Add pipeline tag, library name and link to Github repo

This PR adds the `pipeline_tag` and `library_name` to the model card, ensuring the model appears correctly in search results.
It also adds the link to the Github repo.

README.md

@@ -1,10 +1,13 @@
 ---
-license: llama3.1
 datasets:
 - BAAI/Infinity-Instruct
 language:
 - en
+license: llama3.1
+pipeline_tag: text-generation
+library_name: transformers
 ---
+
 # Infinity Instruct
 
 <p align="center">
@@ -39,7 +42,7 @@ Infinity-Instruct-7M-Gen-Llama3.1-70B is an opensource supervised instruction tu
 <img src="fig/trainingflow.png">
 </p>
 
-Infinity-Instruct-7M-Gen-Llama3.1-70B is tuned on Million-level instruction dataset [Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct). First, we apply the foundational dataset Infinity-Instruct-7M to improve the foundational ability (math & code) of Llama3.1-70B, and get the foundational instruct model Infinity-Instruct-7M-Llama3-70B. Then we finetune the Infinity-Instruct-7M-Llama3-70B to get the stronger chat model Infinity-Instruct-7M-Gen-Llama3_1-70B. Here is the training hyperparamers. 
+Infinity-Instruct-7M-Gen-Llama3_1-70B is tuned on Million-level instruction dataset [Infinity-Instruct](https://huggingface.co/datasets/BAAI/Infinity-Instruct). First, we apply the foundational dataset Infinity-Instruct-7M to improve the foundational ability (math & code) of Llama3.1-70B, and get the foundational instruct model Infinity-Instruct-7M-Llama3-70B. Then we finetune the Infinity-Instruct-7M-Llama3-70B to get the stronger chat model Infinity-Instruct-7M-Gen-Llama3_1-70B. Here is the training hyperparamers. 
 
 ```bash
 epoch: 3
@@ -54,7 +57,7 @@ global_batch_size: 528
 clip_grad: 1.0
 ```
 
-Thanks to [FlagScale](https://github.com/FlagOpen/FlagScale), we could concatenate multiple training samples to remove padding token and apply diverse acceleration techniques to the traning procudure. It effectively reduces our training costs. We will release our code in the near future!
+Thanks to [FlagScale](https://github.com/FlagOpen/FlagScale), we could concatenate multiple training samples to remove padding token and apply diverse acceleration techniques to the traning procudure. It effectively reduces our training costs. We will release our code in the near future! The code is available at: https://github.com/FlagOpen/FlagScale
 
 ## **Benchmark**
 
@@ -76,7 +79,7 @@ Thanks to [FlagScale](https://github.com/FlagOpen/FlagScale), we could concatena
 
 ## **How to use**
 
-Infinity-Instruct-7M-Gen-Llama3_1-70B adopt the same chat template of [Llama3-70B-instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct):
+Infinity-Instruct-7M-Gen-Llama3_1-70B adopt the same chat template of [Llama3-70B-instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct):\
 
 ```bash
 <|begin_of_text|><|start_header_id|>user<|end_header_id|>