Update README.md

README.md

@@ -90,7 +90,7 @@ For ReFT, the nodes in the last 8 layers were unfrozen with attention to allow t
 After 3 to 4 epochs, the model began to overfit regardless of the strategies employed. Increasing both batch size and the number of epochs resulted in higher final training and evaluation cross-entropy.
 
 Following an extensive grid search, supervised fine-tuning of [Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) with LoRA+ and the parameters mentioned below yielded the best training and evaluation cross-entropy. 
-I've chosen the size ratio between the matrices A and B of 8. The matrix A weights were initialized using the He method, while the matrix B values started with zero. Different Gaussian initialization of weights were also considered, but led to a suboptimal result. Since a custom optimizer was built here, I will share the optimizer code on my private GitHub account soon.
+I've chosen the size ratio between the matrices A and B of 8. The matrix A weights were initialized using the He method, while the matrix B values started with zero. Different Gaussian initialization of weights were also considered, but led to a suboptimal result. Since a custom optimizer was built here, I will share that [code](https://github.com/nenad1002/QuantumScienceBotModel-LLM/blob/main/lora_plus_optimizer.py) here. Regarding the rest of the code, including pre-training, CustomSFTTrainer, and the scoring scripts are currently in the private repo, and will become public as soon it's ready.
 
 #### Preprocessing [optional]