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Reformat Getting started page on docs site (#6)
Signed-off-by: Kelly Brown <kelbrown@redhat.com>
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‎docs/getting-started/creating_new_knowledge_or_skills.md‎ renamed to ‎docs/adding-data-to-model/creating_new_knowledge_or_skills.md‎

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‎docs/getting-started/creating_new_wikipedia_based_qna.md‎ renamed to ‎docs/adding-data-to-model/creating_new_wikipedia_based_qna.md‎

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‎docs/getting-started/download_models.md‎

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---
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title: Downloading LLMs
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description: Steps on how to download models using `ilab`
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logo: images/ilab_dog.png
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---
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# 📥 Download the model
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- Run the `ilab model download` command.
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```shell
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ilab model download
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```
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`ilab model download` downloads a compact pre-trained version of the [model](https://huggingface.co/instructlab/) (~4.4G) from HuggingFace:
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```shell
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(venv) $ ilab model download
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Downloading model from Hugging Face: instructlab/merlinite-7b-lab-GGUF@main to /Users/USERNAME/Library/Caches/instructlab/models...
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...
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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
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```
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!!! note
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⏳ 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.
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## Downloading an entire Hugging Face repository (Safetensors Model)
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- Specify repository, and a Hugging Face token if necessary. For example:
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```shell
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HF_TOKEN=<YOUR HUGGINGFACE TOKEN GOES HERE> ilab model download --repository=instructlab/granite-7b-lab
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```
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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.
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## Listing downloaded models
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All downloaded models can be seen with the `ilab model list` command.
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```shell
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ilab model list
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```
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*Example output of `ilab model list` after `ilab model download`*
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```shell
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(venv) $ ilab model list
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+------------------------------+---------------------+--------+
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| Model Name | Last Modified | Size |
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+------------------------------+---------------------+--------+
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| merlinite-7b-lab-Q4_K_M.gguf | 2024-08-01 15:05:48 | 4.1 GB |
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+------------------------------+---------------------+--------+
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```

‎docs/getting-started/initilize_ilab.md‎

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---
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title: Initializing InstructLab
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description: Steps to initilize InsutrctLab on a local machine
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logo: images/ilab_dog.png
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---
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# 🏗️ Initialize `ilab`
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1) Initialize `ilab` by running the following command:
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```shell
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ilab config init
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```
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*Example output*
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```shell
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Welcome to InstructLab CLI. This guide will help you set up your environment.
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Please provide the following values to initiate the environment [press Enter for defaults]:
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Path to taxonomy repo [taxonomy]: <ENTER>
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```
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2) When prompted by the interface, press **Enter** to add a new default `config.yaml` file.
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3) When prompted, clone the `https://github.com/instructlab/taxonomy.git` repository into the current directory by typing **y**.
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**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.
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*Example output after initializing `ilab`*
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```shell
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(venv) $ ilab config init
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Welcome to InstructLab CLI. This guide will help you set up your environment.
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Please provide the following values to initiate the environment [press Enter for defaults]:
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Path to taxonomy repo [taxonomy]: <ENTER>
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`taxonomy` seems to not exists or is empty. Should I clone https://github.com/instructlab/taxonomy.git for you? [y/N]: y
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Cloning https://github.com/instructlab/taxonomy.git...
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```
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`ilab` will use the default configuration file unless otherwise specified. You can override this behavior with the `--config` parameter for any `ilab` command.
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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.
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```shell
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(venv) $ ilab config init
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Welcome to InstructLab CLI. This guide will help you set up your environment.
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Please provide the following values to initiate the environment [press Enter for defaults]:
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Path to taxonomy repo [taxonomy]: <ENTER>
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`taxonomy` seems to not exists or is empty. Should I clone https://github.com/instructlab/taxonomy.git for you? [y/N]: y
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Cloning https://github.com/instructlab/taxonomy.git...
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Path to your model [/Users/USERNAME/Library/Caches/instructlab/models/merlinite-7b-lab-Q4_K_M.gguf]: <ENTER>
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```
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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.
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```shell
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Welcome to InstructLab CLI. This guide will help you to setup your environment.
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Please provide the following values to initiate the environment [press Enter for defaults]:
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Path to taxonomy repo [/home/user/.local/share/instructlab/taxonomy]:
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Path to your model [/home/user/.cache/instructlab/models/merlinite-7b-lab-Q4_K_M.gguf]:
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Generating `/home/user/.config/instructlab/config.yaml` and `/home/user/.local/share/instructlab/internal/train_configuration/profiles`...
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Please choose a train profile to use.
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Train profiles assist with the complexity of configuring specific GPU hardware with the InstructLab Training library.
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You can still take advantage of hardware acceleration for training even if your hardware is not listed.
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[0] No profile (CPU, Apple Metal, AMD ROCm)
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[1] Nvidia A100/H100 x2 (A100_H100_x2.yaml)
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[2] Nvidia A100/H100 x4 (A100_H100_x4.yaml)
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[3] Nvidia A100/H100 x8 (A100_H100_x8.yaml)
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[4] Nvidia L40 x4 (L40_x4.yaml)
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[5] Nvidia L40 x8 (L40_x8.yaml)
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[6] Nvidia L4 x8 (L4_x8.yaml)
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Enter the number of your choice [hit enter for no profile] [0]:
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No profile selected - any hardware acceleration for training must be configured manually.
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Initialization completed successfully, you're ready to start using `ilab`. Enjoy!
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```
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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!
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## `ilab` directory layout after initializing your system
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### Mac directory
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After running `ilab config init` your directories will look like the following on a Mac system:
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```shell
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├─ ~/Library/Application\ Support/instructlab/models/ (1)
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├─ ~/Library/Application\ Support/instructlab/datasets (2)
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├─ ~/Library/Application\ Support/instructlab/taxonomy (3)
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├─ ~/Library/Application\ Support/instructlab/checkpoints (4)
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```
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1) `/Users/USERNAME/Library/Caches/instructlab/models/`: Contains all downloaded large language models, including the saved output of ones you generate with ilab.
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2) `~/Library/Application\ Support/instructlab/datasets/`: Contains data output from the SDG phase, built on modifications to the taxonomy repository.
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3) `~/Library/Application\ Support/instructlab/taxonomy/`: Contains the skill and knowledge data.
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4) `~/Users/USERNAME/Library/Caches/instructlab/checkpoints/`: Contains the output of the training process
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### Linux directory
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After running `ilab config init` your directories will look like the following on a Linux system:
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```shell
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├─ ~/.cache/instructlab/models/ (1)
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├─ ~/.local/share/instructlab/datasets (2)
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├─ ~/.local/share/instructlab/taxonomy (3)
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├─ ~/.local/share/instructlab/checkpoints (4)
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```
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1) `~/.cache/instructlab/models/`: Contains all downloaded large language models, including the saved output of ones you generate with ilab.
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2) `~/.local/share/instructlab/datasets/`: Contains data output from the SDG phase, built on modifications to the taxonomy repository.
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3) `~/.local/share/instructlab/taxonomy/`: Contains the skill and knowledge data.
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4) `~/.local/share/instructlab/checkpoints/`: Contains the output of the training process

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