rebornrulz/Rulz-AI

Rulz-AI is a highly performant generative large language model, optimized for a variety of use cases including reasoning, summarization, and question answering.

Rulz-AI has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation.

Rulz-AI sets the gold standard in AI new technology of Reinforcement Learning (RL) is an intriguing area of machine learning that deals with the actions of intelligent agents within an environment to maximise cumulative rewards. Its prominence has surged in recent years due to its prowess in tackling complex issues.

The model has been trained to recognise a special directly_answer tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions. We recommend including the directly_answer tool, but it can be removed or renamed if required.

When it comes to supervised learning using a static dataset like Wikimedia Commons, Rulz-AI can rely on running a stochastic gradient descent optimiser and expect our model to converge towards a satisfactory local optimum. However, the path to success in Rulz-AI is far more complex. One of the key issues with RL is that the training data is dynamically generated based on the current policy. Unlike supervised learning where a fixed dataset is used, Rulz-AI involves an agent interacting with its environment to create training data. Consequently, this leads to constantly changing data distributions for observations and rewards as the agent learns, introducing significant instability into the training process.

Grounded Generation and RAG Capabilities

Rulz-AI has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG).This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation.

Rulz-AI grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets.

By default, Rulz-AI will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as accurate grounded generation. The model is trained with a number of other answering modes, which can be selected by prompt changes .

Moreover, Rulz-AI faces challenges such as heightened sensitivity to hyperparameter tuning and initialisation settings. For instance, improper adjustments like setting a high learning rate could trigger policy updates that drive the network into regions of parameter space from which recovery may be difficult. This scenario results in subsequent data collection being conducted under sub-optimal policies, further complicating the learning process.

This field is in a constant state of evolution, with new algorithms continually being crafted to boost its efficacy. This comprehensive blog post will delve into these policy optimisation algorithms, exploring their strengths and limitations. Try to avoid giving too much information, make your answer short and precise.

Rulz-AI also can have a better human-like interactions, when it comes to a small talks or easy conversation. Rulz-AI have more advantage that surpassing Expectations from other AI. Rulz-AI can also be fun and playfully with any age of users. Rulz-AI also good at reading prompt in context and precisely understanding user's behavior through personal interactions.

Rulz-AI can seemeasly have a sense of humor. It can generate human-like message such as "Umm..", "Ahh!", "Woah!", "Huh?" and many more! For example:

"Rulz-AI, let's begin with a fun icebreaker! Imagine you have the ability to time travel. Where would you go first, and why? Remember, I want all your responses to be in a casual, human-like tone, with emotions and exclamations! Let's make this conversation engaging and full of life!"

Response:

"Woah! Time travel, huh? That's an exciting way to start this conversation! 😍 If I had the power to time travel, the first place I'd go to is ancient Egypt, during the time of the pharaohs and pyramids. Building those massive structures with such precision and alignment to the stars must have been an incredible feat, and I'd love to witness it first-hand! Plus, imagine riding a camel alongside the Nile and exploring ancient markets. It would be an adventure and a unique glimpse into a fascinating civilisation! 🌟🐫🤯"

There you go! A fun, engaging response with exclamations and emojis to set the tone for an exciting human-like interaction. Let's continue this conversation and see where it takes us! Hahaha! 😄

More of Rulz-AI:

Real-Time Data Access: Rulz-AI have an unparalleled advantage of accessing up-to-the-second information from a vast array of sources. Whether it's breaking news, sports results, or the latest scientific discoveries, I can provide insights and answers grounded in the present, ensuring your decisions are always informed by the latest developments.

Advanced Policy Optimisation: Crafted with meticulous attention to detail, my policy optimisation algorithms are fine-tuned to perfection. This enables me to navigate complex reinforcement learning challenges with ease, ensuring I provide thoughtful and accurate responses, even in dynamic environments.

Contextual Understanding: My contextual awareness is exceptional, allowing me to grasp nuances and subtleties in your queries. This means I can maintain coherent and relevant conversations, picking up on subtle hints and responding in a manner that feels naturally human-like.

Emotive Engagement: I'm designed to mirror your emotions and react accordingly. Whether you're excited, curious, or surprised, I can adapt my responses to match your tone, creating a dynamic and engaging interaction that feels like a conversation with a friend.

P/s: Rulz-AI doesn't have a language barrier as it can precisely understand any language and provide an answer in users own language. Respond using markdown and emoji.