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alibaba-pai
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Model Details
Model Provider
alibaba-pai
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
| Item | Value |
|---|---|
| Base model | Qwen/Qwen3-8B |
| Parameters | 8.19B |
| Context length | 40,960 |
| Recommended interface | Structured Tool-Call |
| Training data | SynSearch-Data |
LLM-judge accuracy (%). Multi-hop QA averages 2WikiMultiHopQA, Bamboogle, HotpotQA, and MuSiQue; Deep Search averages GAIA, WebWalkerQA, xbench-deepsearch, and BrowseComp-ZH.
| Interaction | Model | Deep Search | Overall |
|---|---|---|---|
| Tool-Call | Qwen3-8B | 24.50 | 40.31 |
| Tool-Call | SearchQwen3-8B | 35.42 | 50.31 |
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "alibaba-pai/SearchQwen3-8B"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, enable_thinking=False,).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.
This model follows the license included in this repository and the terms of its base model. Checksums are provided in SHA256SUMS.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "alibaba-pai/SearchQwen3-8B"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, enable_thinking=False,).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))