Update README.md (#579)

According to new docs, which I also linked from the sidebar for increased discoverability.

README.md

@@ -13,10 +13,9 @@
 
 See Trusses for popular models including:
 
-* 🦅 [Falcon 40B](https://github.com/basetenlabs/falcon-40b-truss)
-* 🧙 [WizardLM](https://github.com/basetenlabs/wizardlm-truss)
-* 🎨 [Stable Diffusion](https://github.com/basetenlabs/stable-diffusion-truss)
-* 🗣 [Whisper](https://github.com/basetenlabs/whisper-truss)
+* 🦙 [Llama 2 7B](https://github.com/basetenlabs/truss-examples/tree/main/llama-2-7b-chat) ([13B](https://github.com/basetenlabs/truss-examples/tree/main/llama-2-13b-chat)) ([70B](https://github.com/basetenlabs/truss-examples/tree/main/llama-2-70b-chat))
+* 🎨 [Stable Diffusion XL](https://github.com/basetenlabs/truss-examples/tree/main/stable-diffusion-xl-1.0)
+* 🗣 [Whisper](https://github.com/basetenlabs/truss-examples/tree/main/whisper-truss)
 
 and [dozens more examples](examples/).
 
@@ -36,61 +35,100 @@ As a quick example, we'll package a [text classification pipeline](https://huggi
 
 To get started, create a Truss with the following terminal command:
 
-```
+```sh
 truss init text-classification
 ```
 
-This will create an empty Truss at `./text-classification`.
+When prompted, give your Truss a name like `Text classification`.
+
+Then, navigate to the newly created directory:
+
+```sh
+cd text-classification
+```
 
 ### Implement the model
 
-The model serving code goes in `./text-classification/model/model.py` in your newly created Truss.
+One of the two essential files in a Truss is `model/model.py`. In this file, you write a `Model` class: an interface between the ML model that you're packaging and the model server that you're running it on.
+
+There are two member functions that you must implement in the `Model` class:
+
+* `load()` loads the model onto the model server. It runs exactly once when the model server is spun up or patched.
+* `predict()` handles model inference. It runs every time the model server is called.
+
+Here's the complete `model/model.py` for the text classification model:
 
 ```python
-from typing import List
 from transformers import pipeline
 
 
 class Model:
-    def __init__(self, **kwargs) -> None:
+    def __init__(self, **kwargs):
         self._model = None
 
     def load(self):
         self._model = pipeline("text-classification")
 
-    def predict(self, model_input: str) -> List:
+    def predict(self, model_input):
         return self._model(model_input)
 ```
 
-There are two functions to implement:
-
-* `load()` runs once when the model is spun up and is responsible for initializing `self._model`
-* `predict()` runs each time the model is invoked and handles the inference. It can use any JSON-serializable type as input and output.
-
 ### Add model dependencies
 
-The pipeline model relies on Transformers and PyTorch. These dependencies must be specified in the Truss config.
+The other essential file in a Truss is `config.yaml`, which configures the model serving environment. For a complete list of the config options, see [the config reference](https://truss.baseten.co/reference/config).
 
-In `./text-classification/config.yaml`, find the line `requirements`. Replace the empty list with:
+The pipeline model relies on [Transformers](https://huggingface.co/docs/transformers/index) and [PyTorch](https://pytorch.org/). These dependencies must be specified in the Truss config.
+
+In `config.yaml`, find the line `requirements`. Replace the empty list with:
 
 ```yaml
 requirements:
   - torch==2.0.1
   - transformers==4.30.0
 ```
 
-No other configuration needs to be changed.
+No other configuration is needed.
 
 ## Deployment
 
-You can deploy a Truss to your [Baseten](https://baseten.co) account with:
+Truss is maintained by [Baseten](https://baseten.co), which provides infrastructure for running ML models in production. We'll use Baseten as the remote host for your model.
 
-```
-cd ./text-classification
+Other remotes are coming soon, starting with AWS SageMaker.
+
+### Get an API key
+
+To set up the Baseten remote, you'll need a [Baseten API key](https://app.baseten.co/settings/account/api_keys). If you don't have a Baseten account, no worries, just [sign up for an account](https://app.baseten.co/signup/) and you'll be issued plenty of free credits to get you started.
+
+### Run `truss push`
+
+With your Baseten API key ready to paste when prompted, you can deploy your model:
+
+```sh
 truss push
 ```
 
-Truss will support other remotes soon, starting with AWS SageMaker.
+You can monitor your model deployment from [your model dashboard on Baseten](https://app.baseten.co/models/).
+
+### Invoke the model
+
+After the model has finished deploying, you can invoke it from the terminal.
+
+**Invocation**
+
+```sh
+truss predict -d '"Truss is awesome!"'
+```
+
+**Response**
+
+```json
+[
+  {
+    "label": "POSITIVE",
+    "score": 0.999873161315918
+  }
+]
+```
 
 ## Truss contributors