Use THIS Today to Make Your Local LLM Smarter + Claude 3 Opus Tips

TL;DR

Developing a system to enhance LM responses using code interpretation with CLA 3 Opus, featuring local and GPT models.

Transcript

okay so this is a custom test I used to check the intelligence of llm so we are running it on chat GPT 3.5 I know the answer is 36 and here you can see it calculated that it was 32 apples so let's head over to my boosted llm this is also chat GPT 3.5 but we using the API so when I run this now you can see should use code analysis yes and the next t... Read More

Key Insights

• 👨‍💻 Creating a system that enhances LM responses by analyzing user input and utilizing code interpretation.
• 👨‍💻 Utilizing functions like should_use_code to determine the necessity of incorporating Python code in responses.
• 👨‍💻 Storing code output and integrating it with user questions to provide contextually enriched natural language responses.
• 👤 Adding features like user permission confirmation for code execution to enhance user interaction and customization.

Questions & Answers

Q: What inspired the system's use of code interpretation?

The system draws inspiration from GPT 4's code interpreter to enhance LM responses based on the analysis of user input queries and the context needed.

Q: How does the system determine when to use code for improving answers?

The system's should_use_code function assesses if utilizing Python code would benefit providing the best answer, prompting 'yes' for code usage and 'no' for natural language responses.

Q: How does the system integrate code output with LM responses?

By storing code output and feeding it along with user questions into context prompts, the system enhances LM responses with natural language tailored to the code output.

Q: What feature was added to the system to interact with users' permissions?

A feature was added to prompt users to confirm code execution with 'y' or suggest alternative code with 'n' before proceeding, showcasing interactive engagement.

Summary & Key Takeaways

• A custom test is used to check LM intelligence, involving running it on GPT 3.5 and AMPs with code analysis and fix attempts.

• The system analyzes user input to determine code necessity, executes code, and enhances LM responses with context like Rag.

• Features key code functions like should_use_code to decide code usage, saving code output, and generating natural language responses.