Instructions to use alibaba-pai/SearchQwen2.5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alibaba-pai/SearchQwen2.5-7B with Transformers:

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

pipe = pipeline("text-generation", model="alibaba-pai/SearchQwen2.5-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/SearchQwen2.5-7B")
model = AutoModelForCausalLM.from_pretrained("alibaba-pai/SearchQwen2.5-7B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alibaba-pai/SearchQwen2.5-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alibaba-pai/SearchQwen2.5-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alibaba-pai/SearchQwen2.5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alibaba-pai/SearchQwen2.5-7B

SGLang

How to use alibaba-pai/SearchQwen2.5-7B 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 "alibaba-pai/SearchQwen2.5-7B" \
    --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": "alibaba-pai/SearchQwen2.5-7B",
		"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 "alibaba-pai/SearchQwen2.5-7B" \
        --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": "alibaba-pai/SearchQwen2.5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alibaba-pai/SearchQwen2.5-7B with Docker Model Runner:
```
docker model run hf.co/alibaba-pai/SearchQwen2.5-7B
```

SearchQwen2.5-7B / README.md

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	---
	library_name: transformers
	pipeline_tag: text-generation
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
	language:
	- en
	base_model: Qwen/Qwen2.5-7B-Instruct
	base_model_relation: finetune
	tags:
	- search-agent
	- tool-use
	- function-calling
	- deep-search
	- qwen
	---

	# SearchQwen2.5-7B

	SearchQwen2.5-7B is a compact Search Agent model from the Alibaba Cloud PAI team. It is trained with environment-aligned, solver-verified search trajectories generated by [EasyDistill 2.0](https://github.com/modelscope/easydistill/tree/v2.0.0), and is designed for multi-hop search, browsing, and evidence integration.

	> SearchQwen2.5-7B 是基于 EasyDistill 2.0 环境对齐轨迹蒸馏链路训练的 Search Agent 小模型，支持结构化 `search` / `browse` 工具调用。

	## Model overview

	\| Item \| Value \|
	\|---\|---\|
	\| Base model \| [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) \|
	\| Parameters \| 7.62B \|
	\| Context length \| 32,768 \|
	\| Recommended interface \| Structured Tool-Call \|
	\| Training data \| [SynSearch-Data](https://huggingface.co/datasets/alibaba-pai/SynSearch-Data) \|

	## Results

	LLM-judge accuracy (%). Multi-hop QA averages 2WikiMultiHopQA, Bamboogle, HotpotQA, and MuSiQue; Deep Search averages GAIA, WebWalkerQA, xbench-deepsearch, and BrowseComp-ZH.

	\| Interaction \| Model \| Multi-hop QA \| Deep Search \| Overall \|
	\|---\|---\|---:\|---:\|---:\|
	\| Search-R1 style \| Qwen2.5-7B-Instruct \| 45.28 \| 18.65 \| 31.96 \|
	\| Search-R1 style \| SearchQwen2.5-7B \| 52.95 \| 26.23 \| 39.59 \|
	\| Tool-Call \| Qwen2.5-7B-Instruct \| 45.23 \| 24.35 \| 33.33 \|
	\| Tool-Call \| SearchQwen2.5-7B \| 55.90 \| 33.33 \| 44.61 \|

	## Quickstart

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "alibaba-pai/SearchQwen2.5-7B"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	).eval()

	messages = [
	{"role": "system", "content": "You are a search agent. Use tools before answering."},
	{"role": "user", "content": "Which city is the birthplace of the author of The Old Man and the Sea?"},
	]
	tools = [
	{
	"type": "function",
	"function": {
	"name": "search",
	"description": "Search the web.",
	"parameters": {
	"type": "object",
	"properties": {"query": {"type": "string"}},
	"required": ["query"],
	},
	},
	}
	]

	inputs = tokenizer.apply_chat_template(
	messages,
	tools=tools,
	add_generation_prompt=True,
	tokenize=True,
	return_tensors="pt",
	return_dict=True,
	).to(model.device)

	output = model.generate(**inputs, max_new_tokens=256, do_sample=False)
	print(tokenizer.decode(output[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=False))
	```

	The model returns a structured `<tool_call>`; execute the tool, append its response, and continue until a final answer is produced. An external search/browse backend is required.

	## Related resources

	- Framework: [EasyDistill 2.0](https://github.com/modelscope/easydistill/tree/v2.0.0)
	- Data: [SynSearch-Data](https://huggingface.co/datasets/alibaba-pai/SynSearch-Data)

	## License

	This model follows the license included in this repository and the terms of its base model. Checksums are provided in `SHA256SUMS`.