123 lines
4.3 KiB
Markdown
123 lines
4.3 KiB
Markdown
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---
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license: apache-2.0
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datasets:
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- PKU-ML/Erdos
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- PKU-ML/Erdos-CoT
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language:
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- en
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metrics:
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- accuracy
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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pipeline_tag: text-generation
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tags:
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- graph
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- chat
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library_name: transformers
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---
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# G1-7B
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## Introduction
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G1 is the series of large language models trained on our benchmark [Erdos](https://huggingface.co/datasets/PKU-ML/Erdos) for solving graph reasoning tasks, based on Qwen2.5-Instruct.
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We apply Group Relative Policy Optimization (GRPO) for reinforcement learning with supervised finetuning as a prelimary step.
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G1 brings the following improvements:
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- **Significant improvement on graph reasoning**: G1 models achieve up to 46% improvement over baselines on Erdős, with the 7B variant matching OpenAI’s o3-mini and the 3B model surpassing Qwen2.5-72B-Instruct by notable margins.
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- **Strong Generalization to unseen graph tasks**: G1 exhibits zero-shot generalization on unseen graph tasks, improving performance on *other graph reasoning benchmarks* (GraphWiz, GraphArena) and *real-world graphs* (Cora, PubMed).
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- **NO Compromise on general reasoning**: Crucially, G1 preserves general reasoning ability (GSM8K, MATH, MMLU-Pro), proving its versatility.
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**This repo contains the G1-7B model**, which has the following features:
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- Type: Causal Language Models
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- Training Stage: SFT & RL
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- Architecture: the same with Qwen2.5-Instruct
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- Number of Parameters: 7.62B
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- Context Length: Full 32,768 tokens and generation 8192 tokens
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For more details, please refer to our [paper](https://arxiv.org/pdf/2505.18499) and [GitHub](https://github.com/PKU-ML/G1/tree/main).
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## Requirements
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The model is trained based on Qwen/Qwen2.5-7B-Instruct. The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
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With `transformers<4.37.0`, you will encounter the following error:
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```
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KeyError: 'qwen2'
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```
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## Quickstart
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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INSTRUCTION_TEMPLATE = """
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{instruction}
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Solve the above problem efficiently and clearly. The last line of your response should be of the following format: 'Therefore, the final answer is: $\\boxed{{ANSWER}}$. I hope it is correct' (without quotes) where ANSWER is just the final number or expression that solves the problem. Think step by step before answering.
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""".strip()
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model_name = "PKU-ML/G1-7B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "The task is to determine the degree centrality of a node in the graph.\n\n"\
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"Degree centrality for a node is the fraction of nodes it is connected to.\n\n"\
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"Here is an undirected graph containing nodes from 1 to 15. The edges are: (1, 15), (15, 11), (2, 3), (2, 6), (3, 6), (3, 7), (6, 7), (6, 8), (7, 8), (7, 14), (4, 10), (10, 5), (10, 12), (8, 14), (8, 9), (12, 11), (12, 13).\n\n"\
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"Question: What is the degree centrality of node 2 in the graph?\n\n"\
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"You need to format your answer as a float number."
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messages = [
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{"role": "user", "content": INSTRUCTION_TEMPLATE.format(instruction=prompt)}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=4096,
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top_p=0.95,
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top_k=30,
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temperature=0.6
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## Evaluation & Performance
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Detailed evaluation results are reported in this [📑 paper](https://arxiv.org/pdf/2505.18499).
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## Citation
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If you find our work helpful, feel free to give us a cite.
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```
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@article{guo2025g1,
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title={G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning},
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author={Guo, Xiaojun and Li, Ang and Wang, Yifei and Jegelka, Stefanie and Wang, Yisen},
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journal={arXiv preprint arXiv:2505.18499},
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year={2025}
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}
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```
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