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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("moonshotai/Kimi-K2.5", dtype="auto") - 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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "moonshotai/Kimi-K2.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "moonshotai/Kimi-K2.5", "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 "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/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "moonshotai/Kimi-K2.5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use moonshotai/Kimi-K2.5 with Docker Model Runner:
docker model run hf.co/moonshotai/Kimi-K2.5
Add MathArena evaluation result for hmmt/hmmt_feb_2026
#95
by JasperDekoninck - opened
This PR adds a new MathArena evaluation result so it can be indexed on the model leaderboard page.
Model: moonshotai/Kimi-K2.5
Competition dataset id: MathArena/hmmt_feb_2026
Score: 87.12
Result file: .eval_results/MathArena--hmmt_feb_2026.yaml
The results are the same as the ones displayed on our webpage.
Note: this is an experimental feature, we are currently trying to make this work as smooth as possible.