart](https://platform.kimi.ai/docs/guide/kimi-k3-1-pro-quickstart) and [Thinking Effort](https://platform.kimi.ai/docs/guide/use-thinking-effort).
 ## 6. License