Instructions to use QuantTrio/Kimi-K3-Cubic-2.5Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantTrio/Kimi-K3-Cubic-2.5Bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="QuantTrio/Kimi-K3-Cubic-2.5Bit", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantTrio/Kimi-K3-Cubic-2.5Bit", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use QuantTrio/Kimi-K3-Cubic-2.5Bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantTrio/Kimi-K3-Cubic-2.5Bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantTrio/Kimi-K3-Cubic-2.5Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantTrio/Kimi-K3-Cubic-2.5Bit

SGLang

How to use QuantTrio/Kimi-K3-Cubic-2.5Bit 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 "QuantTrio/Kimi-K3-Cubic-2.5Bit" \
    --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": "QuantTrio/Kimi-K3-Cubic-2.5Bit",
		"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 "QuantTrio/Kimi-K3-Cubic-2.5Bit" \
        --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": "QuantTrio/Kimi-K3-Cubic-2.5Bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use QuantTrio/Kimi-K3-Cubic-2.5Bit with Docker Model Runner:
```
docker model run hf.co/QuantTrio/Kimi-K3-Cubic-2.5Bit
```

JunHowie commited on 28 days ago

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@@ -51,6 +51,8 @@ designed to challenge conventional integer and floating-point formats at
 ultra-low precision while relaxing the usual need for small group sizes; most
 quantized layers in this model use group size 512.
 - **57 output tok/s** — single-request short-context decode
 - **800–900+ output tok/s** — 64-way concurrency, without dSpark or other
   speculative decoding
@@ -991,4 +993,4 @@ Both the code repository and the model weights are released under the [Kimi K3 L
 ## 8. Contact Us
-If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).

 ultra-low precision while relaxing the usual need for small group sizes; most
 quantized layers in this model use group size 512.
+Technical report: [arXiv:2608.06763](https://arxiv.org/abs/2608.06763).
 - **57 output tok/s** — single-request short-context decode
 - **800–900+ output tok/s** — 64-way concurrency, without dSpark or other
   speculative decoding
 ## 8. Contact Us
+If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).