Fix nits in readme

README.md

@@ -60,7 +60,7 @@ A stable version of the code and a public swarm open to everyone will be release
 
 ### 📋 Terms of use
 
-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).
+Before using Petals to run a language model, please make sure that you are familiar with its terms of use, risks, and limitations. In case of BLOOM, they are described in its [model card](https://huggingface.co/bigscience/bloom) and [license](https://huggingface.co/spaces/bigscience/license).
 
 ### 🔒 Privacy and security
 
@@ -101,7 +101,7 @@ For macOS, you can *probably* run everything normally if you manage to install d
 
 ## 🚀 Getting Started
 
-This is a toy example running on a local machine without GPU and with a tiny model. 
+This is a toy example running on a local machine without GPU and with a tiny model.
 For a detailed instruction with larger models, see ["Launch your own swarm"](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm).
 
 First, run a couple of servers, each in a separate shell. To launch your first server, run:
@@ -133,7 +133,7 @@ You can assign `--initial_peers` to one or multiple addresses of other servers,
 The only requirement is that at least one of them is running at the time.
 
 Before you proceed, __please run 3 servers__ for a total of 24 blocks (3x8). If you are running a different model,
-make sure your servers have enough total `--num_blocks` to cover that model. 
+make sure your servers have enough total `--num_blocks` to cover that model.
 
 Once your have enough servers, you can use them to train and/or inference the model:
 ```python
@@ -162,8 +162,8 @@ print("Gradients (norm):", model.transformer.word_embeddings.weight.grad.norm())
 ```
 
 Of course, this is a simplified code snippet. For actual training, see the example notebooks with "deep" prompt-tuning:
-- Simple text semantic classification: [examples/prompt-tuning-sst2.ipynb](./examples/prompt-tuning-sst2.ipynb).
-- A personified chatbot: [examples/prompt-tuning-personachat.ipynb](./examples/prompt-tuning-personachat.ipynb).
+- Simple text semantic classification: [examples/prompt-tuning-sst2.ipynb](./examples/prompt-tuning-sst2.ipynb)
+- A personified chatbot: [examples/prompt-tuning-personachat.ipynb](./examples/prompt-tuning-personachat.ipynb)
 
 Here's a [more advanced tutorial](https://github.com/bigscience-workshop/petals/wiki/Launch-your-own-swarm) that covers 8-bit quantization and best practices for running Petals.