Instructions to use RadixArk/Qwen3.8-27B-DSpark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RadixArk/Qwen3.8-27B-DSpark with Transformers:

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

pipe = pipeline("text-generation", model="RadixArk/Qwen3.8-27B-DSpark", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("RadixArk/Qwen3.8-27B-DSpark", trust_remote_code=True)
model = AutoModel.from_pretrained("RadixArk/Qwen3.8-27B-DSpark", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RadixArk/Qwen3.8-27B-DSpark with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RadixArk/Qwen3.8-27B-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/Qwen3.8-27B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RadixArk/Qwen3.8-27B-DSpark

SGLang

How to use RadixArk/Qwen3.8-27B-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/Qwen3.8-27B-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/Qwen3.8-27B-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/Qwen3.8-27B-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/Qwen3.8-27B-DSpark",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RadixArk/Qwen3.8-27B-DSpark with Docker Model Runner:
```
docker model run hf.co/RadixArk/Qwen3.8-27B-DSpark
```

Fix DSpark reference generation block width

by yisun0618 - opened 2 days ago

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+11

-5

yisun0618

2 days ago

Fixes discussion #1.

The reference spec_generate now consumes all block_size draft rows, verifies anchor + block_size proposals, and reserves the complete target-bonus slot in the scratch buffer.

H100 contract probe for block_size=7:

before: 7 noise rows -> 6 draft logits -> 7-token target verify
after: 7 noise rows -> 7 draft logits -> 8-token target verify

A live SGLang load of this checkpoint independently resolved gamma=7 and verify_num_draft_tokens=8.

Fix DSpark reference generation block widthbb605c21

skx618 changed pull request status to merged 2 days ago

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