Add pipeline tag, library name, link to paper and Github repository

README.md

@@ -1,10 +1,13 @@
 ---
-license: apache-2.0
 datasets:
 - BAAI/Infinity-Instruct
 language:
 - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
+
 # Infinity Instruct
 
 <p align="center">
@@ -12,7 +15,7 @@ language:
 </p>
 <p align="center">
 <em>Beijing Academy of Artificial Intelligence (BAAI)</em><br/>
-<em>[Paper][Code][🤗] (would be released soon)</em>
+<em>[Paper](https://huggingface.co/papers/2506.11116)[Code](https://github.com/FlagOpen/FlagScale)[\ud83e\udd17] (would be released soon)</em>
 </p>
 
 Infinity-Instruct-3M-0625-Mistral-7B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on [Infinity-Instruct-3M and Infinity-Instruct-0625](https://huggingface.co/datasets/BAAI/Infinity-Instruct) and showing favorable results on AlpacaEval 2.0 compared to Mixtral 8x7B v0.1, Gemini Pro, and GPT-3.5.
@@ -133,8 +136,6 @@ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
 
-
-
 ## **Disclaimer**
 
 The resources, including code, data, and model weights, associated with this project are restricted for academic research purposes only and cannot be used for commercial purposes. The content produced by any version of Infinity Instruct is influenced by uncontrollable variables such as randomness, and therefore, the accuracy of the output cannot be guaranteed by this project. This project does not accept any legal liability for the content of the model output, nor does it assume responsibility for any losses incurred due to the use of associated resources and output results.
@@ -151,4 +152,4 @@ Our paper, detailing the development and features of the **Infinity Instruct** d
   journal={arXiv preprint arXiv:2406.XXXX},
   year={2024}
 }
-```
+```