README.md

---
base_model:
- Qwen/Qwen2-7B-Instruct
datasets:
- IDEA-FinAI/Golden-Touchstone
language:
- en
- zh
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
- finance
- text-generation-inference
- retrieval-augmented-generation
- rag
- graph-neural-networks
- llm-reasoning
---

<!-- markdownlint-disable first-line-h1 -->
<!-- markdownlint-disable html -->
<!-- markdownlint-disable no-duplicate-header -->

<div align="center">

<div style="margin: 20px 0;al">
  <img src="https://github.com/DataArcTech/RAG-Factory/blob/main/assets/logo.png?raw=true" width="120" height="120" alt="RAG-Factory Logo" style="border-radius: 20px; box-shadow: 0 8px 32px rgba(0, 217, 255, 0.3);">
</div>

# ✨ TouchstoneGPT-7B-Instruct: A Model for Think-on-Graph 3.0 via RAG-Factory
</div>

<div align="center" style="line-height: 1;">
  <a href="https://huggingface.co/papers/2509.21710" target="_blank" style="margin: 2px;">
    <img alt="Paper" src="https://img.shields.io/badge/Paper-2509.21710-b31b1b.svg?logo=arXiv" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://github.com/DataArcTech/RAG-Factory" target="_blank" style="margin: 2px;">
    <img alt="github" src="https://img.shields.io/github/stars/DataArcTech/RAG-Factory.svg?style=social" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://huggingface.co/datasets/IDEA-FinAI/Golden-Touchstone" target="_blank" style="margin: 2px;">
    <img alt="datasets" src="https://img.shields.io/badge/🤗-Datasets-yellow.svg" style="display: inline-block; vertical-align: middle;"/>
  </a>
  <a href="https://huggingface.co/IDEA-FinAI/TouchstoneGPT-7B-Instruct" target="_blank" style="margin: 2px;">
    <img alt="huggingface" src="https://img.shields.io/badge/🤗-Model-yellow.svg" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>

This Hugging Face repository hosts the `TouchstoneGPT-7B-Instruct` model, an instance of a Large Language Model (LLM) based on `Qwen/Qwen2-7B-Instruct`. This model is suitable for integration within the **Think-on-Graph 3.0 (ToG-3)** framework, a novel approach to Retrieval-Augmented Generation (RAG) that enhances LLM reasoning on heterogeneous graphs. The ToG-3 framework is implemented and further detailed in the [RAG-Factory GitHub repository](https://github.com/DataArcTech/RAG-Factory).

## Paper Abstract: Think-on-Graph 3.0

Retrieval-Augmented Generation (RAG) and Graph-based RAG has become the important paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, existing approaches face a fundamental trade-off. While graph-based methods are inherently dependent on high-quality graph structures, they face significant practical constraints: manually constructed knowledge graphs are prohibitively expensive to scale, while automatically extracted graphs from corpora are limited by the performance of the underlying LLM extractors, especially when using smaller, local-deployed models. This paper presents Think-on-Graph 3.0 (ToG-3), a novel framework that introduces Multi-Agent Context Evolution and Retrieval (MACER) mechanism to overcome these limitations. Our core innovation is the dynamic construction and refinement of a Chunk-Triplets-Community heterogeneous graph index, which pioneeringly incorporates a dual-evolution mechanism of Evolving Query and Evolving Sub-Graph for precise evidence retrieval. This approach addresses a critical limitation of prior Graph-based RAG methods, which typically construct a static graph index in a single pass without adapting to the actual query. A multi-agent system, comprising Constructor, Retriever, Reflector, and Responser agents, collaboratively engages in an iterative process of evidence retrieval, answer generation, sufficiency reflection, and, crucially, evolving query and subgraph. This dual-evolving multi-agent system allows ToG-3 to adaptively build a targeted graph index during reasoning, mitigating the inherent drawbacks of static, one-time graph construction and enabling deep, precise reasoning even with lightweight LLMs. Extensive experiments demonstrate that ToG-3 outperforms compared baselines on both deep and broad reasoning benchmarks, and ablation studies confirm the efficacy of the components of MACER framework.

## ✨ Features of RAG-Factory (Think-on-Graph 3.0 Implementation)

The [RAG-Factory](https://github.com/DataArcTech/RAG-Factory) framework, which implements the concepts of Think-on-Graph 3.0, provides a factory for building advanced RAG pipelines, including:

- Standard RAG implementations
- GraphRAG architectures
- Multi-modal RAG systems

<div align="center">
  <img src="https://github.com/DataArcTech/RAG-Factory/blob/main/assets/knowledge_base_screenshot.png?raw=true" alt="Example Knowledge Base Screenshot of RAG-Factory" width="800">
</div>

Key features include:
- Modular design for easy customization
- Support for various knowledge graph backends
- Integration with multiple LLM providers
- Configurable pipeline components

## Installation (for RAG-Factory)

To set up the RAG-Factory environment, clone the repository and install dependencies:

```bash
pip install -e .
```

## Usage (for RAG-Factory)

You can run predefined RAG pipelines using the `RAG-Factory` framework:

```bash
bash run.sh naive_rag/graph_rag/mm_rag
```
or

```bash
python main.py --config examples/graphrag/config.yaml
```

For more examples and detailed configurations, please refer to the `examples/` directory in the [RAG-Factory GitHub repository](https://github.com/DataArcTech/RAG-Factory).

## Usage of TouchstoneGPT-7B-Instruct

This `TouchstoneGPT-7B-Instruct` model is a `Qwen2-7B-Instruct`-based LLM that can be used for text generation tasks, either standalone or as a component within RAG frameworks like Think-on-Graph 3.0. Below is a code snippet using the `transformers` library to load the tokenizer and model and generate content.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "IDEA-FinAI/TouchstoneGPT-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("IDEA-FinAI/TouchstoneGPT-7B-Instruct")

prompt = "What is the sentiment of the following financial post: Positive, Negative, or Neutral?
sees #Apple at $150/share in a year (+36% from today) on growing services business."
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(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
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]
```

## Citation
If you find our work on Think-on-Graph 3.0 useful for your research and applications, please consider citing the paper:

```bibtex
@misc{wu2025ToG-3,
      title={Think-on-Graph 3.0: Efficient and Adaptive LLM Reasoning on Heterogeneous Graphs via Multi-Agent Dual-Evolving Context Retrieval}, 
      author={Xiaojun Wu, Cehao Yang, Xueyuan Lin, Chengjin Xu, Xuhui Jiang, Yuanliang Sun, Hui Xiong, Jia Li, Jian Guo},
      year={2025},
      eprint={2509.21710},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.21710}, 
}
```