Instructions to use RadixArk/Kimi-K3-DSpark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RadixArk/Kimi-K3-DSpark with Transformers:

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

pipe = pipeline("text-generation", model="RadixArk/Kimi-K3-DSpark", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("RadixArk/Kimi-K3-DSpark", trust_remote_code=True)
model = AutoModel.from_pretrained("RadixArk/Kimi-K3-DSpark", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RadixArk/Kimi-K3-DSpark with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/RadixArk/Kimi-K3-DSpark

SGLang

How to use RadixArk/Kimi-K3-DSpark 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 "RadixArk/Kimi-K3-DSpark" \
    --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": "RadixArk/Kimi-K3-DSpark",
		"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 "RadixArk/Kimi-K3-DSpark" \
        --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": "RadixArk/Kimi-K3-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RadixArk/Kimi-K3-DSpark with Docker Model Runner:
```
docker model run hf.co/RadixArk/Kimi-K3-DSpark
```

Training compute

by kshou-kai - opened Aug 10

Discussion

kshou-kai

Aug 10

Could you share the approximate GPU-hours required for (1) regenerating the training data with Kimi-K3 and (2) training the DSpark draft model? Even rough numbers would be very helpful.

skx618

RadixArk org Aug 10

Consider 16 GB300s. The cost of regeneration depends on the source dataset and generation configuration. In general, generating sufficient training data is expected to take approximately one week. Training is anticipated to take 1–3 days, depending on the readiness and volume of the regenerated data.

Strike12138

18 days ago

Consider 16 GB300s. The cost of regeneration depends on the source dataset and generation configuration. In general, generating sufficient training data is expected to take approximately one week. Training is anticipated to take 1–3 days, depending on the readiness and volume of the regenerated data.

hi, i am currently working on SFT mtp layer for deepseek. I wonder if you set temperature = 0 when regenerating the training data or not?
looking forward to your reply~

skx618

RadixArk org 18 days ago

I'd suggest setting temperature = 1, or whatever values are recommended for deployment, for a better train–inference match.

Strike12138

17 days ago

I'd suggest setting temperature = 1, or whatever values are recommended for deployment, for a better train–inference match.

got it, thx a lot

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