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Model: Seungyoun/qwen2.5-3b-it_searchR1-like-multiturn
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---
datasets:
- PeterJinGo/nq_hotpotqa_train
language:
- en
base_model:
- Qwen/Qwen2.5-3B-Instruct
---
# Qwen2.5‑3B Search‑R1‑Multiturn (reproduce)
> **Author · Seungyoun Shin**
>
> 🤗 Model Hub: [hf](https://huggingface.co/Seungyoun/qwen2.5-3b-it_searchR1-like-multiturn)
>
> 📈 W\&B Report: [wandb](https://wandb.ai/yoon1001/search_r1_like_async_rl/reports/Qwen2-5-3b-it-search-r1-reproduce--VmlldzoxMzA2NzA2NA)
A faithful re‑implementation of the *Search‑R1* on **Qwen 2.5‑3B-instruct**, trained purely on `nq-hotpotqa-train` with GRPO via the open‑source [VERL](https://github.com/volcengine/verl) framework.
---
## 🚀 Quick start
```bash
pip install "transformers>=4.41" torch duckduckgo_search>=6.3.5 accelerate
```
### Full inference script
Below is the exact script used in our experiments—drop it next to the model weights and run.
```python
#!/usr/bin/env python3
"""
Minimal **multi‑turn tool‑calling** demo for the Qwen2.5‑3B Search‑R1 model
Highlights
-----------
* Presents the `search` function schema via `tools=[…]` so the model emits JSON calls.
* Detects `<tool_call>` → parses `{name:"search", arguments:{query_list:[…]}}` and runs DuckDuckGo.
* Streams results back in `<tool_response>` until an `<answer>` block appears.
"""
from __future__ import annotations
import json, re, sys
from typing import List
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from duckduckgo_search import DDGS
DEFAULT_SYSTEM_CONTENT = "You are a helpful and harmless assistant."
DEFAULT_USER_CONTENT_PREFIX = (
"Answer the given question. You must conduct reasoning inside <think> and "
"</think> first every time you get new information. After reasoning, if you "
"find you lack some knowledge, you can call a search engine by <tool_call> "
"query </tool_call> and it will return the top searched results between "
"<tool_response> and </tool_response>. You can search as many times as your "
"want. If you find no further external knowledge needed, you can directly "
"provide the answer inside <answer> and </answer>, without detailed "
"illustrations. For example, <answer> Beijing </answer>. Question: "
)
MODEL_NAME = "Seungyoun/qwen2.5-3b-it_searchR1-like-multiturn"
MAX_TURNS, MAX_RESPONSE_TOKENS = 4, 512
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
SEARCH_SCHEMA = {
"type": "function",
"function": {
"name": "search",
"description": "DuckDuckGo web search",
"parameters": {
"type": "object",
"properties": {
"query_list": {
"type": "array",
"items": {"type": "string"},
"description": "Fully‑formed semantic queries."
}
},
"required": ["query_list"],
},
},
}
def create_prompt(q: str) -> List[dict]:
return [
{"role": "system", "content": DEFAULT_SYSTEM_CONTENT},
{"role": "user", "content": DEFAULT_USER_CONTENT_PREFIX + q},
]
def ddg_search(query: str, k: int = 5) -> str:
with DDGS() as ddgs:
hits = list(ddgs.text(query, safesearch="moderate", max_results=k))
return "\n".join(f"{i+1}. {h['title']} – {h['body']} ({h['href']})" for i,h in enumerate(hits))
def extract_queries(raw: str) -> List[str]:
try:
payload = json.loads(raw)
if payload.get("name") == "search":
return payload.get("arguments", {}).get("query_list", [])
except json.JSONDecodeError:
pass
return [raw]
def main() -> None:
q = sys.argv[1] if len(sys.argv) > 1 else "How is the weather in Seoul?"
tok = AutoTokenizer.from_pretrained(MODEL_NAME, padding_side="left")
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16, device_map="auto")
msgs = create_prompt(q)
history = tok.apply_chat_template(msgs, tools=[SEARCH_SCHEMA], add_generation_prompt=True, tokenize=False)
pattern = re.compile(r"<tool_call>\s*(.*?)\s*</tool_call>", re.S)
for turn in range(MAX_TURNS):
enc = tok(history, return_tensors="pt").to(DEVICE)
out = model.generate(**enc, max_new_tokens=MAX_RESPONSE_TOKENS, temperature=0.7, do_sample=True)
new = tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True)
print(f"\n===== Assistant (turn {turn+1}) =====\n{new}\n")
history += new
m = pattern.search(new)
if not m: break
results = "\n---\n".join(ddg_search(q,5) for q in extract_queries(m.group(1)))
history += f"<tool_response>\n{results}\n</tool_response>"
if __name__ == "__main__":
main()
```
---
## 🧠 Reasoning style
```
<think> … chain‑of‑thought … </think>
<tool_call>{"name":"search", "arguments":{"query_list":["…"]}}</tool_call>
<tool_response>
1. web result
…
</tool_response>
<answer> final concise answer </answer>
```
---
## 📊 Evaluation (Pass@1)
| Dataset | Original Search‑R1 (Qwen2.5‑3B) | **This work** |
| --------- | ------------------------------- | ------------- |
| NQ | 0.397 | **0.406** |
| TriviaQA | 0.565 | **0.582** |
| PopQA | 0.391 | **0.420** |
| HotpotQA | 0.331 | **0.338** |
| 2Wiki | 0.310 | **0.332** |
| Musique | **0.124** | 0.111 |
| Bamboogle | 0.232 | **0.296** |
---
## 🤝 Acknowledgements
* [Qwen LM](https://github.com/QwenLM) for the base model.
* [Search‑R1 authors](https://github.com/PeterGriffinJin/Search-R1) for the dataset & baseline.
* [Volcengine **VERL**](https://github.com/volcengine/verl) for the GRPO training toolkit.
* HuggingFace for the open ecosystem.
---
## 📄 License & citation
Code is released under **MIT**; model weights under the original **Qwen open‑source license**.
```bibtex
@misc{shin2025qwen25_searchr1_multiturn,
author = {Seungyoun Shin},
title = {Qwen2.5-3B Search-R1-Multiturn (reproduce)},
year = 2025,
howpublished = {HuggingFace Model Hub},
url = {https://huggingface.co/Seungyoun/qwen2.5-3b-it_searchR1-like-multiturn}
}
```