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+ ---
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+ inference: false
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+ ---
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+
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+ # Robin Model Card
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+
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+ ## Model Details
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+
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+ Robin is a series of models finetuned from LLaMA on several high-quality data.
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+
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+ - **Developed by:** [LMFlow](https://github.com/OptimalScale/LMFlow/)
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+ - **Model type:** An auto-regressive language model based on the transformer architecture.
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+ - **License:** Non-commercial license
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+ - **Finetuned from model:** [LLaMA](https://arxiv.org/abs/2302.13971).
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+
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+ ### Model Sources
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+
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+ - **Repository:** https://github.com/OptimalScale/LMFlow/
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+ - **Blog:** https://medium.com/@hkust.ml/robin-v2-launches-achieves-unparalleled-performance-on-openllm-4f6886e822c1
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+ - **Paper:** https://arxiv.org/abs/2306.12420
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+ - **Demo:** https://lmflow.com/
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+
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+ ## Uses
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+
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+ 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.
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+
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+ ## How to Get Started with the Model
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+ We provide four kinds of demos including:
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+ - 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.
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+ - Colab Chatbot (shell): An interactive shell-based chatbot for you to easily deploy a chatbot on colab.
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+ - Colab Chatbot (web): An interactive web-based chatbot for you to easily deploy your own chatbot on colab.
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+ - 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.
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+ Please refer to https://github.com/OptimalScale/LMFlow#demos
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+
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+ ## Training Details
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+ 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).
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+ The new training split is created by merging the following datasets:
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+ - ShareGPT: randomly sample 50K English data and 10K Chinese data from ShareGPT.
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+ - GPT-4-LLM: 52K English data from GPT-4-LLM.
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+ - BELLE: randomly sample 80K Chinese data from BELLE.
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+
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+ See more details in the "Instruction Tuning" section in our [paper](https://arxiv.org/pdf/2306.12420.pdf).
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+
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+ ## Evaluation
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+
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+ Robin is evaluated with [LMFlow Benchmark](https://blog.gopenai.com/lmflow-benchmark-an-automatic-evaluation-framework-for-open-source-llms-ef5c6f142418).
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+ See more details in this [paper](https://arxiv.org/pdf/2306.12420.pdf).
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+
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+ ## Citation
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+ If you find this repository useful, please consider giving ⭐ and citing our [paper](https://arxiv.org/abs/2306.12420):
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+
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+ ```
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+ @misc{lmflow,
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+ author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang},
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+ title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models},
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+ year = {2023},
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+ publisher = {GitHub},
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+ journal = {GitHub repository},
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+ howpublished = {\url{https://optimalscale.github.io/LMFlow/}},
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+ }
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+ ```