Welcome to our guide on setting up the Bagel-7B AI model, a magical algorithm that brings together vast datasets and advanced tuning techniques to create a powerful model for various tasks. Whether you’re a seasoned AI enthusiast or a curious beginner, we’re here to break down the process and make it user-friendly!

Overview of Bagel-7B

Bagel-7B is a DPOd (Direct Preference Optimization) version of a language model. If you encounter frequent refusals or unexpected outputs, consider trying the non-DPO version. This guide will help you create and manage the necessary datasets needed to fine-tune this model effectively.

Benchmarks

This section showcases scores from different benchmarks against which the Bagel model has been evaluated:

 model  arc_challenge  boolq  gsm8k  hellaswag  mmlu  openbookqa  piqa  truthful_qa  winogrande
bagel  __0.6715__  0.8813  __0.5618__  0.8397  __0.6408__  __0.51__  __0.8406__  __0.6275__  __0.7561__

Creating Datasets

The first step in fine-tuning is creating a dataset that combines both Supervised Fine-tuning (SFT) and DPO data. Let us dive into the steps:

• Convert instruction data into ShareGPT format for easier usage.
• Deduplicate data using UUID v5 of the instruction text, ensuring only unique instructions are included.
• Prioritize entries from higher confidence sources during deduplication.

Data Sources for Fine-Tuning

Here are some selected data sources included in SFT:

• ai2_arc – Measures intelligence through abstraction and reasoning.
• airoboros – Synthetic instructions generated by GPT-4.
• apps – Python coding dataset with various challenges.
• belebele – Multi-lingual reading comprehension records.
• … And many others!

Training Strategies

In keeping with the multifaceted approach of Bagel-7B, we utilize multiple prompt formats and training epochs to maximize performance:

Think of it as a chef preparing a bagel with various ingredients, each adding unique flavors and layers. Instead of sticking to a single recipe, using multiple formats helps to create a more robust output that can generalize better across tasks.

Fine-Tuning Process

Supervised Fine-tuning (SFT)

• Set up your environment variables for the workspace and wandb projects.
• Use the appropriate scripts to start the pretraining process.
• Optimize your training parameters for best results – a learning rate of 3.5e-7 is suggested.

Direct Preference Optimization (DPO)

• Follow the previous SFT-trained model to kickstart DPO.
• Adjust batch sizes and evaluation strategies for the desired outcomes.

Troubleshooting Tips

If you face issues during initial setup or training, consider the following suggestions:

• Recheck your dataset paths and ensure they are correctly specified.
• Adjust the batch sizes if you’re encountering memory issues.
• Monitor gradient accumulation steps and learning rates—small tweaks can lead to significant changes in performance.
• If you’re getting too many refusals, revert to the non-DPO version for revised outputs.

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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.