Initial upload

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,189 @@
+---
+license: mit
+tags:
+- reinforcement-learning
+- agentic-reasoning
+- math-reasoning
+- tool-use
+language:
+- en
+- zh
+pipeline_tag: text-generation
+---
+# rStar2-Agent-14B: Advanced Agentic Reasoning Model
+
+## Model Description
+
+This is a reproduced version of rStar2-Agent, a 14B parameter math reasoning model that achieves performance comparable to 67B DeepSeek-R1 through pure agentic reinforcement learning. The model excels at planning, reasoning, and autonomously using coding tools to efficiently explore, verify, and reflect for complex problem-solving.
+
+## Usage
+
+This is an example usage. To reproduce the math evaluation results in technical report, please refer to [@microsoft/rstar](https://github.com/microsoft/rstar).
+
+### 1. Start SGLang Server
+
+First, serve the model using SGLang with the following command:
+
+```bash
+python -m sglang.launch_server \
+    --model-path rstar2-reproduce/rstar2-agent \
+    --port 30000 \
+    --tensor-parallel-size 4 \
+    --tool-call-parser qwen25
+```
+
+**Parameters:**
+- `--model-path`: Path to the rStar2-Agent model
+- `--port`: Server port (default: 30000)
+- `--tensor-parallel-size`: Number of GPUs for parallel processing
+- `--tool-call-parser`: Parser for tool calls (use "qwen25" for this model)
+
+### 2. Use with OpenAI-compatible API
+
+```python
+from openai import OpenAI
+import json
+
+# Initialize OpenAI client pointing to SGLang server
+client = OpenAI(
+    base_url="http://localhost:30000/v1",  # SGLang server URL
+    api_key="EMPTY"  # No API key required for local server
+)
+
+# Define Python code execution tool for the model
+tools = [
+    {
+        "type": "function", 
+        "function": {
+            "name": "execute_python_code_with_standard_io",
+            "description": "Execute Python code with standard input and capture standard output.\nThis function takes a Python code string and an input string, provides the input string\nthrough standard input (stdin) to the code, and captures and returns any output produced\nthrough standard output (stdout). If the executed code raises an exception, the error\nmessage will be captured and returned instead.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "code": {
+                        "type": "string",
+                        "description": "A string containing Python code to be executed. The code can read from standard input using the input() function."
+                    },
+                    "input": {
+                        "type": "string", 
+                        "description": "A string that will be provided as standard input to the code when it calls input()."
+                    }
+                },
+                "required": ["code", "input"]
+            }
+        }
+    }
+]
+
+# Define Python code execution function
+def execute_python_code_with_standard_io(code, input_data):
+    """
+    Execute Python code with standard input and capture output.
+    
+    Args:
+        code (str): Python code to execute
+        input_data (str): Input data to provide to the code
+        
+    Returns:
+        str: Output from the executed code or error message
+    """
+    import subprocess
+    import sys
+    
+    try:
+        # Create subprocess to execute Python code
+        process = subprocess.Popen(
+            [sys.executable, "-c", code],
+            stdin=subprocess.PIPE,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            text=True
+        )
+        
+        # Send input and get output
+        stdout, stderr = process.communicate(input=input_data)
+        
+        if stderr:
+            return f"Error: {stderr}"
+        return stdout.strip()
+        
+    except Exception as e:
+        return f"Execution error: {str(e)}"
+
+# Example: Create a math problem conversation
+messages = [
+    {
+        "role": "user", 
+        "content": "You must put your answer inside <answer> </answer> tags, i.e., <answer> answer here </answer>. And your final answer will be extracted automatically by the \\boxed{} tag. Solve this math problem: Find the sum of all prime numbers less than 20."
+    }
+]
+
+# Main conversation loop - handle tool calls until completion
+turn_idx = 0
+while True:
+    print(f'========== Turn: {turn_idx} ==========')
+    turn_idx += 1
+    
+    # Get model response with tool support
+    response = client.chat.completions.create(
+        model="rstar2-reproduce/rstar2-agent",
+        messages=messages,
+        tools=tools,
+        tool_choice="auto",  # Let model decide when to use tools
+        temperature=0.6      # Adjust for creativity vs consistency
+    )
+    
+    # Add the assistant's response to conversation history
+    messages.append(response.choices[0].message)
+    
+    print(f'{response.choices[0].message.content}')
+    
+    # Check if model wants to use tools
+    if response.choices[0].message.tool_calls:        
+        # Process each tool call
+        for tool_call in response.choices[0].message.tool_calls:
+            function_args = json.loads(tool_call.function.arguments)
+            
+            print(f">>> Executing Code:\n{function_args['code']}")
+            input_text = function_args.get('input', '')
+            print(f">>> With Input: {input_text if input_text else '(no input)'}")
+            
+            # Execute the Python code
+            result = execute_python_code_with_standard_io(
+                function_args["code"], 
+                function_args.get("input", "")
+            )
+            
+            print(f">>> Tool result: {result}")
+            
+            # Add tool response to conversation
+            messages.append({
+                "role": "tool",
+                "tool_call_id": tool_call.id,
+                "content": result
+            })
+    else:
+        # No more tool calls, conversation finished
+        print("✅ No more tool calls. Conversation finished.")
+        break
+```
+
+
+## Citation
+
+If you use this model in your research, please cite:
+```bibtex
+@misc{shang2025rstar2agentagenticreasoningtechnical,
+      title={rStar2-Agent: Agentic Reasoning Technical Report}, 
+      author={Ning Shang and Yifei Liu and Yi Zhu and Li Lyna Zhang and Weijiang Xu and Xinyu Guan and Buze Zhang and Bingcheng Dong and Xudong Zhou and Bowen Zhang and Ying Xin and Ziming Miao and Scarlett Li and Fan Yang and Mao Yang},
+      year={2025},
+      eprint={2508.20722},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.20722}, 
+}
+```
+
+## License
+
+This model is released under the MIT License.

added_tokens.json

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+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. The reasoning process is enclosed within <reason> </reason> and answer is enclosed within <answer> </answer> tags, respectively, i.e., <reason> reasoning process here </reason> <answer> answer here </answer>.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nA conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. The reasoning process is enclosed within <reason> </reason> and answer is enclosed within <answer> </answer> tags, respectively, i.e., <reason> reasoning process here </reason> <answer> answer here </answer>.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n<reason>' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 17408,
+  "layer_types": [
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
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+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 40,
+  "model_type": "qwen3",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "max_new_tokens": 2048,
+  "transformers_version": "4.53.0"
+}

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+  "tokenizer_class": "Qwen2Tokenizer",
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+}