End of training

README.md

@@ -60,7 +60,7 @@ wandb_log_model:
 
 gradient_accumulation_steps: 4
 micro_batch_size: 1
-num_epochs: 6
+num_epochs: 8
 optimizer: adamw_torch
 lr_scheduler: cosine
 learning_rate: 0.0002
@@ -99,7 +99,7 @@ special_tokens:
 
 </details><br>
 
-[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/afrias5/Code32bTDualGPU/runs/0i7pl0s6)
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/afrias5/Code32bTDualGPU/runs/oh019yiu)
 # codellama-cont2
 
 This model is a fine-tuned version of [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) on the None dataset.
@@ -133,7 +133,7 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_steps: 10
-- num_epochs: 6
+- num_epochs: 8
 
 ### Training results