Instructions to use anuppillai/Kimi-K3-REAP-720-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anuppillai/Kimi-K3-REAP-720-MXFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="anuppillai/Kimi-K3-REAP-720-MXFP4", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("anuppillai/Kimi-K3-REAP-720-MXFP4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anuppillai/Kimi-K3-REAP-720-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anuppillai/Kimi-K3-REAP-720-MXFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anuppillai/Kimi-K3-REAP-720-MXFP4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anuppillai/Kimi-K3-REAP-720-MXFP4

SGLang

How to use anuppillai/Kimi-K3-REAP-720-MXFP4 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 "anuppillai/Kimi-K3-REAP-720-MXFP4" \
    --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": "anuppillai/Kimi-K3-REAP-720-MXFP4",
		"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 "anuppillai/Kimi-K3-REAP-720-MXFP4" \
        --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": "anuppillai/Kimi-K3-REAP-720-MXFP4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anuppillai/Kimi-K3-REAP-720-MXFP4 with Docker Model Runner:
```
docker model run hf.co/anuppillai/Kimi-K3-REAP-720-MXFP4
```

Kimi-K3-REAP-720-MXFP4 / assets /aquarium-generation-comparison.png

anuppillai

Document paired generation spot checks

e62897f verified 26 days ago

Download

History Blame Contribute Delete

159 kB

Xet Pointer Details

( Raw pointer file )

Xet hash:: 87d15a96681c9540d6d04fd389a223c1527f143ca168d26ed78581666423f5af
Size of remote file:: 159 kB
SHA256:: 0fbbdd5d2506c3d17909c35ce532a2c8ade301513b467cee19c63338103e6309

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.