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README.md

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-library_name: transformers
-tags: []
----
-
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
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-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
+# Qwen2.5‑3B Search‑R1‑Multiturn (reproduce)
 
+> **Author · Seungyoun Shin**
+> 🤗 Model Hub: [https://huggingface.co/Seungyoun/qwen2.5-3b-it\_searchR1-like-multiturn](https://huggingface.co/Seungyoun/qwen2.5-3b-it_searchR1-like-multiturn)
 
-#### Training Hyperparameters
+A faithful re‑implementation of the *Search‑R1* on **Qwen 2.5‑3B**, trained purely on the Wikipedia‑based Search‑R1 corpus (HotpotQA train split) with GRPO via the open‑source [VERL](https://github.com/volcengine/verl) framework.
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
+---
 
-## Citation [optional]
+## 🚀 Quick start
+
+
+### Full inference script (Using duck-duck-go)
+
+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()
+```
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+---
 
-**BibTeX:**
+## 🧠 Reasoning style
 
-[More Information Needed]
+```
+<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>
+```
 
-**APA:**
+---
 
-[More Information Needed]
+## 📊 Evaluation (Pass@1)
 
-## Glossary [optional]
+| 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**     |
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## 🤝 Acknowledgements
 
-[More Information Needed]
+* [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.
 
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+## 📄 License & citation
 
-## Model Card Contact
+Code is released under **MIT**; model weights under the original **Qwen open‑source license**.
 
-[More Information Needed]
+```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}
+}
+```