Tau LLM Series: Enhancements and Debugging | part 18, 19

Welcome back to our Tau LLM series! 🌟 Over the past 48 hours, we've made significant progress and tackled some exciting challenges. In this blog post, we'll recap our recent work and share insights from our latest episodes.

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Recent Developments

Oproof Integration Success

In our previous episode, we successfully integrated the oproof Python package into our system. This package is designed to validate prompt-response pairs using Ollama and Python, ensuring data integrity and accuracy. The integration went smoothly, and we're excited about the potential it brings to our project.

Enhanced Command Processor

We've upgraded our command processor to handle lists of arguments instead of just a single string. This enhancement makes our command processor more versatile and powerful, allowing for more complex and flexible command inputs.

Debugging and Fine-Tuning

Although our oproof engine is working, we encountered an issue where the proof task isn't processing the returned data correctly. Over the past two days, we've been debugging and fine-tuning this to ensure everything runs smoothly. This involves inspecting the output, adding logging statements, and comparing expected vs. actual outputs.

Output Verification

To verify that the proof engine is working as expected, we've been closely inspecting the generated files and making necessary adjustments. This step is crucial to ensure the accuracy and reliability of our proof engine.

Episode Recap

Part 18: Pretrain an LLM from Scratch with Sentence Transformers

In our last episode, we showcased the following advancements:

• Oproof Python Package Completion: We successfully completed the oproof Python package, designed to validate prompt-response pairs using Ollama and Python.
• Terminal Command Implementation: We integrated the oproof package into Tau's kernel as the data oproof {filename} terminal command. This command loads a data file of training messages and validates each prompt-response pair, checking for domain accuracy in basic math, grammar, and spelling.
• Error Handling and Output: Any invalid messages are removed from the input training data and saved into a *_oproof_error.json file, similar to our ophrase terminal command.

Part 19: Enhancements and Debugging

In our latest episode, we continued our journey with the following updates:

• Oproof Integration Success: We've successfully integrated the oproof Python package into our system.
• Enhanced Command Processor: Our command processor now handles lists of arguments instead of just a single string.
• Debugging and Fine-Tuning: We're debugging and fine-tuning the proof task to ensure it processes the returned data correctly.
• Output Verification: We're inspecting the output to verify that the proof engine is working as expected.

Join us as we tackle these challenges and enhance our LLM with custom tools and techniques. Whether you're a beginner or an experienced developer, these episodes offer valuable insights into developing, testing, and refining an LLM.

Stay tuned and let's get started! 🚀