Instructions to use ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ with Transformers:

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

pipe = pipeline("text-generation", model="ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ

SGLang

How to use ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ 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 "ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ" \
    --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": "ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ",
		"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 "ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ" \
        --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": "ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ with Docker Model Runner:
```
docker model run hf.co/ISTA-DASLab/Kimi-K3-P48NVFP4-MoESQ
```

Kimi K3 GSQ-RCO-GGUF IQ1_S, IQ1_M, IQ2_XXS

by ReingeFallen - opened 5 days ago

Discussion

ReingeFallen

5 days ago

A Kimi K3 GSQ-RCO-GGUF IQ1_S would probably be useful since even the IQ1_S unsloth version is quite good with short contexts. Im guessing an GSQ-RCO optimized version could handle longer contexts without becoming incoherent.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment