Welcome to our guide on utilizing the Contextual Decision Process Optimization (DPO) with the Microsoft Phi-3 Mini model! This model is designed to help applications better adhere to provided context and significantly reduce hallucinations. Let’s dive into how you can implement this innovative approach in your projects.

Understanding the Basics

The contextual prompt format we will be discussing is composed of specific delimiters that delineate the different sections of your input. Think of it like organizing a bookshelf: every book (input) has its own space (block) and a tag (context) that tells you exactly what it’s about. This structure allows for structured retrieval of information that is more precise and contextually relevant.

Constructing Your Contextual Prompt

The format for a contextual prompt is as follows:

BEGININPUT
BEGINCONTEXT[key0: value0][key1: value1]... other metadata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[Add as many other blocks as necessary]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION

Breaking Down the Formatting

• BEGININPUT – This marker starts a new input block.
• BEGINCONTEXT – This section contains metadata key-value pairs relevant to the input.
• ENDCONTEXT – Marks the conclusion of the metadata block.
• [text] – Place your main content here, which can consist of paragraphs and more.
• ENDINPUT – This marker signifies the end of the current input block.
• [Repeat the above format as needed]
• BEGININSTRUCTION – Indicates the start of instructions to process the input blocks above.
• [instruction(s)] – Insert your specific instructions for the model here.
• ENDINSTRUCTION – This marks the end of your instruction set.

An Analogy for Clarity

Imagine you are a director in a large production. You have several scenes (input blocks) full of actors (content) and storylines (context). Each scene is tailored with instructions for the actors. Now, if an actor forgets their line and instead starts giving random monologues, the audience (your model) will get confused. By organizing the script and clearly labeling each part—much like our prompt structure—you ensure that every actor knows their part, and the audience is kept engaged and informed. This is the essence of using explicit delimiters in contextual prompts: it helps avoid confusion and promotes clarity.

Troubleshooting Common Issues

As you begin implementing the contextual DPO model, you might encounter some challenges. Here are a few troubleshooting tips to help you out:

• Issue: Incomplete responses or confusion in outputs.

  Solution: Double-check the structuring of your input and ensure that you are using the proper sequence of BEGIN and END tags.

• Issue: Hallucinations or inaccurate responses.

  Solution: Reassess the context you are providing—ensure it’s comprehensive and relevant to the questions being asked.

• Issue: Slow retrieval times.

  Solution: Optimize the dataset size or refine the specific context blocks being inputted.

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Conclusion

With these guidelines, you are now equipped to effectively utilize the contextual DPO model in your applications. Remember, the rigging of context is essential for success in retrieving accurate and reliable responses. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.