Create README.md

README.md

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+---
+inference: false
+---
+
+# Robin Model Card
+
+## Model Details
+
+Robin is a series of models finetuned from LLaMA on several high-quality data.
+
+- **Developed by:** [LMFlow](https://github.com/OptimalScale/LMFlow/)
+- **Model type:** An auto-regressive language model based on the transformer architecture.
+- **License:** Non-commercial license
+- **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971).
+
+### Model Sources
+
+- **Repository:** https://github.com/OptimalScale/LMFlow/
+- **Blog:** https://medium.com/@hkust.ml/robin-v2-launches-achieves-unparalleled-performance-on-openllm-4f6886e822c1
+- **Paper:** https://arxiv.org/abs/2306.12420
+- **Demo:** https://lmflow.com/
+
+## Uses
+
+Robin is primarily utilized for conducting research on extensive language models and chatbots, catering to users specializing in natural language processing, machine learning, and artificial intelligence research.
+
+## How to Get Started with the Model
+
+We provide four kinds of demos including:
+
+- Online Service: If you don't want to run any code and just want to try our models, we deploy our instruction-tuned LLaMA you to have a try.
+- Colab Chatbot (shell): An interactive shell-based chatbot for you to easily deploy a chatbot on colab.
+- Colab Chatbot (web): An interactive web-based chatbot for you to easily deploy your own chatbot on colab.
+- Local Deploy: We also provide a way for you to deploy your model/chatbot locally, which means you can deploy much larger model than previous three methods if you have enough resource.
+
+Please refer to https://github.com/OptimalScale/LMFlow#demos
+
+## Training Details
+
+
+Expanding upon the initial idea of self-instruct techniques, we incorporated several different data sources and build a new dataset called [LMFlow Dataset](http://lmflow.org:5000/lmflow_data.tar.gz). 
+The new training split is created by merging the following datasets:
+- ShareGPT: randomly sample 50K English data and 10K Chinese data from ShareGPT.
+- GPT-4-LLM: 52K English data from GPT-4-LLM.
+- BELLE: randomly sample 80K Chinese data from BELLE.
+
+See more details in the "Instruction Tuning" section in our [paper](https://arxiv.org/pdf/2306.12420.pdf).
+
+## Evaluation
+
+Robin is evaluated with [LMFlow Benchmark](https://blog.gopenai.com/lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms-ef5c6f142418).
+See more details in this [paper](https://arxiv.org/pdf/2306.12420.pdf).
+
+## Citation
+If you find this repository useful, please consider giving ⭐ and citing our [paper](https://arxiv.org/abs/2306.12420):
+
+```
+@misc{lmflow,
+  author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang},
+  title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://optimalscale.github.io/LMFlow/}},
+}
+```