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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,207 @@
+---
+license: apache-2.0
+language:
+- zh
+- en
+base_model:
+- Qwen/Qwen3-8B
+---
+
+# TableGPT-R1
+
+## Model details
+
+We developed and released TableGPT-R1, a specialized large language model optimized for complex tabular reasoning and data analysis. Unlike traditional models that rely solely on Supervised Fine-Tuning (SFT), TableGPT-R1 is trained using a systematic Reinforcement Learning (RL) framework. It is designed to bridge the gap between natural language understanding and professional data science requirements, such as multi-step logic, robust code execution, and autonomous environment interaction.
+
+**Model Developers**  
+
+Zhejiang University
+
+**Key Technical Breakthroughs**
+
+* **Autonomous Agentic Reasoning**: The model is trained to "think" before acting. It generates a visible reasoning chain within `<think>` tags, plans Python-based data manipulations, and refines its strategy based on environment feedback (Code Interpreter).
+* **Unified Reward System**: We introduced a hybrid reward mechanism that combines rule-based verification (for deterministic SQL/Code tasks) with a **Criteria-Injected Reward Model** (for open-ended analytical questions), ensuring both accuracy and interpretability.
+* **GRPO++ Framework**: Utilizing an enhanced version of Group Relative Policy Optimization, the model optimizes its decision-making process across diverse table structures while maintaining its general-purpose reasoning capabilities.
+* **Cold-Start Data Engineering**: Bootstrapped with high-quality, long-chain reasoning trajectories, allowing the model to handle extreme table heterogeneity and complex multi-table joins.
+
+**Input**
+
+TableGPT-R1 accepts both natural language instructions and tabular data. It uniquely supports **table-path inputs**, enabling the model to autonomously load and retrieve information from files using a built-in code interpreter.
+
+**Output** 
+
+TableGPT-R1 supports two output behaviors depending on the task:
+
+For tasks requiring logical deduction, metadata explanation, or semantic understanding without external execution.
+* **Format**: `<think> ... </think> [Answer]`
+* **Behavior**: The model performs internal "Chain-of-Thought" to verify its logic before presenting the final result.
+
+For data-intensive tasks requiring precise calculation, visualization, or large-scale data processing.
+* **Format**: 
+  1.  **Plan**: `<think> ... </think>` (Analyze the goal and plan the code)
+  2.  **Act**: `<tool_call> ... </tool_call>` (Generate Python/SQL code)
+  3.  **Observe**: `<observation> ... </observation>` (Receive environment feedback)
+  4.  **Finalize**: `<answer> ... </answer>` (Summarize results)
+
+* **Behavior**: The model operates as an autonomous agent, reacting to execution errors or intermediate data results to ensure accuracy.
+
+Additionally, to enforce model thinking, the default chat template automatically includes `<think>`. Therefore, it is normal for the model's output to contain only `</think>` without an explicit opening `<think>` tag.
+
+**Language**  
+
+Our model places a strong emphasis on Chinese corpora, and currently, queries in other languages may have limited support.
+
+**Model Architecture** 
+
+TableGPT-R1 is built upon the **Qwen3-8B** transformer architecture, significantly enhanced for long-context tabular understanding and agentic workflows.
+
+* **Base Backbone**: Qwen3-8B (Dense Transformer).
+* **Context Window**: 128K tokens, optimized for processing large-scale table schemas, extensive metadata, and long execution logs.
+* **Specialized Tokenizer**: Enhanced to handle structural delimiters, whitespace in tables, and code-specific syntax (Python/SQL) more efficiently.
+* **Agentic Loop Integration**: The architecture is designed to support a seamless **"Think-Act-Observe"** cycle. It treats the environment's feedback (Code Interpreter output) as a first-class sequence input, allowing for real-time error correction and iterative reasoning.
+* **Instruction Following**: Optimized via RL to strictly adhere to formatting constraints, distinguishing between internal thought process and external tool calls.
+
+**Status**  
+
+This model is static, trained on an offline dataset. Future versions may be released to enhance its performance on specialized tasks.
+
+**QuickStart**
+
+This code snippet demonstrates how to build a prompt with table information, and shows how to load the tokenizer, load the model, and generate content.
+
+> Note that you need `transformers>=4.51.0` to use `TableGPT-R1`:
+> ```sh
+> pip install transformers>=4.51.0
+> ```
+
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Using pandas to read some structured data
+import pandas as pd
+from io import StringIO
+
+# single table
+EXAMPLE_CSV_CONTENT = """
+"Loss","Date","Score","Opponent","Record","Attendance"
+"Hampton (14–12)","September 25","8–7","Padres","67–84","31,193"
+"Speier (5–3)","September 26","3–1","Padres","67–85","30,711"
+"Elarton (4–9)","September 22","3–1","@ Expos","65–83","9,707"
+"Lundquist (0–1)","September 24","15–11","Padres","67–83","30,774"
+"Hampton (13–11)","September 6","9–5","Dodgers","61–78","31,407"
+"""
+
+csv_file = StringIO(EXAMPLE_CSV_CONTENT)
+df = pd.read_csv(csv_file)
+
+model_name = "tablegpt/TableGPT-R1"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, torch_dtype="auto", device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+example_prompt_template = """Given access to several pandas dataframes, write the Python code to answer the user's question.
+
+/*
+"{var_name}.head(5).to_string(index=False)" as follows:
+{df_info}
+*/
+
+Question: {user_question}
+"""
+question = "哪些比赛的战绩达到了40胜40负？"
+
+prompt = example_prompt_template.format(
