Add Function Calling Fine-tuning LLMs on xLAM Dataset notebook

[behroozazarkhalili]

Summary

This notebook demonstrates how to fine-tune language models for function calling capabilities using the xLAM dataset from Salesforce and QLoRA technique.

Key Features

• Universal Model Support: Works with Llama, Qwen, Mistral, Gemma, Phi, and more
• Memory Efficient: QLoRA training on consumer GPUs (16-24GB VRAM)
• Automatic Configuration: Smart token detection and model setup
• Production Ready: Comprehensive documentation and error handling
• Complete Pipeline: From training to Hugging Face Hub deployment

Technical Details

• Uses QLoRA (Quantized Low-Rank Adaptation) for efficient fine-tuning
• Supports multiple model architectures with automatic pad token detection
• Includes comprehensive testing and evaluation functions
• Modular design with proper type hints and documentation

Contribution Guidelines Compliance

• Notebook filename in lowercase: function_calling_fine_tuning_llms_on_xlam.ipynb
• Author information added with GitHub profile link
• Added to _toctree.yml in LLM Recipes section
• Added to index.md in Latest notebooks section
• Non-informative outputs removed from pip install cells
• No empty code cells
• Comprehensive documentation and markdown explanations

Test Plan

• Notebook structure and organization verified
• All cells contain proper documentation
• Code quality and error handling implemented
• Ready for community use and contribution

✅ All contribution guidelines followed according to the README

@merveenoyan , @stevhliu

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[stevhliu]

Thanks for your contribution!

My first impression is that it is very code-heavy without really any supporting text that explains what is happening and the rationale behind certain decisions. Breaking up these code blocks will make it easier for users to digest.

Also pinging @sergiopaniego, our recipe chef, for any other additional suggestions ❤️

[behroozazarkhalili]

Thanks for your contribution!

My first impression is that it is very code-heavy without really any supporting text that explains what is happening and the rationale behind certain decisions. Breaking up these code blocks will make it easier for users to digest.

Also pinging @sergiopaniego, our recipe chef, for any other additional suggestions ❤️

Hi @stevhliu, Thank you for the feedback.
Are you asking if I should include an explanation for each step and the reasons for selecting specific parameters, etc.? I would be grateful if you could add any more clarification.

[stevhliu]

Sorry I wasn't clear!

Yes, a general explanation for each step would be nice. You don't have to go too in-depth explaining why you selected specific parameters (unless its important), but the user should be able to read a paragraph and have a good idea of what is happening at a step.

[behroozazarkhalili]

Sorry I wasn't clear!

Yes, a general explanation for each step would be nice. You don't have to go too in-depth explaining why you selected specific parameters (unless its important), but the user should be able to read a paragraph and have a good idea of what is happening at a step.

No worries. I'll make the updates based on your comments and submit the pull request soon. :)

[sergiopaniego]

Thanks for the effort!! 😃 Following the same ideas suggested by @stevhliu and similar to #319:
Code blocks should be divided into smaller sections and explained. We don’t need an in-depth breakdown of every parameter, but rather an explanation of the problem we’re trying to solve and why each function or block of code is necessary.

A recipe should be aimed at readers who want to learn more about a specific technique or package, so the focus should be more educational rather than simply presenting a complete project with a lot of code. You can also reference other recipes to provide additional context and insights.