Reformat Getting started page on docs site (#6)

Signed-off-by: Kelly Brown <[email protected]>

docs/getting-started/download_models.md

@@ -0,0 +1,54 @@
+---
+title: Downloading LLMs
+description: Steps on how to download models using `ilab`
+logo: images/ilab_dog.png
+---
+
+# 📥 Download the model
+
+- Run the `ilab model download` command.
+
+```shell
+ilab model download
+```
+
+`ilab model download` downloads a compact pre-trained version of the [model](https://huggingface.co/instructlab/) (~4.4G) from HuggingFace:
+
+```shell
+(venv) $ ilab model download
+Downloading model from Hugging Face: instructlab/merlinite-7b-lab-GGUF@main to /Users/USERNAME/Library/Caches/instructlab/models...
+...
+INFO 2024-08-01 15:05:48,464 huggingface_hub.file_download:1893: Download complete. Moving file to /Users/USERNAME/Library/Caches/instructlab/models/merlinite-7b-lab-Q4_K_M.gguf
+```
+
+!!! note
+    ⏳ This command can take few minutes or immediately depending on your internet connection or model is cached. If you have issues connecting to Hugging Face, refer to the [Hugging Face discussion forum](https://discuss.huggingface.co/) for more details.
+
+## Downloading an entire Hugging Face repository (Safetensors Model)
+
+- Specify repository, and a Hugging Face token if necessary. For example:
+
+```shell
+HF_TOKEN=<YOUR HUGGINGFACE TOKEN GOES HERE> ilab model download --repository=instructlab/granite-7b-lab
+```
+
+These types of models are useful for GPU-enabled systems or anyone looking to serve a model using vLLM. InstructLab provides Safetensor versions of our Granite models on HuggingFace.
+
+## Listing downloaded models
+
+All downloaded models can be seen with the `ilab model list` command.
+
+```shell
+ilab model list
+```
+
+*Example output of `ilab model list` after `ilab model download`*
+
+```shell
+(venv) $ ilab model list
++------------------------------+---------------------+--------+
+| Model Name                   | Last Modified       | Size   |
++------------------------------+---------------------+--------+
+| merlinite-7b-lab-Q4_K_M.gguf | 2024-08-01 15:05:48 | 4.1 GB |
++------------------------------+---------------------+--------+
+```

