[SUBMISSION] December 2024 - module 1 & 2

[Francesck94]

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?
   Instruction Tuning, DPO and ORPO.

2. What changes or additions did you make?

• For the sft_finetuning_example.ipynb I used the "Felladrin/ChatML-open-instruct" dataset.
• For the dpo_finetuning_example.ipynb I used the "argilla/ultrafeedback-binarized-preferences" dataset to make the preference alignment of the model I trained in 'sft_finetuning_example.ipynb'.
• For the orpo_finetuning_example.ipynb I used a subset of "mlabonne/orpo-dpo-mix-40k" dataset.

3. Any challenges you faced? I had to downsampling the datasets in order to use colab gpu for the training

Notebooks Added/Modified

List any notebooks you've added or modified:

• Added new example in module_name/student_examples/my_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.
2.

Additional Notes

Any other information that might be helpful for reviewers:
I used colab to run the experiment