Instructions to use RUC-AIBOX/Qwen-32B-SimpleDeepSearcher with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RUC-AIBOX/Qwen-32B-SimpleDeepSearcher with Transformers:

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

pipe = pipeline("text-generation", model="RUC-AIBOX/Qwen-32B-SimpleDeepSearcher")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RUC-AIBOX/Qwen-32B-SimpleDeepSearcher")
model = AutoModelForCausalLM.from_pretrained("RUC-AIBOX/Qwen-32B-SimpleDeepSearcher")
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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RUC-AIBOX/Qwen-32B-SimpleDeepSearcher with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RUC-AIBOX/Qwen-32B-SimpleDeepSearcher"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RUC-AIBOX/Qwen-32B-SimpleDeepSearcher",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RUC-AIBOX/Qwen-32B-SimpleDeepSearcher

SGLang

How to use RUC-AIBOX/Qwen-32B-SimpleDeepSearcher 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 "RUC-AIBOX/Qwen-32B-SimpleDeepSearcher" \
    --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": "RUC-AIBOX/Qwen-32B-SimpleDeepSearcher",
		"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 "RUC-AIBOX/Qwen-32B-SimpleDeepSearcher" \
        --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": "RUC-AIBOX/Qwen-32B-SimpleDeepSearcher",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RUC-AIBOX/Qwen-32B-SimpleDeepSearcher with Docker Model Runner:
```
docker model run hf.co/RUC-AIBOX/Qwen-32B-SimpleDeepSearcher
```

Improve model card for Qwen-32B-CyberSearcher with paper, code, description, and metadata

by nielsr HF Staff - opened Oct 9, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+61

-7

nielsr

Oct 9, 2025

This PR significantly enhances the model card for Qwen-32B-CyberSearcher by:

Linking it to the paper SimpleDeepSearcher: Deep Information Seeking via Web-Powered Reasoning Trajectory Synthesis.
Adding a direct link to the GitHub repository: https://github.com/RUCAIBox/SimpleDeepSearcher.
Adding the pipeline_tag: text-generation and license: mit to the metadata, ensuring better discoverability and clarity.
Adding relevant tags like deep-search, retrieval-augmented-generation, web-agent, and qwen.
Replacing placeholder content with a comprehensive model description, key contributions, framework overview, and performance highlights from the paper and GitHub README, including illustrative images.
Populating sections like "Intended uses & limitations" and "Training and evaluation data".
Including citation and contact information.
Removing the automatically generated boilerplate comment.

Note: A sample usage code snippet has not been added, as the provided GitHub README does not contain a direct inference example for this model that aligns with the transformers library, and making up code is explicitly disallowed.

Please review and merge this PR if everything looks good.

Improve model card for Qwen-32B-CyberSearcher with paper, code, description, and metadata50521524

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