docs/getting-started/initilize_ilab.md

@@ -0,0 +1,110 @@
+---
+title: Initializing InstructLab
+description: Steps to initilize InsutrctLab on a local machine
+logo: images/ilab_dog.png
+---
+
+# 🏗️ Initialize `ilab`
+
+1) Initialize `ilab` by running the following command:
+
+```shell
+ilab config init
+```
+
+*Example output*
+
+```shell
+Welcome to InstructLab CLI. This guide will help you set up your environment.
+Please provide the following values to initiate the environment [press Enter for defaults]:
+Path to taxonomy repo [taxonomy]: <ENTER>
+```
+
+2) When prompted by the interface, press **Enter** to add a new default `config.yaml` file.
+
+3) When prompted, clone the `https://github.com/instructlab/taxonomy.git` repository into the current directory by typing **y**.
+
+   **Optional**: If you want to point to an existing local clone of the `taxonomy` repository, you can pass the path interactively or alternatively with the `--taxonomy-path` flag.
+
+   *Example output after initializing `ilab`*
+
+   ```shell
+   (venv) $ ilab config init
+   Welcome to InstructLab CLI. This guide will help you set up your environment.
+   Please provide the following values to initiate the environment [press Enter for defaults]:
+   Path to taxonomy repo [taxonomy]: <ENTER>
+   `taxonomy` seems to not exists or is empty. Should I clone https://github.com/instructlab/taxonomy.git for you? [y/N]: y
+   Cloning https://github.com/instructlab/taxonomy.git...
+   ```
+
+   `ilab` will use the default configuration file unless otherwise specified. You can override this behavior with the `--config` parameter for any `ilab` command.
+
+4) When prompted, provide the path to your default model. Otherwise, the default of a quantized [Merlinite](https://huggingface.co/instructlab/merlinite-7b-lab-GGUF) model will be used - you can download this model with `ilab model download`. The following example output displays the paths of a Mac instance. 
+
+   ```shell
+   (venv) $ ilab config init
+   Welcome to InstructLab CLI. This guide will help you set up your environment.
+   Please provide the following values to initiate the environment [press Enter for defaults]:
+   Path to taxonomy repo [taxonomy]: <ENTER>
+   `taxonomy` seems to not exists or is empty. Should I clone https://github.com/instructlab/taxonomy.git for you? [y/N]: y
+   Cloning https://github.com/instructlab/taxonomy.git...
+   Path to your model [/Users/USERNAME/Library/Caches/instructlab/models/merlinite-7b-lab-Q4_K_M.gguf]: <ENTER>
+   ```
+
+5) When prompted, please choose a train profile. Train profiles are GPU specific profiles that enable accelerated training behavior. **YOU ARE ON MacOS**, please choose `No Profile (CPU, Apple Metal, AMD ROCm)` by hitting Enter. There are various flags you can utilize with individual `ilab` commands that will allow you to utilize your GPU if applicable. The following example output uses the Linux paths. 
+
+   ```shell
+    Welcome to InstructLab CLI. This guide will help you to setup your environment.
+    Please provide the following values to initiate the environment [press Enter for defaults]:
+    Path to taxonomy repo [/home/user/.local/share/instructlab/taxonomy]:
+    Path to your model [/home/user/.cache/instructlab/models/merlinite-7b-lab-Q4_K_M.gguf]:
+    Generating `/home/user/.config/instructlab/config.yaml` and `/home/user/.local/share/instructlab/internal/train_configuration/profiles`...
+    Please choose a train profile to use.
+    Train profiles assist with the complexity of configuring specific GPU hardware with the InstructLab Training library.
+    You can still take advantage of hardware acceleration for training even if your hardware is not listed.
+    [0] No profile (CPU, Apple Metal, AMD ROCm)
+    [1] Nvidia A100/H100 x2 (A100_H100_x2.yaml)
+    [2] Nvidia A100/H100 x4 (A100_H100_x4.yaml)
+    [3] Nvidia A100/H100 x8 (A100_H100_x8.yaml)
+    [4] Nvidia L40 x4 (L40_x4.yaml)
+    [5] Nvidia L40 x8 (L40_x8.yaml)
+    [6] Nvidia L4 x8 (L4_x8.yaml)
+    Enter the number of your choice [hit enter for no profile] [0]:
+    No profile selected - any hardware acceleration for training must be configured manually.
+    Initialization completed successfully, you're ready to start using `ilab`. Enjoy!
+   ```
+
+   The GPU profiles are listed by GPU type and number. If you happen to have a GPU configuration with a similar amount of VRAM as any of the above profiles, feel free to try them out!
+
+## `ilab` directory layout after initializing your system
+### Mac directory 
+
+After running `ilab config init` your directories will look like the following on a Mac system:
+
+```shell
+├─ ~/Library/Application\ Support/instructlab/models/ (1)
+├─ ~/Library/Application\ Support/instructlab/datasets (2)
+├─ ~/Library/Application\ Support/instructlab/taxonomy (3)
+├─ ~/Library/Application\ Support/instructlab/checkpoints (4)
+```
+
+ 1) `/Users/USERNAME/Library/Caches/instructlab/models/`: Contains all downloaded large language models, including the saved output of ones you generate with ilab.
+ 2) `~/Library/Application\ Support/instructlab/datasets/`: Contains data output from the SDG phase, built on modifications to the taxonomy repository.
+ 3) `~/Library/Application\ Support/instructlab/taxonomy/`: Contains the skill and knowledge data.
+ 4) `~/Users/USERNAME/Library/Caches/instructlab/checkpoints/`: Contains the output of the training process
+
+ ### Linux directory
+
+After running `ilab config init` your directories will look like the following on a Linux system:
+
+```shell
+├─ ~/.cache/instructlab/models/ (1)
+├─ ~/.local/share/instructlab/datasets (2)
+├─ ~/.local/share/instructlab/taxonomy (3)
+├─ ~/.local/share/instructlab/checkpoints (4)
+```
+
+1) `~/.cache/instructlab/models/`: Contains all downloaded large language models, including the saved output of ones you generate with ilab.
+2) `~/.local/share/instructlab/datasets/`: Contains data output from the SDG phase, built on modifications to the taxonomy repository.
+3) `~/.local/share/instructlab/taxonomy/`: Contains the skill and knowledge data.
+4) `~/.local/share/instructlab/checkpoints/`: Contains the output of the training process