Training in progress, step 198000
Browse files- README.md +7 -8
- logs/attn_norm=layernorm, attn_projector=mlp, attn_weight=5, learning_rate=0.0001, per_device_train_batch_size=2, warmup_ratio=0/completed.flag +0 -0
- logs/attn_norm=layernorm, attn_projector=mlp, attn_weight=5, learning_rate=0.0002, per_device_train_batch_size=2, warmup_ratio=0/events.out.tfevents.1725090745.63bdd91cff03 +3 -0
- logs/attn_norm=layernorm, attn_projector=mlp, attn_weight=5, learning_rate=0.0002, per_device_train_batch_size=2, warmup_ratio=0/events.out.tfevents.1725091248.63bdd91cff03 +3 -0
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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# Resource Usage Comparison
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- VRAM Use: 7.
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# Distillation (Teacher -> Student) Architecture Difference:
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<details>
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<summary>Expand</summary>
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- learning_rate: `0.
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- train_batch_size: `
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- eval_batch_size: `8`
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- seed: `42`
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- optimizer: `Adam with betas=(0.9,0.999) and epsilon=1e-08`
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- lr_scheduler_type: `polynomial`
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- lr_scheduler_warmup_ratio: `0.2`
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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=5, loss_fn=raw_mse, layer_mapper=layer-2, norm=layernorm, projector=mlp))`
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- train_embeddings: `True`
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- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at
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- student_model_name_or_path: `None`
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- student_config_name_or_path: `distilbert/distilgpt2`
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- student_model_config: `None`
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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
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- warmup_steps: `0`
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- gradient_checkpointing: `True`
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# Framework Versions
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- Distily 0.4.1
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- Transformers 4.44.2
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- Pytorch 2.
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- Datasets 2.
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# Resource Usage Comparison
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- VRAM Use: 7.4146 GB
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# Distillation (Teacher -> Student) Architecture Difference:
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<details>
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<summary>Expand</summary>
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- learning_rate: `0.0001`
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- train_batch_size: `2`
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- eval_batch_size: `8`
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- seed: `42`
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- optimizer: `Adam with betas=(0.9,0.999) and epsilon=1e-08`
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- lr_scheduler_type: `polynomial`
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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=5, loss_fn=raw_mse, layer_mapper=layer-2, norm=layernorm, projector=mlp))`
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- train_embeddings: `True`
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- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fcf48307cd0>`
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- student_model_name_or_path: `None`
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- student_config_name_or_path: `distilbert/distilgpt2`
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- student_model_config: `None`
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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`
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- warmup_steps: `0`
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- gradient_checkpointing: `True`
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# Framework Versions
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- Distily 0.4.1
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- Transformers 4.44.2
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- Pytorch 2.3.0
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- Datasets 2.21.0
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logs/attn_norm=layernorm, attn_projector=mlp, attn_weight=5, learning_rate=0.0001, per_device_train_batch_size=2, warmup_ratio=0/completed.flag
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logs/attn_norm=layernorm, attn_projector=mlp, attn_weight=5, learning_rate=0.0002, per_device_train_batch_size=2, warmup_ratio=0/events.out.tfevents.1725090745.63bdd91cff03
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logs/attn_norm=layernorm, attn_projector=mlp, attn_weight=5, learning_rate=0.0002, per_device_train_batch_size=2, warmup_ratio=0/events.out.tfevents.1725091248.63bdd91cff03
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model.safetensors
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training_args.bin
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