Update README.md

README.md

@@ -16,6 +16,7 @@ We explore supervised multitask pre-training by proposing ***Instruction Pre-Tra
 
 **************************** **Updates** ****************************
 * 2024/9/20: Our paper has been accepted by EMNLP 2024 main conference🎉
+* 2024/9/11: Updated [FAQ on continual pre-training from Llama3](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
 * 2024/8/29: Updated [guidelines](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B) on evaluating any 🤗Huggingface models on the domain-specific tasks
 * 2024/7/31: Updated pre-training suggestions in the `Advanced Usage` section of [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
 * 2024/7/15: We scaled up the pre-trained tokens from 100B to 250B, with the number of synthesized instruction-response pairs reaching 500M. The performance trend on downstream tasks throughout the pre-training process:
@@ -224,7 +225,7 @@ text_ids = tokenizer(text, add_special_tokens=False, **kwargs).input_ids
 ## Citation
 If you find our work helpful, please cite us:
 
-Instruction Pre-Training (EMNLP 2024)
+[Instruction Pre-Training](https://huggingface.co/papers/2406.14491) (EMNLP 2024)
 ```bibtex
 @article{cheng2024instruction,
   title={Instruction Pre-Training: Language Models are Supervised Multitask Learners},
@@ -234,7 +235,7 @@ Instruction Pre-Training (EMNLP 2024)
 }
 ```
 
-[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530)（ICLR 2024）
+[Adapt LLM to Domains](https://huggingface.co/papers/2309.09530) (ICLR 2024)
 ```bibtex
 @inproceedings{
 cheng2024adapting,