Alpaca-350M-Fine-Tuned

Professional work-related project

In this project, I have provided code and a Colaboratory notebook that facilitates the fine-tuning process of an Alpaca 350M parameter model originally developed at Stanford University. The particular model that is being fine-tuned has around 350 million parameters, which is one of the smaller Alpaca models (smaller than my previous fine-tuned model).

The model uses low-rank adaptation LoRA to run with fewer computational resources and training parameters. We use bitsandbytes to set up and run in an 8-bit format so it can be used on colaboratory. Furthermore, the PEFT library from HuggingFace was used for fine-tuning the model.

Hyper Parameters:

1. MICRO_BATCH_SIZE = 4 (4 works with a smaller GPU)
2. BATCH_SIZE = 32
3. GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
4. EPOCHS = 2 (Stanford's Alpaca uses 3)
5. LEARNING_RATE = 2e-5 (Stanford's Alpaca uses 2e-5)
6. CUTOFF_LEN = 256 (Stanford's Alpaca uses 512, but 256 accounts for 96% of the data and runs far quicker)
7. LORA_R = 4
8. LORA_ALPHA = 16
9. LORA_DROPOUT = 0.05

Credit for Original Model: Qiyuan Ge

Fine-Tuned Model: RyanAir/Alpaca-350M-Fine-Tuned (HuggingFace)