Well, it’s a large language model (LLM) that can understand and generate human languages. It has been trained on billions of words from various sources like books, articles, and web pages.

But enough with the technical jargon! Let me explain how Llama 2 works in simpler terms: imagine you’re having a conversation with someone who can read your mind (creepy, I know). They understand everything you think and respond accordingly. That’s basically what Llama 2 does it reads your input (in this case, text) and generates an output based on its training data.
Now fine-tuning. This is the process of adapting a pre-trained model to perform specific tasks or functions. For example, if you want Llama 2 to be able to answer questions related to cooking recipes, you can fine-tune it using a dataset that contains recipe instructions and answers to common cooking queries.
On-device execution refers to running the trained model on your own device (like a phone or tablet) instead of relying on cloud services. This is important for privacy reasons you don’t want your personal data being sent to some remote server every time you ask Llama 2 a question! Plus, it can be faster and more efficient since the model doesn’t have to travel over the internet.
So how do we fine-tune Llama 2 for on-device execution? First, we need to convert the pre-trained model into a format that can run on mobile devices (like TensorFlow Lite or ONNX). This involves compressing and optimizing the model’s weights so it takes up less space and runs faster.
Next, we train the fine-tuned model using a dataset that contains examples of how to use Llama 2 for specific tasks (e.g. answering cooking questions or generating poetry). The training process involves feeding the model input data and comparing its output with the correct answer. If the output is close enough to the correct answer, the model learns from its mistakes and improves over time.
Finally, we test the fine-tuned model on a separate dataset (called a validation set) to make sure it’s working properly. This involves feeding the model input data and comparing its output with the correct answers. If the output is close enough to the correct answer, the model passes the test!
It’s like training a puppy to do tricks by giving it treats when it does something right (like answering cooking questions or generating poetry). And just like with any good dog, the more you train Llama 2, the better it gets!