--- 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 - react - deep-search - multi-hop-qa - qwen --- # SearchQwen2.5-7B **SearchQwen2.5-7B** is a compact Search Agent model from the Alibaba Cloud PAI team. It is post-trained on environment-aligned multi-hop search tasks and solver-verified ReAct trajectories generated with the EasyDistill2 pipeline. The model is optimized for iterative **search → browse → evidence integration → answer** behavior. It supports both structured Tool-Call interaction and Search-R1-style text interaction (Qwen2.5 variants). > 中文简介：SearchQwen2.5-7B 是面向多跳问答与深度搜索任务训练的 Search Agent 小模型，重点增强结构化工具调用、网页浏览和跨来源信息整合能力。 ## Model details | Item | Value | |---|---| | Base model | [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) | | Parameters | 7.62B | | Architecture | Qwen2, 28 layers | | Context length | 32,768 tokens in the released config | | Primary interface | Structured `search` / `browse` Tool-Call | | Training | Environment-aligned trajectories, Solver-in-the-Loop verification, and process-aware post-training | | Companion dataset | [alibaba-pai/SynSearch-Data](https://huggingface.co/datasets/alibaba-pai/SynSearch-Data) | ## Evaluation We report LLM-judge accuracy (%) over two benchmark groups: - **Multi-hop QA:** 2WikiMultiHopQA, Bamboogle, HotpotQA, MuSiQue. - **Deep Search:** GAIA, WebWalkerQA, xbench-deepsearch, BrowseComp-ZH. ### Search-R1-style text interaction | Model | Multi-hop QA Avg. | Deep Search Avg. | Overall Avg. | |---|---:|---:|---:| | Qwen2.5-7B-Instruct | 45.28 | 18.65 | 31.96 | | **SearchQwen2.5-7B** | **52.95** | **26.23** | **39.59** | ### Structured Tool-Call interaction | Model | Multi-hop QA Avg. | Deep Search Avg. | Overall Avg. | |---|---:|---:|---:| | Qwen2.5-7B-Instruct | 45.23 | 24.35 | 33.33 | | **SearchQwen2.5-7B** | **55.90** | **33.33** | **44.61** | Deep Search gains are consistently larger than the gains on conventional multi-hop QA, indicating improved search, browsing, and evidence-integration behavior rather than answer memorization alone. ## Requirements ```bash pip install -U transformers accelerate torch ``` ## Transformers 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() tools = [ { "type": "function", "function": { "name": "search", "description": "Search the web for relevant pages.", "parameters": { "type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"], }, }, }, { "type": "function", "function": { "name": "browse", "description": "Read the content of a web page.", "parameters": { "type": "object", "properties": {"url": {"type": "string"}}, "required": ["url"], }, }, }, ] 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?"}, ] inputs = tokenizer.apply_chat_template( messages, tools=tools, add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True, ).to(model.device) with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=256, do_sample=False) new_tokens = output[0, inputs["input_ids"].shape[-1]:] print(tokenizer.decode(new_tokens, skip_special_tokens=False)) ``` A typical first response is a structured call: ```xml {"name": "search", "arguments": {"query": "..."}} ``` Execute the requested tool, append the tool response to the conversation, and call the model again until it returns a final answer. ## vLLM deployment ```bash vllm serve alibaba-pai/SearchQwen2.5-7B \ --served-model-name SearchQwen2.5-7B \ --enable-auto-tool-choice \ --tool-call-parser hermes \ --max-model-len 8192 ``` For `SearchQwen2.5-7B`, the release was validated with Transformers and an OpenAI-compatible vLLM endpoint on NVIDIA L20 GPUs. The test covered checkpoint loading, structured `search` emission, tool-result consumption, and final-answer generation. ## Training data The released companion dataset contains 5,000 audited multi-hop ReAct trajectories covering 4,182 unique tasks. Each example includes a complete `messages` history and metadata for seed provenance, hop count, difficulty, rollout statistics, and answer-equivalence auditing. ## Intended use - Research on search agents and retrieval-augmented reasoning. - Search/browse tool-use experiments. - Fine-tuning, evaluation, and deployment of compact agent models. ## Limitations - The model requires an external search/browse runtime; it does not provide a search index itself. - Final-answer quality depends on tool availability and retrieved evidence quality. - Tool schemas and prompt templates should remain compatible with the released chat template. - LLM-judge scores may vary with evaluator model, decoding settings, and search backend. - Do not use the model as the sole authority for safety-critical, legal, medical, or financial decisions. ## Artifact integrity `SHA256SUMS` contains checksums for all weight shards, the weight index, configuration, and tokenizer. ## Related releases - [SearchQwen2.5-7B](https://huggingface.co/alibaba-pai/SearchQwen2.5-7B) - [SearchQwen2.5-3B](https://huggingface.co/alibaba-pai/SearchQwen2.5-3B) - [SearchQwen3-8B](https://huggingface.co/alibaba-pai/SearchQwen3-8B) - [SynSearch-Data](https://huggingface.co/datasets/alibaba-pai/SynSearch-Data) ## Citation ```bibtex @misc{searchqwen2026, title = {Fake It Till You Make It: Training Deep Search Agents on Synthetic Reality}, author = {Alibaba Cloud PAI Team}, year = {2026}, note = {SearchQwen model family and SynSearch-Data} } ``` ## License This derivative model is released under the license file included in this repository and remains subject to the terms of its base model.