Image-Text-to-Text
Transformers
Safetensors
kimi_k31
text-generation
compressed-tensors
conversational
custom_code
Eval Results
8-bit precision
Instructions to use moonshotai/Kimi-K3.1-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moonshotai/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="moonshotai/Kimi-K3.1-Pro", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("moonshotai/Kimi-K3.1-Pro", trust_remote_code=True, device_map="auto") - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use moonshotai/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 "moonshotai/Kimi-K3.1-Pro" # 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-K3.1-Pro", "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-K3.1-Pro
- SGLang
How to use moonshotai/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 "moonshotai/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/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "moonshotai/Kimi-K3.1-Pro", "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-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/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "moonshotai/Kimi-K3.1-Pro", "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-K3.1-Pro with Docker Model Runner:
docker model run hf.co/moonshotai/Kimi-K3.1-Pro
Download model-00005-of-000096.safetensors from moonshotai/Kimi-K3.1-Pro: direct link, hf CLI and curl.
- Browser
- Download file 17 GB
-
https://huggingface.co/moonshotai/Kimi-K3.1-Pro/resolve/refs%2Fpr%2F2/model-00005-of-000096.safetensors
- Command line
-
hf download hf://moonshotai/Kimi-K3.1-Pro@refs/pr/2/model-00005-of-000096.safetensors
-
curl -L -o model-00005-of-000096.safetensors https://huggingface.co/moonshotai/Kimi-K3.1-Pro/resolve/refs%2Fpr%2F2/model-00005-of-000096.safetensors
17 GB
- Xet hash:
- 5616ef64a926143d700c06b0a628ebaf1ad87f453bf4b6ca34b8577a0a94244f
- Size of remote file:
- 17 GB
- SHA256:
- 596fc235fe92c9656cbbd63eb719cd46f68b19e22d792224ef307c156f699682
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