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
```

关于Tau3分数的疑问

by yche - opened 3 days ago

Discussion

yche

3 days ago

您好，关于您在Benchmark-Result里放的Tau3的分数，有一些疑问。Tau3的分数按照官方的计算方法似乎是四个榜的分数取平均，但是文档里报的分数不太像是四个子榜分数的算术平均？我们这里评的是Tau3-Banking吗

pengwenzhi

SparkLLM org 3 days ago

你好，感谢你对我们评测结果的关注。首先澄清一下：我们报告的 𝜏³ 分数特指 𝜏³-Banking 的评测结果，并非 4 个子任务分数的平均值。

我们采用了 Artificial Analysis 公布的 𝜏³-Banking 评测方案(https://artificialanalysis.ai/methodology/intelligence-benchmarking#tau3-banking)，具体设置如下：

使用上游 tau2-bench v1.0.1 的数据集和评分器，评估完整的 97 个 Banking 任务。
采用 bm25_grep 检索模式。
使用 GPT-5.4 Mini（中等推理强度）作为用户模拟器和自然语言断言判定器。
每个任务重复运行 5 次，每次最多 200 步。“步”按照评测框架中的消息计数，包含用户模拟器的消息，而不只是被评测模型的轮次。
根据后端数据库的实际状态及相关断言判定任务是否成功，最终报告全部 97 个任务、每个任务运行 5 次的平均 pass@1 分数。

hgeist

3 days ago

I really appreciate that you can answer this in detail @pengwenzhi . Thank you

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment