Welcome to the world of AI model tuning! Today, we will explore how to leverage the EXL2 quant models effectively. This guide will walk you through the setup and usage of these models, as well as provide troubleshooting tips to help you along the way.

Getting Started with EXL2 Quants

This repository houses the EXL2 quants of a sophisticated AI model designed to replicate the exquisite prose quality found in the Claude 3 models, namely Sonnet and Opus. If you’re in search of original weights, you can find them here: Original Weights.

Available Quant Files

The base repository contains a measurement file, with various quant versions for you to choose from:

• measurement.json
• 4.0bpw
• 5.0bpw
• 6.0bpw
• 8.0bpw

Understanding the Model’s Functionality

Think of the model like a baker crafting a series of artisanal bread loaves. Each quant version is akin to a different recipe or method that yields a unique texture or flavor. The 4.0bpw, 5.0bpw, etc. represent various measurements of precision, allowing you to select the most suitable version for your needs, just like choosing between sourdough or brioche based on your taste preferences.

Prompting the Model

The model has been ingeniously fine-tuned with ChatML formatting, meaning it understands prompts in a specific structure. A sample interaction might look like this:

pyim_startuserHi there!im_endim_startim_startassistantNice to meet you!im_endim_startim_startuserCan I ask a question?im_endim_start

Credits and Acknowledgments

This remarkable achievement has been a true team effort. Some datasets used include:

• NobodyExistsOnTheInternet Claude 3.5s Single Turn
• NobodyExistsOnTheInternet PhiloGlanSharegpt
• NobodyExistsOnTheInternet Magpie-Reasoning-Medium-Subset
• Kalomaze Opus Instruct 25k

Training Details

The training process was carried out over 2 epochs using eight powerful NVIDIA H100 Tensor Core GPUs. This infrastructure ensures that even complex connections between data points are well captured, leading to a high-performance AI model.

Troubleshooting Your Model Experience

If you encounter any issues while using the EXL2 quant models, consider the following troubleshooting steps:

• Confirm that you are using the correct quant version for your requirements.
• Ensure that your prompts are formatted according to the ChatML guidelines.
• Check for any compatibility issues with your environment or hardware.
• Restart the kernel or interface if things seem unresponsive.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

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.