Instructions to use XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math with Transformers:

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

pipe = pipeline("text-generation", model="XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math")
model = AutoModelForCausalLM.from_pretrained("XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math

SGLang

How to use XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math 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 "XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math" \
    --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": "XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math",
		"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 "XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math" \
        --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": "XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math with Docker Model Runner:
```
docker model run hf.co/XingYing-stack/TIPS-Qwen3-4B-Thinking-2507-Math
```

Add paper link

by nielsr HF Staff - opened 17 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-2

nielsr

17 days ago

This PR adds a direct link to the paper page in the model card content, making it easier for users to find the original publication. No changes to the metadata are needed, as it is already complete and correct.

Add paper link98c272c4

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

Tap or paste here to upload images

Ready to merge

This branch is ready to get merged automatically.

· Sign up or log in to comment