Oct 9th updates, 0.19 updates.

Updates :)

Signed-off-by: JJ Asghar <[email protected]>

Author: jjasghar

docs/README.md

@@ -15,14 +15,13 @@ locally to help it gain some knowledge.
 * How to use an AI model that is built in a verifiable and legal way
 * Learn a suggested workflow on how to fine tune a model with new knowledge and skills!
 
-### About this workshop
+## About this workshop
 
 The introductory page of the workshop is broken down into the following sections:
 
-* [Agenda](./#agenda)
-* [Compatibility](./#compatibility)
-* [Technology Used](./#technology-used)
-* [Credits](./#credits)
+* [Agenda](#agenda)
+* [Compatibility](#compatibility)
+* [Credits](#credits)
 
 ## Agenda
 
@@ -39,7 +38,6 @@ The introductory page of the workshop is broken down into the following sections
 This workshop has been tested on the following platforms:
 
 * **MacOS**: version 14.5
-* **Windows**: version 11
 * **Linux**: Fedora 40
 
 ## Credits

docs/lab-1/README.md

@@ -5,7 +5,7 @@
 Success! We're ready to start with the first steps on your AI journey with us today.
 With this first lab, we'll be working through the steps in this [blogpost using Granite as a code assistant](https://developer.ibm.com/tutorials/awb-local-ai-copilot-ibm-granite-code-ollama-continue/).
 
-In this tutorial, I will show how to use a collection of open-source components to run a feature-rich developer code assistant in Visual Studio Code while addressing data privacy, licensing, and cost challenges that are common to enterprise users. The setup is powered by local large language models (LLMs) with IBM's open-source LLM family, [Granite Code](https://github.com/ibm-granite/granite-code-models). All components run on a developer's workstation and have business-friendly licensing.
+In this tutorial, we will show how to use a collection of open-source components to run a feature-rich developer code assistant in Visual Studio Code while addressing data privacy, licensing, and cost challenges that are common to enterprise users. The setup is powered by local large language models (LLMs) with IBM's open-source LLM family, [Granite Code](https://github.com/ibm-granite/granite-code-models). All components run on a developer's workstation and have business-friendly licensing.
 
 There are three main barriers to adopting these tools in an enterprise setting:
 
@@ -17,13 +17,14 @@ There are three main barriers to adopting these tools in an enterprise setting:
 
 Why did we select Granite as the LLM of choice for this exercise?
 
-Granite Code was produced by IBM Research, with the goal of building an LLM that had only seen code which used enterprise-friendly licenses. According to section 2 of the Granite Code paper ([Granite Code Models: A Family of Open Foundation Models for Code Intelligence][paper]),the IBM Granite Code models meticulously curated their training data for licenses, and to make sure that all text did not contain any hate, abuse, or profanity.
+Granite Code was produced by IBM Research, with the goal of building an LLM that had only seen code which used enterprise-friendly licenses. According to section 2 of the Granite Code paper ([Granite Code Models: A Family of Open Foundation Models for Code Intelligence][paper]), the IBM Granite Code models meticulously curated their training data for licenses, and to make sure that all text did not contain any hate, abuse, or profanity.
 
 Many open LLMs available today license the model itself for derivative work, but because they bring in large amounts of training data without discriminating by license, most companies can't use the output of those models since it potentially presents intellectual property concerns. Granite
 
-Granite Code comes in a wide range of sizes to fit your workstation's available resources. Generally, the bigger the model, the better the results, with a tradeoff: model responses will be slower, and it will take up more resources on your machine. I chose the 20b option as my starting point for chat and the 8b option for code generation. Ollama offers a convenient pull feature to download models:
+Granite Code comes in a wide range of sizes to fit your workstation's available resources. Generally, the bigger the model, the better the results, with a tradeoff: model responses will be slower, and it will take up more resources on your machine. We chose the 20b option as my starting point for chat and the 8b option for code generation. Ollama offers a convenient pull feature to download models:
 
