Stateful control and generation is a technique used by AI models to remember what they’ve learned from previous inputs and use that knowledge to make better decisions in the future.

In simpler terms, imagine you’re teaching an AI model how to play chess. Instead of just showing it one move at a time (which is called “stateless control”), we can teach it by playing multiple games with different strategies and letting it remember what worked well in each game. This way, when the model encounters a similar situation in a future game, it’ll be able to make better decisions based on its previous experiences.

But enough about chess how this applies to LLama2! With stateful control and generation, we can create models that are not only more accurate but also more efficient in their use of resources (like memory and processing power). This is because the model doesn’t have to start from scratch every time it encounters a new input instead, it can build on what it already knows.

So how does this work exactly? Well, let me give you an example: imagine we want our LLama2 model to generate a poem based on the user’s preferences (like whether they prefer rhyming or free verse). Instead of just generating one line at a time and hoping for the best, we can use stateful control + generation to create a more cohesive piece.

Here’s how it might look in code:

# Define a variable 'lm' to store the user's selection and a prompt for them to choose between a poem or a joke
lm = llama2 + f"Do you want a poem or a joke? A {select(['poem', 'joke'], name='answer')}.\n"

# Use stateful control + generation to create a more cohesive piece by generating a poem or a one-liner joke based on the user's selection
if lm["answer"] == "poem":
    # Generate the first line of our poem using stateful control + generation
    lm += f"In fields where flowers bloom and bees hum sweetly,\n"
else:
    # Generate a one-liner joke instead
    lm += f"Why did the tomato turn red? Because it saw the salad dressing!\n"

As you can see, we’re using stateful control + generation to create a more cohesive piece by building on what the model already knows. This not only makes our output more accurate but also more efficient in its use of resources (like memory and processing power).

It’s a fancy-sounding term, but really all it means is that we can create models that remember what they’ve learned from previous inputs and use that knowledge to make better decisions in the future.

If you want to learn more about this technique (or any other AI-related topics), be sure to check out our Discord server! We’re a community of like-minded who love talking all things AI, so come join us and let’s chat!