---
title: SearchQwen3-8B
canonical_url: "https://www.modelscope.ai/models/PAI/SearchQwen3-8B"
md_url: "https://www.modelscope.ai/models/PAI/SearchQwen3-8B.md"
repository: PAI/SearchQwen3-8B
last_updated: 2026-08-24
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - qwen3
architectures:
  - Qwen3ForCausalLM
base_model:
  - Qwen/Qwen3-8B
base_model_relation: finetune
parameters: 8.2B
tensor_type:
  - BF16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
language:
  - en
downloads: 83
stars: 0
tags:
  - search-agent
  - tool-use
  - function-calling
  - deep-search
  - qwen
---

# SearchQwen3-8B

> SearchQwen3-8B - An open-source model by PAI on ModelScope. SearchQwen3-8B 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…

PAI/SearchQwen3-8B is a 8.2B-parameter text-generation model on ModelScope. licensed under apache-2.0. derived from Qwen/Qwen3-8B.

- **Repository**: PAI/SearchQwen3-8B
- **License**: apache-2.0
- **Tasks**: text-generation
- **Parameters**: 8.2B
- **Base model**: Qwen/Qwen3-8B
- **Tags**: search-agent, tool-use, function-calling, deep-search, qwen
- **Downloads**: 83
- **Stars**: 0
- **Last updated**: 2026-08-24

Source: https://www.modelscope.ai/models/PAI/SearchQwen3-8B

---

# SearchQwen3-8B

**SearchQwen3-8B** 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.

> SearchQwen3-8B 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/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) |
| Parameters | 8.19B |
| Context length | 40,960 |
| 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 | Deep Search | Overall |
|---|---|---:|---:|
| Tool-Call | Qwen3-8B | 24.50 | 40.31 |
| Tool-Call | **SearchQwen3-8B** | **35.42** | **50.31** |

## Quickstart

```python
import torch
from 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.

## 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`.