documentation: reflow the readme to make following the setup more clear

This minor (though time consuming) change,

1) Moves the models/gmml-vocab.bin file into the test folder.

2) changes the order in which information is presented to the user

3) recommends using symlinks to link training data into the right place in the repo

4) adds some clarification around the importance of the model weights

1 is handy because it enables 'automation' towards 3, e.g. the
command rm -r models/ can safely be symlinked into the models folder
and the commands to do so are clearly listed and described in the README.md

2 is ultimately the only important aspect of this change.  The readme
currently must be read in full by the user, cached, and then returned to
in order to follow along with all the steps in the documentation.

3 is (I think) handy because these files are pretty huge and not exclusive
to this repo.  Symlinks shine in this as that many symlinks can be created
across multiple projects and all point to the same source location.
If researchers were copying/ pasting these to each project, it would get
out of hand fast I think.

4 seems valuable, the AI world looks really opaque to people just getting started.
I did my best to be accurate with my statements in the hops that it makes
it more possible for humans to become more aware of this technology and
what's happening to the internet and the world.

README.md

@@ -150,7 +150,7 @@ And here is another demo of running both LLaMA-7B and [whisper.cpp](https://gith
 
 https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4
 
-## Usage
+## Getting Started
 
 Here are the steps for the LLaMA-7B model.
 
@@ -161,17 +161,25 @@ git clone https://github.com/ggerganov/llama.cpp
 cd llama.cpp
 ```
 
-### Build
+### Build with Linux/ Mac Using Make
 
-In order to build llama.cpp you have three different options.
-
-- Using `make`:
-  - On Linux or MacOS:
+These commands are specific to Ubuntu linux but OS specific varients are just a google away given this handy dependency list.  Also, if you're using your windows gaming machine, some users have reported great success in using [WSL2](https://github.com/ggerganov/llama.cpp/issues/103#issuecomment-1470440202) to install Ubuntu within Windows and following the linux build instructions to run this project.
 
 ```bash
+# Install dependencies (these are for linux, use brew equivelants for mac)
+sudo apt-get update
+sudo apt-get install make cmake build-essentials python3 pip git-lfs
+
+# Build llama.cpp
 make
+
+# Install python dependencies
+python3 -m pip install -r requirements.txt
 ```
 
+### Build For Windows
+
+- Using `make`:
   - On Windows:
 
     1. Download the latest fortran version of [w64devkit](https://github.com/seeto/w64devkit/releases).
@@ -198,6 +206,8 @@ In order to build llama.cpp you have three different options.
     zig build -Drelease-fast
     ```
 
+Don't forget to install the Python dependencies (e.g. `python -m pip install -r requirements.txt`)
+
 ### BLAS Build
 
 Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
@@ -257,15 +267,45 @@ Building the program with BLAS support may lead to some performance improvements
     cmake --build . --config Release
     ```
 
-### Prepare Data & Run
+### Aquiring Setting up the 7b model weights
+
+You can use this system to conduct research on an AI chatbot vaguely comparable to ChatGPT-3 and it will even run on your local machine without needing massive amounts of hardware.  But to do so you **must** install the Alpaca 7b model weights into the models folder.
+
+Because these resources belong to Facebook, their official path to obtaining the data should be followed.  While it's true that most researchers using the Alpaca weights obtained them from a magnet link to a torrent file, linking or sharing that magnet link should not be done in this repo due to the questionability of violating FaceBook's IP rights and also (not to be an alarmist here) the potential for the popularization of these weights to cause harm.
+
+- **Under no circumstances should IPFS, magnet links, or any other links to model downloads be shared anywhere in this repository, including in issues, discussions, or pull requests. They will be immediately deleted.**
+- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository.
+- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data.  Any Magnet links shared
+
+#### Putting the Model Weights in the Right Spot
+
+This guide will assume that you've downloaded the files to an arbitrary folder, `/mnt/c/ai/models/LLaMA` using some responsible means described above.
+
+Because model weights files are so large, you may find it convenient to use softlinks to make them appear within the `models/` folder instead of having to copy them around on your HDD.  Otherwise, if you prefer, you can just download the files directly in the `models/` folder and skip the below softlink command:
 
 ```bash
-# obtain the original LLaMA model weights and place them in ./models
+# On linux, run this from the root of this repo
+rm models/.gitkeep
+
+# Now that the folder is empty, this command can safely remove the models/ folder or errors if something is still there
+rm -r models/
+
+# Create a symlink to the folder where the LLaMA weights are located
+ln -s /mnt/c/ai/models/LLaMA $(pwd)/models
+```
+
+### Prepare Data
+
+```bash
+# Verify the original LLaMA model data
 ls ./models
 65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model
 
-# install Python dependencies
-python3 -m pip install -r requirements.txt
+# Check for file corruption and wrong data
+sha256sum --ignore-missing -c SHA256SUMS
+
+# On mac, use this command instead
+shasum -a 256 --ignore-missing -c SHA256SUMS
 
 # convert the 7B model to ggml FP16 format
 python3 convert.py models/7B/
@@ -277,6 +317,13 @@ python3 convert.py models/7B/
 ./main -m ./models/7B/ggml-model-q4_0.bin -n 128
 ```
 
+### Run the Alpaca 7b Model
+
+```bash
+# run the inference
+./main -m ./models/7B/ggml-model-q4_0.bin -n 128
+```
+
 When running the larger models, make sure you have enough disk space to store all the intermediate files.
 
 ### Memory/Disk Requirements
@@ -374,19 +421,7 @@ python3 convert.py models/gpt4all-7B/gpt4all-lora-quantized.bin
 
 - The newer GPT4All-J model is not yet supported!
 
-### Obtaining and verifying the Facebook LLaMA original model and Stanford Alpaca model data
-
-- **Under no circumstances should IPFS, magnet links, or any other links to model downloads be shared anywhere in this repository, including in issues, discussions, or pull requests. They will be immediately deleted.**
-- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository.
-- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data.
-- Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files.
-- The following command will verify if you have all possible latest files in your self-installed `./models` subdirectory:
-
-  `sha256sum --ignore-missing -c SHA256SUMS` on Linux
-
-  or
-
-  `shasum -a 256 --ignore-missing -c SHA256SUMS` on macOS
+### Additional Notes on the original Facebook LLaMA model and Stanford Alpaca model data
 
 - If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
 - LLaMA: