Update README.md

README.md

@@ -2,26 +2,41 @@
 license: apache-2.0
 language:
 - en
+datasets:
+- instruction-pretrain/ft-instruction-synthesizer-collection
 ---
 # Instruction Pre-Training: Language Models are Supervised Multitask Learners
 This repo contains the **context-based instruction synthesizer** used in our paper **Instruction Pre-Training: Language Models are Supervised Multitask Learners**.
 
-We explore supervised multitask pre-training by proposing ***Instruction Pre-Training***, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train language models. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of *Instruction Pre-Training*. ***Instruction Pre-Training* outperforms *Vanilla Pre-training* in both general pre-training from scratch and domain-adaptive continued pre-training.** In pre-training from scratch, *Instruction Pre-Training* not only improves pre-trained base models but also benefits more from further instruction tuning. In continual pre-training, *Instruction Pre-Training* enables Llama3-8B to be comparable to or even outperform Llama3-70B.
+We explore supervised multitask pre-training by proposing ***Instruction Pre-Training***, a framework that scalably augments massive raw corpora with instruction-response pairs to pre-train language models. The instruction-response pairs are generated by an efficient instruction synthesizer built on open-source models. In our experiments, we synthesize 200M instruction-response pairs covering 40+ task categories to verify the effectiveness of *Instruction Pre-Training*. ***Instruction Pre-Training* outperforms *Vanilla Pre-training* in both general pre-training from scratch and domain-adaptive continual pre-training.** In pre-training from scratch, *Instruction Pre-Training* not only improves pre-trained base models but also benefits more from further instruction tuning. In continual pre-training, *Instruction Pre-Training* enables Llama3-8B to be comparable to or even outperform Llama3-70B.
 
 <p align='center'>
     <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/vRdsFIVQptbNaGiZ18Lih.png" width="400">
 </p>
 
-## Synthesize Instruction-Response Pairs from Any Raw Corproa
-We conduct multitask fine-tuning on a language model to develop an instruction synthesizer capable of generating instruction-response pairs from any raw text.
+
+## Resources
+**🤗 We share our data and models with example usages, feel free to open any issues or discussions! 🤗**
+
+- Context-Based Instruction Synthesizer: [instruction-synthesizer](https://huggingface.co/instruction-pretrain/instruction-synthesizer)
+- Fine-Tuning Data for the Synthesizer: [ft-instruction-synthesizer-collection](https://huggingface.co/datasets/instruction-pretrain/ft-instruction-synthesizer-collection)
+- General Models Pre-Trained from Scratch:
+  - [InstructLM-500M](https://huggingface.co/instruction-pretrain/InstructLM-500M)
+  - [InstructLLM-1.3B](https://huggingface.co/instruction-pretrain/InstructLLM-1.3B)
+- Domain-Specific Models Pre-Trained from Llama3-8B:
+  - [Finance-Llama3-8B](https://huggingface.co/instruction-pretrain/finance-Llama3-8B)
+  - [Biomedicine-Llama3-8B](https://huggingface.co/instruction-pretrain/medicine-Llama3-8B)
+
+
+## Synthesize Instruction-Response Pairs based on Any Raw text
+We conduct multitask fine-tuning on a language model to develop an instruction synthesizer capable of generating instruction-response pairs from any raw text. The fine-tuning data are available at [ft-instruction-synthesizer-collection](https://huggingface.co/datasets/instruction-pretrain/ft-instruction-synthesizer-collection)
+
 
 <p align='center'>
-    <img src="./https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0889QyG59QM3rPeZlcTzZ.png" width="700">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/66711d2ee12fa6cc5f5dfc89/0889QyG59QM3rPeZlcTzZ.png" width="700">
 </p>
 
-The fine-tuning data are available at [ft-instruction-synthesizer-collection](https://huggingface.co/datasets/instruction-pretrain/ft-instruction-synthesizer-collection)
-
-To prompt the synthesizer to generate instruction-response pairs based on a given raw text:
+For example, to prompt the synthesizer to generate instruction-response pairs based on a given raw text:
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
@@ -75,8 +90,13 @@ for index, pair in enumerate(instruction_response_pairs):
     print(f'## Instruction {index + 1}:\n{pair["Q"]}\n## Response {index + 1}:\n{pair["A"]}\n')
 ```
 
+### To-Do
+- [ ] Add example usages for synthesizing few-shot examples
+
 ## Citation
 If you find our work helpful, please cite us:
+
+[AdaptLLM](https://huggingface.co/papers/2309.09530)
 ```bibtex
 @inproceedings{
 cheng2024adapting,