Update pipeline tag to `graph-ml` and add HF paper link

This PR improves the model card for `PKU-ML/G1-CoT-SFT-7B` by:

* Updating the `pipeline_tag` to `graph-ml` for more precise categorization and discoverability on the Hugging Face Hub (https://huggingface.co/models?pipeline_tag=graph-ml).
* Adding a direct link to the official Hugging Face paper page for easy access to the associated research.

Please review these changes.

README.md

@@ -1,48 +1,49 @@
 ---
-license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
 datasets:
 - PKU-ML/Erdos-CoT
 language:
 - en
+library_name: transformers
+license: apache-2.0
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2.5-7B-Instruct
-pipeline_tag: text-generation
+pipeline_tag: graph-ml
 tags:
 - graph
 - chat
-library_name: transformers
 ---
 
-
 # G1-CoT-SFT-7B
 
+This model is part of the G1 series of large language models for graph reasoning tasks, as presented in the paper [G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning](https://huggingface.co/papers/2505.18499).
+
 ## Introduction
 
 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. 
-We apply Group Relative Policy Optimization (GRPO) for reinforcement learning with supervised finetuning as a prelimary step.
+We apply Group Relative Policy Optimization (GRPO) for reinforcement learning with supervised finetuning as a preliminary step.
 
 G1 brings the following improvements:
 
-- **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.
-- **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).
-- **NO Compromise on general reasoning**: Crucially, G1 preserves general reasoning ability (GSM8K, MATH, MMLU-Pro), proving its versatility.
+-   **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.
+-   **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).
+-   **NO Compromise on general reasoning**: Crucially, G1 preserves general reasoning ability (GSM8K, MATH, MMLU-Pro), proving its versatility.
 
 
 **This repo contains the G1-CoT-SFT-7B model**, which has the following features:
-- Type: Causal Language Models
-- Training Stage: SFT
-- Architecture: the same with Qwen2.5-Instruct
-- Number of Parameters: 7.62B
-- Context Length: Full 32,768 tokens and generation 8192 tokens
+-   Type: Causal Language Models
+-   Training Stage: SFT
+-   Architecture: the same as Qwen2.5-Instruct
+-   Number of Parameters: 7.62B
+-   Context Length: Full 32,768 tokens and generation 8192 tokens
 
 For more details, please refer to our [paper](https://arxiv.org/pdf/2505.18499) and [GitHub](https://github.com/PKU-ML/G1/tree/main).
 
 
 ## Requirements
 
-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`.
+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`.
 
 With `transformers<4.37.0`, you will encounter the following error:
 ```
@@ -72,10 +73,18 @@ model = AutoModelForCausalLM.from_pretrained(
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-prompt = "The task is to determine the degree centrality of a node in the graph.\n\n"\
-        "Degree centrality for a node is the fraction of nodes it is connected to.\n\n"\
-        "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"\
-        "Question: What is the degree centrality of node 2 in the graph?\n\n"\
+prompt = "The task is to determine the degree centrality of a node in the graph.
+
+"\
+        "Degree centrality for a node is the fraction of nodes it is connected to.
+
+"\
+        "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).
+
+"\
+        "Question: What is the degree centrality of node 2 in the graph?
+
+"\
         "You need to format your answer as a float number."
 messages = [
     {"role": "user", "content": INSTRUCTION_TEMPLATE.format(instruction=prompt)}