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, 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 |
| Parameters | 7.62B |
| Context length | 32,768 |
| Recommended interface | Structured Tool-Call |
| Training data | 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
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
- Data: SynSearch-Data
License
This model follows the license included in this repository and the terms of its base model. Checksums are provided in SHA256SUMS.