Training in progress, step 99000
Browse files- README.md +8 -7
- config.json +1 -1
- generation_config.json +1 -1
- logs/attn_norm=layernorm, attn_projector=orthogonal, attn_weight=5, learning_rate=0.0001, per_device_train_batch_size=4, warmup_ratio=0.2/completed.flag +0 -0
- logs/attn_norm=layernorm, attn_projector=orthogonal, attn_weight=5, learning_rate=0.0002, per_device_train_batch_size=4, warmup_ratio=0.2/events.out.tfevents.1725049026.23668649e3db +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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<br/>
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# Train Dataset
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Trained on 226,
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- Num Samples: `396,000`
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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=5, loss_fn=raw_mse, layer_mapper=layer-2, projector=orthogonal))
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```
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# Hyperparameters
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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, projector=orthogonal))`
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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.
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- Pytorch 2.4.0+cu121
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- Datasets 2.21.0
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# Resource Usage Comparison
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- VRAM Use: 7.4155 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 226,128,512 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.
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- Num Samples: `396,000`
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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=5, loss_fn=raw_mse, layer_mapper=layer-2, norm=layernorm, projector=orthogonal))
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```
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# Hyperparameters
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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=orthogonal))`
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- train_embeddings: `True`
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- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7f057c8fdb40>`
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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: `0.2`
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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 2.4.0+cu121
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- Datasets 2.21.0
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config.json
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}
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},
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.
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"use_cache": true,
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"vocab_size": 50257
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}
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}
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},
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.2",
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"use_cache": true,
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"vocab_size": 50257
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}
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generation_config.json
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"_from_model_config": true,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"transformers_version": "4.44.
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}
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"_from_model_config": true,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"transformers_version": "4.44.2"
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}
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logs/attn_norm=layernorm, attn_projector=orthogonal, attn_weight=5, learning_rate=0.0001, per_device_train_batch_size=4, warmup_ratio=0.2/completed.flag
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logs/attn_norm=layernorm, attn_projector=orthogonal, attn_weight=5, learning_rate=0.0002, per_device_train_batch_size=4, warmup_ratio=0.2/events.out.tfevents.1725049026.23668649e3db
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model.safetensors
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training_args.bin
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