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
Kimi K2.5 slow?
Hi,
When I look at the benchmarks Intelligence VS Speed, for some reason Kimi K2.5 comes out as one of the slowest.
https://artificialanalysis.ai/#intelligence-vs-output-speed
Is it possible that there needs soms finetuning happening?
Hopefully a 2.6 version can speed it up (that would reduce the overall cost of running it).
I would also appreciate if there was a Kimi K2.5 flash variant comming out, that can run on consumer grade hardware with 128gb unified memory devices (MAC or AMD 395+). GPT-oss-120b is after 9 months still the leader for devices with 128gb ram
Have you tried with thinking disabled using the extra_body argument?
completion = client.chat.completions.create(
model="kimi-k2.5",
messages=[
{"role": "system", "content": system_prompt},
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": image_url,
},
},
{
"type": "text",
"text": "Locate every player and output bounding boxes in JSON format.",
},
],
},
],
extra_body={
"thinking": {"type": "disabled"}},
max_tokens=1000
)
Hi Ronnie,
Thank you for the suggestion, but it was rather a question, because it currently is benchmarked as one of the slowest models.
Such a result doesn't reflect well on the product, so I was wondering what could have caused those bad performance numbers.
It's a great (actually the best) model out there, but results on a trustfull website like this can have a negative effect on it's adoptability (which I'd prefer it would not have).
As for that example = 1000 max tokens thats an abnormally low value, that's about 650 words?