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
 
@@ -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.