README Fixing

copilot/README.md

@@ -32,7 +32,7 @@ copilot deploy
 ```
 
 This will deploy the services but it won't be 100% ready for usage. Before being
-ready, we have to inspect the AWS Secrets manager and extract out the database
+ready, we have to inspect the AWS Secrets Manager and extract out the database
 credentials. Read those credentials then put them, and a few other secrets, in a
 `secrets.yml` file like the following:
 

docs/docs/architecture/README.md

@@ -4,6 +4,6 @@
 
 The Inference architecture is comprised of several core components: a text, or
 frontend client, a FastAPI webserver, a database with several tables, Reddis
-used for queueing, and distributed gpu workers.
+used for queueing, and distributed GPU workers.
 
 A more detailed overview can be viewed [here](inference.md).

docs/docs/plugins/README.md

@@ -6,7 +6,7 @@
 
 :::note
 
-In the GitHub repo You can see all issues and PR's with the
+In the GitHub repo, you can see all issues and PR's with the
 [`plugins`](https://github.com/LAION-AI/Open-Assistant/issues?q=label%3Aplugins)
 label if you want to dive deeper.
 

inference/server/README.md

@@ -3,7 +3,7 @@
 Workers communicate with the `/work` endpoint via Websocket. They provide their
 configuration and if a task is available, the server returns it. The worker then
 performs the task and returns the result in a streaming fashion to the server,
-also via websocket.
+also via Websocket.
 
 Clients first call `/chat` to make a new chat, then add to that via
 `/chat/<id>/message`. The response is a SSE event source, which will send tokens

inference/worker/README.md

@@ -2,8 +2,8 @@
 
 ## Running the worker
 
-To run the worker, you need to have docker installed, including the docker
-nvidia runtime if you want to use a GPU. We made a convenience-script you can
+To run the worker, you need to have Docker installed, including the Docker
+NVIDIA runtime if you want to use a GPU. We made a convenience-script you can
 download and run to start the worker:
 
 ```bash

notebooks/closed-book-qa/README.md

@@ -38,7 +38,7 @@ The output of this is a dictionary with the following information:
 
 ## Requirements
 
-This code is verified to work on a 24GB vram graphics card (like an RTX3090). We
+This code is verified to work on a 24GB VRAM graphics card (like an RTX3090). We
 are working on getting it to run on Google Colab TPUs, and also it may be
 possible to use smaller T5 models like the 3 billion parameter model and still
 get acceptable results.

notebooks/data-augmentation/essay-instructions/README.md

@@ -2,10 +2,10 @@
 
 Essay Instructions is a notebook that takes an essay as an input and generates
 instructions on how to generate that essay. This will be very useful for data
-collecting for the model
+collecting for the model.
 
 ## Contributing
 
 Feel free to contribute to this notebook, it's nowhere near perfect but it's a
 good start. If you want to contribute finding a new model that better suits this
-task would be great. Huggingface has a lot of models that could help.
+task would be great. HuggingFace has a lot of models that could help.

notebooks/detoxify-evaluation/README.md

@@ -1,7 +1,7 @@
 # Detoxify evaluation
 
 [Detoxify](https://github.com/unitaryai/detoxify) is a open source model used to
-identify prompts as toxic
+identify prompts as toxic.
 
 <img  src="https://raw.githubusercontent.com/unitaryai/detoxify/master/examples.png"  alt="Image from detoxify github that shows the example input/output of their model"  />
 
@@ -16,15 +16,15 @@ trained on
 
 Unbiased and original models also have a 'small' version - but since normal
 models are not memory heavy, and small models perform noticeably worse, they are
-only described in the notebook
+only described in the notebook.
 
 ## All tests below were ran on a 3090TI
 
 # Inference and training times and memory usages
 
 Charts showing detailed memory usages and times for different sentence lengths
 and batch sizes are inside the notebook Quick overview batch size 16, sentence
-length 4k for training, batch size 128 sentence length 4k for Inference
+length 4k for training, batch size 128 sentence length 4k for Inference.
 
 |  Model name  | Training memory | Training speed | Inference Memory | Inference Speed |
 | :----------: | :-------------: | :------------: | :--------------: | :-------------: |
@@ -34,7 +34,7 @@ length 4k for training, batch size 128 sentence length 4k for Inference
 
 # Filtering quality
 
-Detoxify was tested on 4 different types of inputs
+Detoxify was tested on 4 different types of inputs:
 
 - Not obviously toxic
 - Not obviously non-toxic
@@ -57,7 +57,7 @@ toxicity if it's presented in formal language.
 
 With some caution it can be used to filter prompts but I would suggest also
 using someone for verification of messages that are marked as toxic but still
-below 90% confidence
+below 90% confidence.
 
 # Licensing
 

website/cypress/README.md

@@ -1,6 +1,6 @@
 # Component and e2e testing with Cypress
 
-[Cypress](https://www.cypress.io/) is used for both component- and end-to-end testing. Below there's a few examples for
+[Cypress](https://www.cypress.io/) is used for both component and end-to-end testing. Below there's a few examples for
 the context of this site. To learn more, the
 [Cypress documentation](https://docs.cypress.io/guides/getting-started/opening-the-app) has it all.