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 running in the browser on WebGPU (ONNX, official INT4 weights)

#26

by mohdmahmodi - opened 7 days ago

Discussion

mohdmahmodi

7 days ago

Hi XHToken team, thanks for Spark-X2.6!

I converted Spark-X2.6-4B to ONNX so it runs in the browser on WebGPU, with no server and nothing to install.

Demo: https://huggingface.co/spaces/mohdmahmodi/Spark-X2.6-WebGPU
Model files: https://huggingface.co/mohdmahmodi/Spark-X2.6-4B-ONNX-WebGPU
Source and validation: https://github.com/mohdmahmodi/Spark-X2.6-4B-onnx-webgpu

The 4-bit weights are your GPTQ codes from Spark-X2.6-4B-INT4, repacked for onnxruntime-web without changing them. The sliding-window and full-attention layers, partial rotary, output gate, thinking modes and tool calling all work, using the official chat template.

I compared it with the original model in transformers on the same prompts (greedy, thinking off):

	bf16	official INT4	this build
GSM8K, 1,319 problems	90.8%	91.2%	90.5%
BFCL v3 simple, 200 cases	89.0%	89.5%	89.5%
Needle in a haystack, 1k to 16k tokens	12/12	12/12	12/12

The GSM8K gap to INT4 is within noise (McNemar p = 0.18).

On an RTX 3090 Ti in Chrome it does about 92 tok/s with a short prompt and 69 tok/s at 16k tokens of context. The download is 2.77 GB and the browser caches it.

It also works with Transformers.js 4.3:

import { pipeline } from "@huggingface/transformers";

const generator = await pipeline("text-generation", "mohdmahmodi/Spark-X2.6-4B-ONNX-WebGPU", { device: "webgpu", dtype: "q4f16" });
const out = await generator([{ role: "user", content: "Hello!" }], {
  max_new_tokens: 256, tokenizer_encode_kwargs: { enable_thinking: false },
});
console.log(out[0].generated_text.at(-1).content);

I've only tested it on Windows with an NVIDIA GPU so far. If anyone tries it on a Mac, an AMD or Intel GPU, or a phone, let me know how it goes. And if you think it's useful, I'd be glad to see it linked from the model card next to the GGUF.

linchao-2026

SparkLLM org 7 days ago

Thanks for the contribution and detailed validation! We’ve added a WebGPU section to our README linking to your demo, model files, and source code.

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