Instructions to use XHToken/Spark-X2.6-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XHToken/Spark-X2.6-4B with Transformers:

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

pipe = pipeline("text-generation", model="XHToken/Spark-X2.6-4B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("XHToken/Spark-X2.6-4B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XHToken/Spark-X2.6-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XHToken/Spark-X2.6-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XHToken/Spark-X2.6-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XHToken/Spark-X2.6-4B

SGLang

How to use XHToken/Spark-X2.6-4B 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 "XHToken/Spark-X2.6-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XHToken/Spark-X2.6-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "XHToken/Spark-X2.6-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XHToken/Spark-X2.6-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XHToken/Spark-X2.6-4B with Docker Model Runner:
```
docker model run hf.co/XHToken/Spark-X2.6-4B
```

Spark-X2.6-4B in two live web assistants: share what you are building

#29

by FenjuFu - opened about 11 hours ago

Discussion

FenjuFu

SparkLLM org about 11 hours ago

Spark-X2.6-4B is running in two live web assistants. These short recordings show English questions, actual model replies, and the thinking-level controls available to visitors.

Try the apps, then share a demo of your own Spark-X2.6 application in the comments. Longer case write-ups are welcome too—post them on Hugging Face and drop the link here.

Astron Harness Tutorial: Learning Assistant

Open the learning assistant · Source repository

This bilingual tutorial assistant helps visitors explore AStudio, Loomy and AstronClaw. It uses relevant tutorial material as context for the conversation.

Question: “Plan mode vs Goal mode in AStudio? Two short sentences, no Markdown.”

The recording shows the assistant explaining when to review a plan before execution and when to use Goal mode for continued progress. The interface offers None, Low and High thinking levels and remembers the visitor's selection.

Full-length recording

Awesome Astron Workflow: AI Chat

Open AI Chat

This assistant answers questions about Astron projects and capabilities using the site's reference material.

Question: “How can SkillHub help a team reuse AI skills? Give 3 benefits in under 60 words.”

The recorded reply discusses skill distribution, secure sharing and discoverability. Both the question and the answer remain as they appeared in the application.

Full-length recording

Integration details

Both applications call spark-x2.6-4b through iFLYTEK Astron MaaS, with credentials kept on the server. The browser sends the conversation and selected thinking level to the application's backend. These demos use Low.

The server maps the controls to the model request:

Selection	Request behavior
None	Disable extra thinking
Low	Enable thinking with low reasoning effort
High	Enable thinking with high reasoning effort

The tutorial assistant supports streaming replies and stopping an answer. The clips show the website Q&A experience: typing a question, waiting for the model and reading the response.

About the recordings

These are real website recordings, and the responses have not been rewritten or simulated. The AI Chat GIF shortens pure waiting time and labels that edit. The full-length MP4 links retain the complete captures. These clips illustrate application integration; they are not latency benchmarks.

Share your Spark-X2.6 case

What are you building with Spark-X2.6? Share a working demo or screenshot in the comments. For a longer Hugging Face write-up, useful details include your use case, model variant, deployment stack, prompt, thinking level, integration approach and what you learned.

Spark-X2.6 model collection

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Tap or paste here to upload images

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