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@@ -17,7 +17,9 @@
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- Beyond traditional language model APIs — you can employ any fine-tuning and sampling methods by executing custom paths through the model or accessing its hidden states. This allows for the comforts of an API with the flexibility of PyTorch.
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<p align="center">
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- <b><a href="https://arxiv.org/pdf/2209.01188.pdf">[Read paper]</a></b> | <b><a href="https://petals.ml/">[View website]</a></b>
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+ 📜 <b><a href="https://arxiv.org/pdf/2209.01188.pdf">Read paper</a></b>
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+
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+ 🖥️ <b><a href="https://petals.ml/">View website</a></b>
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</p>
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## How it works?
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@@ -56,6 +58,10 @@ Be careful: some features may not work, interfaces may change, and we have no de
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A stable version of the code and a public swarm open to everyone will be released in November 2022. You can [subscribe](https://petals.ml/) to be emailed when it happens or fill in [this form](https://forms.gle/TV3wtRPeHewjZ1vH9) to help the public launch by donating GPU time. In the meantime, you can launch and use your own private swarm.
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+### 📋 Terms of use
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+
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+Before using Petals to run a language model, please make sure that you are familiar with its terms of use, risks, and limitations. For BLOOM, they are described in its [model card](https://huggingface.co/bigscience/bloom) and [license](https://huggingface.co/spaces/bigscience/license).
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+
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### 🔒 Privacy and security
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If you work with sensitive data, you should only use a private swarm (or a subset of servers in the public swarm) hosted by people and institutions you trust, who are authorized to process this data.
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Alexander Borzunov