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inference: false |
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--- |
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# Robin Model Card |
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## Model Details |
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Robin is a series of models finetuned from LLaMA on several high-quality data. |
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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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### Model Sources |
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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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## Uses |
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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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## 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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## 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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See more details in the "Instruction Tuning" section in our [paper](https://arxiv.org/pdf/2306.12420.pdf). |
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## Evaluation |
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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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## 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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@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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``` |