+    var_name="df",
+    df_info=df.head(5).to_string(index=False),
+    user_question=question,
+)
+
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+    {"role": "user", "content": prompt},
+]
+text = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(**model_inputs, max_new_tokens=8192)
+generated_ids = [
+    output_ids[len(input_ids) :]
+    for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+
+**Deployment**
+
+For deployment, you can use `sglang>=0.5.2` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint:
+- SGLang:
+    ```shell
+    python -m sglang.launch_server --model-path tablegpt/TableGPT-R1 --served-model-name TableGPT-R1 --reasoning-parser qwen3
+    ```
+- vLLM:
+    ```shell
+    vllm serve tablegpt/TableGPT-R1 --served-model-name TableGPT-R1 --enable-reasoning --reasoning-parser deepseek_r1
+    ```
+
+Then you can access the Chat API by:
+
+```bash
+curl http://localhost:xxx/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+    "model": "TableGPT-R1",
+    "messages": [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "xxxxx?"}
+    ]
+    }'
+
+```
+
+**License**  
+
+TableGPT-R1 is under apache-2.0 license.
+
+**Research Paper**  
+
+TableGPT-R1 is introduced and validated in the paper "[TableGPT-R1: Advancing Tabular Reasoning Through Reinforcement Learning](https://arxiv.org/xxxx)" available on arXiv.
+
+**Where to send questions or comments about the model**  
+
+Inquiries and feedback are welcome at [j.zhao@zju.edu.cn](mailto:j.zhao@zju.edu.cn).
+
+## Evaluation Results
+
+ Performance comparison grouped by model scale. Left Group: Models with comparable
+parameters to TableGPT-R1. Right Group: Significantly larger models and proprietary closed-source
+models. Bold indicates the best result within each group. Gray background highlights TableGPT-R1. Abbreviations: Q3: Qwen3; QwQ: QwQ-32B; DS-V3: DeepSeek-V3; Q-Plus: Qwen-Plus;
+T-LLM: TableLLM; Llama: Llama-3.1-8B; TGPT2: TableGPT2-7B; TGPT-R1: TableGPT-R1-8B;
+FC: Fact Checking; NR: Numerical Reasoning; SC: Structure Comprehending; DA: Data Analysis;
+CG: Chart Generation.
+
+| Benchmark | Task | Met. | Q3-8B | T-LLM | Llama | TGPT2 | **TGPT-R1 (8B)** | Q3-14B | Q3-32B | Q3-70B | QwQ | GPT-4o | DS-V3 | Q-Plus |
+| :--- | :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
+| **Internal Bench** | Table Info | Acc | 69.20 | 0.97 | 37.26 | - | **82.00** | 66.10 | 72.58 | 51.10 | 69.68 | 67.26 | 66.00 | **76.90** |
+| | Table Path | Acc | 73.90 | 0.65 | 31.77 | - | **85.00** | 74.70 | 78.55 | 60.50 | 75.00 | - | 72.90 | **81.50** |
+| **NL2SQL** | Spider | EX | 86.07 | 65.30 | 73.59 | 74.38 | **86.73** | 87.61 | 87.80 | 61.71 | 85.33 | 87.98 | 88.54 | **89.19** |
+| | BIRD | EX | 61.67 | 30.64 | 40.03 | 49.28 | **63.04** | 61.80 | 63.04 | 53.91 | 54.30 | 65.25 | 65.65 | **68.32** |
+| **Holistic Table** | TableBench DP | Rge | 42.10 | 3.63 | 18.04 | 42.10 | **47.58** | 47.41 | **52.18** | 48.61 | 49.33 | 40.91 | 36.56 | 31.01 |
+| **Evaluation** | PoT | Rge | 28.01 | 0.00 | 6.73 | **39.80** | 34.86 | 36.61 | 37.78 | 27.72 | 40.03 | **51.96** | 33.05 | 41.79 |
+| | SCoT | Rge | 41.86 | 1.99 | 21.94 | 40.70 | **48.68** | 47.36 | 47.47 | 45.68 | 44.84 | 41.43 | **50.11** | 44.06 |
+| | TCoT | Rge | 41.71 | 3.18 | 15.26 | 46.19 | **48.16** | 46.07 | 51.74 | 47.63 | 48.83 | 45.71 | **54.28** | 52.07 |
+| **RealHitBench** | FC | EM | 58.83 | 33.44 | 30.32 | 43.06 | **62.85** | 62.36 | **65.00** | 60.23 | 28.95 | 55.22 | **65.08** | 56.53 |
+| | NR | EM | 39.43 | 13.51 | 18.25 | 31.75 | **44.91** | 45.62 | 47.45 | 42.82 | 42.61 | 48.66 | **53.89** | 49.88 |
+
+## Citation
+
+If you find our work helpful, please cite us by
+
+```bibtex
+
+```

added_tokens.json

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config.json

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+  ],
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+    "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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
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+  "max_position_embeddings": 40960,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
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+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.53.2",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.53.2"
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special_tokens_map.json

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+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

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+    "added_tokens_decoder": {
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+            "normalized": false,
+            "rstrip": false,
+            "single_word": false,
+            "special": true
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+            "normalized": false,
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+            "special": true
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+        "<|vision_pad|>",
+        "<|image_pad|>",
+        "<|video_pad|>"
+    ],
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+    "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\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>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\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\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if reasoning_content %}\n            {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n<think>\\n' }}\n{%- endif %}",
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+    "eos_token": "<|im_end|>",
+    "errors": "replace",
+    "model_max_length": 262144,
+    "pad_token": "<|endoftext|>",
+    "split_special_tokens": false,
+    "tokenizer_class": "Qwen2Tokenizer",
+    "unk_token": null,
+    "add_bos_token": false
+}