 Open up your terminal, and run the following commands:
+
 ```bash
 ollama pull granite-code:20b
 ollama pull granite-code:8b

docs/lab-2/README.md

@@ -5,15 +5,17 @@ Lets play with our new found local AI Open Source AI!
 ## Sanity checks
 
 When you open up `continue` inside of VSCode it should look something like:
-![](https://docs.continue.dev/assets/images/understand-ca0edc3d06922dd4a95e31fa06f999ec.gif)
+![](https://docs.continue.dev/assets/images/move-to-right-sidebar-b2d315296198e41046fc174d8178f30a.gif)
 
 Before we go any farther, write in "Who is batman?" to verify that `ollama`,
 VSCode, and `continue` are all working correctly.
 
 !!! troubleshooting
     If Continue is taking a long time to respond, restart Visual Studio Code. If that doesn't resolve your issue, restart Ollama.
 
-If you would like to go deeper with continue, take a look at the [official Continue.dev how-to guide]( https://docs.continue.dev/how-to-use-continue).
+If you would like to go deeper with `continue`, take a look at the [official Continue.dev how-to guide](https://docs.continue.dev/how-to-use-continue).
+Its worth taken the moment if you want, otherwise, when you get home and try this on your own
+hardware, it's awesome to see what `continue` can do.
 
 Now that we have our local AI co-pilot with us, let's start using it. Now these
 next examples are going to be focused on `python` but there is nothing stopping
@@ -39,6 +41,10 @@ Now use the `command-i` or `ctrl-i` to open up the `generate code` command palet
 write me out conways game of life using pygame
 ```
 
+!!! note
+    If you don't know what Conway's Game of Life is, take a look [here](https://en.wikipedia.org/wiki/Conway's_Game_of_Life) or
+    raise your hand, I'm betting the TA's would love to talk to you about it. 😁
+
 Now granite-code should start giving you a good suggestion here, it should look something like:
 ![gameoflife_v1](../images/gameoflife_v1.png)
 
@@ -59,17 +65,22 @@ Don't believe me? Bring up the terminal and attempt to run this code after you a
 ![nope doesn't do anything](../images/nowork.png)
 
 Well that isn't good is it? Yours may be different code, or maybe it does work, but at least in this
-example we need to to get it fixed.
+example we need to to get the code fixed.
 
 ## First pass at debugging
 
 I'll run the following commands to build up an virtual environment, and install some modules, lets
 see how far we get.
 
+!!! tip
+    If these next commands are foreign to you, it's ok. These are `python` commands, and you can just
+    copy paste it in. If you'd like to know more or _why_ it is, raise your hand a TA should be able
+    to explain it to you.
+
 ```bash
 python3.11 -m venv venv
 source venv/bin/activate
-workshop-python-ai pip install pygame
+pip install pygame
 ```
 
 Well better, I think, but nothing still happens. So even noticing the `import pygame` tells me I need to
@@ -79,7 +90,10 @@ so it's more readable.
 ## Cleaning up the AI generated code
 
 !!! note
-    You can try using the built-in autocomplete and code assistant functions to generate any missing code. In our example, we're missing a "main" entry point to the script. Try hitting `cmd/ctrl + I` again, and typing in something like: "write a main function for my game that plays twenty rounds of Conway's game of life using the `board()` function". What happens?
+    You can try using the built-in autocomplete and code assistant functions to generate any missing code.
+    In our example, we're missing a "main" entry point to the script. Try hitting `cmd/ctrl + I` again,
+    and typing in something like: "write a main function for my game that plays twenty rounds of Conway's
+    game of life using the `board()` function." What happens?
 
 Cleaning up the code. Now everything is smashed together, it's hard to read the logic here, so first
 thing first, going to break up the code and add a `def main` function so I know what the entry point is.
@@ -126,7 +140,7 @@ function so I get a better understanding of what the program is doing.
 Go ahead and walk through your version, see the logic, and who knows maybe it'll highlight why yours
 isn't working yet, or not, the next step will help you even more!
 
-## Automagicly creating tests!
+## Automagicly creating tests
 
 One of the most powerful/helping stream line your workflow as a developer is writing good tests
 around your code. It's a safety blanket to help make sure your custom code has a way to check for

