submission of instruction tuning exercises

[ravison123]

December 2024 Student Submission

Module Completed

• Module 1: Instruction Tuning
• Module 2: Preference Alignment
• Module 3: Parameter-efficient Fine-tuning
• Module 4: Evaluation
• Module 5: Vision-language Models
• Module 6: Synthetic Datasets
• Module 7: Inference
• Module 8: Deployment

Changes Made

Describe what you've done in this PR:

1. What concepts did you learn

• Chat templates: how to convert a dataset into the chatml format using chat templates.
• SFT: Finetune a model using trl library. Different arguements for training / finetuning and their significance
• How to load a dataset / model

3. What changes or additions did you make?

• Created 2 notebooks which contain solutions of the exercises. I have also solve the hard problem of finetuning a code generation model.

5. Any challenges you faced?

• I faced a lot of difficulty in how to generate dataset for code completion task

Notebooks Added/Modified

List any notebooks you've added or modified:

• [ x] Added 2 new examples in 1_instruction_tuning/student_examples/ravison123/chat_templates_example.ipynb
• Modified existing notebook with additional examples
• Added documentation or comments

Checklist

• I have read the module materials
• My code runs without errors
• I have pushed models and datasets to the huggingface hub
• My PR is based on the december-2024 branch

Questions or Discussion Points

Add any questions you have or points you'd like to discuss:
1.
3.

Additional Notes

Any other information that might be helpful for reviewers:

[burtenshaw]

Nice work @ravison123 !

Would you like to take part in peer review? If so, mention me on a PR from another student, review it, and I'll get a student to review yours.