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End of training

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  1. README.md +6 -6
README.md CHANGED
@@ -46,7 +46,7 @@ More information needed
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  # Resource Usage Comparison
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- - VRAM Use: 7.7871 GB
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  # Distillation (Teacher -> Student) Architecture Difference:
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@@ -66,7 +66,7 @@ More information needed
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  <br/>
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  # Train Dataset
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- Trained on 145,744,973 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.
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  - Num Samples: `247,500`
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  - Subset: `20231101.en`
@@ -76,7 +76,7 @@ Trained on 145,744,973 tokens from the [wikimedia/wikipedia](https://huggingface
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  # Training Objective
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  ```
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- DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=25.0, loss_fn=raw_mse, layer_mapper=layer-2, norm=layernorm, projector=orthogonal))
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  ```
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  # Hyperparameters
@@ -93,9 +93,9 @@ The following hyperparameters were used during training:
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  - lr_scheduler_type: `cosine_with_min_lr`
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  - lr_scheduler_warmup_ratio: `0.5`
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  - num_epochs: `1.0`
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- - distillation_objective: `DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=25.0, loss_fn=raw_mse, layer_mapper=layer-2, norm=layernorm, projector=orthogonal))`
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  - train_embeddings: `True`
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- - lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fe929482740>`
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  - student_model_name_or_path: `None`
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  - student_config_name_or_path: `None`
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  - student_model_config: `None`
@@ -114,7 +114,7 @@ The following hyperparameters were used during training:
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  - dataset_test_size: `0.01`
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  - gradient_accumulation_steps: `1`
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  - weight_decay: `0.0`
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- - max_grad_norm: `1.0`
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  - warmup_ratio: `0.5`
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  - warmup_steps: `0`
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  - gradient_checkpointing: `True`
 
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  # Resource Usage Comparison
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+ - VRAM Use: 7.7868 GB
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  # Distillation (Teacher -> Student) Architecture Difference:
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  <br/>
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  # Train Dataset
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+ Trained on 145,714,513 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.
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  - Num Samples: `247,500`
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  - Subset: `20231101.en`
 
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  # Training Objective
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  ```
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+ DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=25.0, loss_fn=cos, layer_mapper=layer-2, norm=batchnorm, projector=orthogonal))
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  ```
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  # Hyperparameters
 
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  - lr_scheduler_type: `cosine_with_min_lr`
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  - lr_scheduler_warmup_ratio: `0.5`
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  - num_epochs: `1.0`
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+ - distillation_objective: `DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=25.0, loss_fn=cos, layer_mapper=layer-2, norm=batchnorm, projector=orthogonal))`
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  - train_embeddings: `True`
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+ - lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fc4addc8820>`
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  - student_model_name_or_path: `None`
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  - student_config_name_or_path: `None`
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  - student_model_config: `None`
 
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  - dataset_test_size: `0.01`
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  - gradient_accumulation_steps: `1`
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  - weight_decay: `0.0`
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+ - max_grad_norm: `100`
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  - warmup_ratio: `0.5`
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  - warmup_steps: `0`
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  - gradient_checkpointing: `True`