# Llama2 - Training

### <mark style="color:blue;">LLama2</mark>

To execute the main training script Axolotl provides this command to begin training Phi 2.0

```bash
accelerate launch -m axolotl.cli.train examples/llama-2/lora.yml
```

If you have not already done so, you will be asked to enter your Weights and Biases API Key.&#x20;

&#x20;Enter the key at the command line prompt:

```yaml
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
```

### <mark style="color:blue;">An analysis of the axolotl.clt.train module</mark>

<details>

<summary><mark style="color:green;">Analysis of train.py script</mark></summary>

The <mark style="color:yellow;">`train.py`</mark> script in the Axolotl platform is a Command Line Interface (CLI) tool designed for training machine learning models.&#x20;

This script is structured to provide a user-friendly interface for configuring and executing model training. Here's a detailed analysis:

#### <mark style="color:blue;">Script Structure and Functionality</mark>

<mark style="color:green;">**Imports and Logger Setup**</mark>

* Essential modules like `logging`, `pathlib.Path`, `fire`, and `transformers` are imported.
* The script sets up a logger `LOG` using the `logging` module for logging various events and statuses during the script's execution.

<mark style="color:green;">**do\_cli Function**</mark>

* <mark style="color:blue;">**Function Definition**</mark><mark style="color:blue;">:</mark> The <mark style="color:yellow;">`do_cli`</mark> function is the main entry point of the script. It accepts a <mark style="color:yellow;">`config`</mark> argument (with a default value pointing to an "examples" directory) and `**kwargs` for additional arguments.
* <mark style="color:blue;">**ASCII Art Display**</mark><mark style="color:blue;">:</mark> <mark style="color:yellow;">`print_axolotl_text_art()`</mark> is called to display ASCII art, likely for aesthetic purposes.
* <mark style="color:blue;">**Configuration Loading**</mark><mark style="color:blue;">:</mark> <mark style="color:yellow;">`load_cfg`</mark> <mark style="color:yellow;"></mark><mark style="color:yellow;">l</mark>oads configuration details from the provided `config` path. These configurations are essential for setting up model training parameters.
* <mark style="color:blue;">**Accelerator and User Token Checks**</mark><mark style="color:blue;">:</mark> The script verifies the default configuration for the accelerator (such as a GPU) and checks the user token. These checks are crucial for ensuring that the hardware is correctly set up and the user is authenticated.
* <mark style="color:blue;">**CLI Arguments Parsing**</mark><mark style="color:blue;">:</mark> It uses <mark style="color:yellow;">`transformers.HfArgumentParser`</mark> to parse additional CLI arguments into data classes (<mark style="color:yellow;">`TrainerCliArgs`</mark>). This step allows for dynamic customization of training parameters via the command line.
* <mark style="color:blue;">**Dataset Loading**</mark><mark style="color:blue;">:</mark> <mark style="color:yellow;">`load_datasets`</mark> is called with the parsed configuration and CLI arguments. This function is responsible for loading the dataset as per the configuration, which is a critical step in the training process.
* <mark style="color:blue;">**Model Training**</mark><mark style="color:blue;">:</mark> The <mark style="color:yellow;">`train`</mark> function is invoked with the loaded configuration, CLI arguments, and dataset metadata. This function likely encompasses the core logic for model training.

<mark style="color:green;">**Main Block**</mark>

* The script checks if it's being run as the main program <mark style="color:yellow;">(</mark><mark style="color:yellow;">`__name__ == "__main__"`</mark><mark style="color:yellow;">)</mark> and not as a module in another script. If it's the main program, it uses <mark style="color:yellow;">`fire.Fire(do_cli)`</mark> to execute the <mark style="color:yellow;">`do_cli`</mark> function, enabling the script to be interacted with from the command line.

</details>
