Instructions to use moonshotai/Kimi-K2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moonshotai/Kimi-K2.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="moonshotai/Kimi-K2.5", 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-K2.5", trust_remote_code=True, dtype="auto") - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use moonshotai/Kimi-K2.5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "moonshotai/Kimi-K2.5" # 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-K2.5", "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-K2.5
- SGLang
How to use moonshotai/Kimi-K2.5 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-K2.5" \ --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-K2.5", "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-K2.5" \ --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-K2.5", "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-K2.5 with Docker Model Runner:
docker model run hf.co/moonshotai/Kimi-K2.5
is K2.5 the same as K2.5 Thinking?
Official documentation only mentioned K2.5 but the chat is referring to K2.5 Thinking.
https://platform.moonshot.ai/docs/guide/kimi-k2-5-quickstart#breakthrough-in-coding-capabilities
[Excerpt from link above] kimi-k2.5 still has strong reasoning capabilities, supporting multi-step tool invocation and reasoning, excelling at solving complex problems, such as complex logical reasoning, mathematical problems, and code writing.
is K2.5 the same as K2.5 Thinking?
Kimi-K2.5 is a hybrid thinking model that supports both thinking mode and instant(disable thinking) mode. You can refer to https://huggingface.co/moonshotai/Kimi-K2.5#chat-completion to see how to enable/disable thinking.
Hi @Lord-Shibe ,
Based on the README and model documentation:
Kimi K2.5 supports both modes in a single model:
Thinking Mode (default): The model uses
<think>...</think>tags to show its reasoning process before providing the final answer. This is enabled by default.- Temperature:
1.0recommended - Access reasoning via
response.choices[0].message.reasoning_content
- Temperature:
Instant Mode: Direct response without explicit reasoning. Enable by passing:
# Official API extra_body={'thinking': {'type': 'disabled'}} # vLLM/SGLang extra_body={'chat_template_kwargs': {"thinking": False}}- Temperature:
0.6recommended
- Temperature:
So "K2.5" and "K2.5 Thinking" refer to the same model - it's just a matter of which mode you're using. The model was trained with both capabilities built-in.
Hope this helps!