[Docs] Bootstrap Getting Started page

- As preparation for the first Alpha release, we should create a getting started page that will help our users navigate the first N steps to run our models.
- Next steps should include:
1. Populating details for wheel installation
2. Link to the list of supported models (require outlined release process and dedicated `.md` section)
3. More detailed next steps (when applicable)

Fix #1114

docs/src/SUMMARY.md

@@ -2,6 +2,7 @@
 
 # Introduction
 - [Introduction](./introduction.md)
+- [Getting Started](./getting-started.md)
 - [Architecture Overview](./architecture_overview.md)
 
 # Project setup
@@ -13,6 +14,3 @@
 # Dev Notes
 - [Running standalone FFE generated TTIRs](./dev_notes/standalone_ttir_run.md)
 - [Verification in tests](./dev_notes/verification.md)
-
-# User Guide
-- [Getting Started](./getting-started.md)

docs/src/getting-started.md

@@ -0,0 +1,58 @@
+# Getting Started
+
+## Setup
+You choose between two ways to setup our project:
+- Install pre-built wheel
+- Building from source
+
+### Install using Wheel
+
+*Wheel installation instructions will be provided soon. Stay tuned!*
+
+### Build from Source
+
+To build Forge-FE from source, you need to clone the project from our GitHub page:
+```bash
+git clone https://github.com/tenstorrent/tt-forge-fe.git
+```
+
+Afterwards, you can follow our [build instructions](https://docs.tenstorrent.com/tt-forge-fe/build.html) which outline prerequisites, as well as how to build dependencies and our project.
+
+## Run First Example Case
+
+To confirm that our environment is properly setup, let's run one sanity test for element-wise add operation:
+```bash
+pytest forge/test/mlir/operators/eltwise_binary/test_eltwise_binary.py::test_add
+```
+
+In a few seconds, you should get confirmation if this test passed successfully. Once that's done, we can run one of our model tests as well:
+```bash
+pytest forge/test/mlir/llama/tests/test_llama_prefil.py::test_llama_prefil_on_device_decode_on_cpu
+```
+
+## Where to Go Next
+
+Now that you have set up Forge-FE, you can try to compile and run your own models!
+
+For a quick start, here is an example of how to run your own model. Note the introduction of the `forge.compile` call:
+
+```py
+import torch
+from transformers import ResNetForImageClassification
+
+def resnet():
+    # Load image, pre-process, etc.
+    ...
+
+    # Load model (e.g. from HuggingFace)
+    framework_model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
+
+    # Compile the model using Forge
+    compiled_model = forge.compile(framework_model, input_image)
+
+    # Run compiled model
+    logits = compiled_model(input_image)
+
+    ...
+    # Post-process output, return results, etc.
+```