update badges

README.md

@@ -6,27 +6,27 @@
 [![PyPI Downloads](https://pepy.tech/badge/lightning-hivemind)](https://pepy.tech/project/lightning-hivemind)
 [![Docs](https://github.com/Lightning-AI/lightning-Hivemind/actions/workflows/docs-deploy.yml/badge.svg?event=push)](https://lightning-ai.github.io/lightning-Hivemind/)
 
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+[![General checks](https://github.com/Lightning-Universe/lightning-Hivemind/actions/workflows/ci-checks.yml/badge.svg?event=push)](https://github.com/Lightning-Universe/lightning-Hivemind/actions/workflows/ci-checks.yml)
+[![CI testing](https://github.com/Lightning-Universe/lightning-Hivemind/actions/workflows/ci-testing.yml/badge.svg?event=push)](https://github.com/Lightning-Universe/lightning-Hivemind/actions/workflows/ci-testing.yml)
+[![Build Status](https://dev.azure.com/Lightning-AI/compatibility/_apis/build/status%2Fstrategies%2FLightning-Universe.lightning-Hivemind?branchName=main)](https://dev.azure.com/Lightning-AI/compatibility/_build/latest?definitionId=64&branchName=main)
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 Collaborative Training tries to solve the need for top-tier multi-GPU servers by allowing you to train across unreliable machines,
-such as local machines or even preemptible cloud compute across the internet.
+such as local machines or even preemptible cloud computing across the internet.
 
-Under the hood, we use [Hivemind](https://github.com/learning-at-home/hivemind) which provides de-centralized training across the internet.
+Under the hood, we use [Hivemind](https://github.com/learning-at-home/hivemind), which provides de-centralized training across the internet.
 
-To use Collaborative Training, you need to first this extension.
+To use Collaborative Training, you need first to have this extension.
 
 ```bash
 pip install -U lightning-Hivemind
 ```
 
-The `HivemindStrategy` accumulates gradients from all processes that are collaborating until they reach a `target_batch_size`. By default, we use the batch size
+The `HivemindStrategy` accumulates gradients from all collaborating processes until they reach a `target_batch_size`. By default, we use the batch size
 of the first batch to determine what each local machine batch contributes towards the `target_batch_size`. Once the `target_batch_size` is reached, an optimizer step
 is made on all processes.
 
-When using `HivemindStrategy` note that you cannot use gradient accumulation (`accumulate_grad_batches`). This is because Hivemind manages accumulation internally.
+When using `HivemindStrategy`, note that you cannot use gradient accumulation (`accumulate_grad_batches`). This is because Hivemind manages accumulation internally.
 
 ```py
 from lightning import Trainer
@@ -39,10 +39,10 @@ Followed by:
 
 ```bash
 python train.py
-# Other machines can connect running the same command:
+# Other machines can connect by running the same command:
 # INITIAL_PEERS=... python train.py
 # or passing the peers to the strategy:"
 # HivemindStrategy(initial_peers=...)"
 ```
 
-A helper message is printed once your training begins, which shows you how to start training on other machines using the same code.
+A helper message is printed once your training begins, showing you how to train on other machines using the same code.