docs/lab-3/README.md

@@ -6,6 +6,11 @@ logo: images/ilab_dog.png
 
 # Getting Started with InstructLab
 
+!!! tip
+    We are jumping in the deep end here, don't hesitate to raise your hand and ask questions.
+    We also assume you have a good _foundational_ (heh) knowledege of `python` if not, ask
+    one of the TA's to run through this on the overhead screen.
+
 Now that we have a working VSCode instance working with the granite model, lets talk about more
 things you can do with an Open Source AI system. This is where the [InstructLab](https://instructlab.ai/)
 project comes into play.
@@ -25,12 +30,12 @@ it to have.
     These steps will pull down a premade `qna.yaml` so you can do a local build. Skip the `wget`, `mv`, and `ilab taxonomy diff` if you don't want to do this.
 
 ```bash
-python3.11 -m venv venv-instructlab-0.18-3.11
-source venv-instructlab-0.18-3.11/bin/activate
+python3.11 -m venv venv-instructlab
+source venv-instructlab/bin/activate
 pip install 'instructlab[mps]'
 which ilab
 ilab config init
-cd ~/Library/Application\ Support/instructlab/
+cd ~/.config/instructlab/
 mkdir -p taxonomy/knowledge/astronomy/constellations/Phoenix/
 wget https://raw.githubusercontent.com/instructlab/taxonomy/26b3fe21ccbb95adc06fe8ce76c7c18559e8dd05/knowledge/science/astronomy/constellations/phoenix/qna.yaml
 mv qna.yaml taxonomy/knowledge/astronomy/constellations/Phoenix/
@@ -210,22 +215,18 @@ Path to taxonomy repo [taxonomy]: <ENTER>
 
    ```shell
    (venv) $ ilab config init
-   Welcome to InstructLab CLI. This guide will help you set up your environment.
+   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 [taxonomy]: <ENTER>
-   `taxonomy` seems to not exists or is empty. Should I clone https://github.com/instructlab/taxonomy.git for you? [y/N]: y
+   Path to taxonomy repo [/Users/USERNAME/.local/share/instructlab/taxonomy]:
+   `/Users/USERNAME/.local/share/instructlab/taxonomy` seems to not exist 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>
+   Path to your model [/Users/USERNAME/.cache/instructlab/models/merlinite-7b-lab-Q4_K_M.gguf]: y
+   Generating `/Users/USERNAME/.config/instructlab/config.yaml`...
    ```
 
 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-Only)` by hitting Enter. There are various flags you can utilize with individual `ilab` commands that will allow you to utilize your GPU if applicable.
 
    ```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 [~/Library/Application\ Support/instructlab/taxonomy]:
-   Path to your model [/Users/USERNAME/Library/Caches//instructlab/models/merlinite-7b-lab-Q4_K_M.gguf]:
-   Generating `~/Library/Application\ Support/instructlab/config.yaml`...
    Please choose a train profile to use:
    [0] No profile (CPU-only)
    [1] A100_H100_x2.yaml
@@ -243,22 +244,22 @@ Path to taxonomy repo [taxonomy]: <ENTER>
 
 ### `ilab` directory layout after initializing your system
 
-After running `ilab config init` your directories will look like the following on a Linux system:
+After running `ilab config init` your directories will look like the following on a MacOS 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)
+├─ ~/.config/instructlab/models/ (1)
+├─ ~/.config/instructlab/datasets (2)
+├─ ~/.config/instructlab/taxonomy (3)
+├─ ~/.config/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.
+1) `/Users/USERNAME/.config/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.
+2) `~/.config/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.
+3) `~/.config/instructlab/taxonomy/`: Contains the skill and knowledge data.
 
-4) `~/Users/USERNAME/Library/Caches/instructlab/checkpoints/`: Contains the output of the training process
+4) `~/Users/USERNAME/.config/instructlab/checkpoints/`: Contains the output of the training process
 
 ### 📥 Download the model
 
@@ -272,9 +273,9 @@ ilab model download
 
 ```shell
 (venv) $ ilab model download
-Downloading model from Hugging Face: instructlab/merlinite-7b-lab-GGUF@main to /Users/USERNAME/Library/Caches/instructlab/models...
+Downloading model from Hugging Face: instructlab/merlinite-7b-lab-GGUF@main to /Users/USERNAME/.config/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
+INFO 2024-08-01 15:05:48,464 huggingface_hub.file_download:1893: Download complete. Moving file to /Users/USERNAME/.config/instructlab/models/merlinite-7b-lab-Q4_K_M.gguf
 ```
 
 !!! note

docs/pre-work/README.md

@@ -73,6 +73,15 @@ python --version
 Python 3.11.4
 ```
 
+#### Mac installation steps
+
+##### Terminal Installation
+
+If you need to install Python via `brew` please do the following:
+```bash
+brew install [email protected]
+```
+
 Please confirm that your `python --version` is at least `3.11+` for the best experience.
 
 With this you should have the applications you need, let's start the workshop!