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
```

DSpark accept rate is under 0.5, what should I do next?

by zzyong - opened 8 days ago

Discussion

zzyong

8 days ago

I use this dspark draft model, accept rate is only 0.45 ~ 0.5, accept len is 2 ( I use --speculative-dspark-block-size 2).
I had try other dspark draft model on hugging face, it's accept rate is worse then this one.
why? what should I do if I want to raise the accept rate.

cmRDXA

8 days ago

Have you tried the --speculative-dspark-block-size 7?
What is the acc len on this block size?

zzyong

2 days ago

Have you tried the --speculative-dspark-block-size 7?
What is the acc len on this block size?

I try --speculative-dspark-block-size 7, the accept len is 2.5, a little improved, but verify cost higher, so I use block-size 2.

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