qing-yao

ppt-pythia-160m-uniform250-uniform-seed324-stage1

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README

License: apache-2.0

Model description

More information needed

Intended uses & limitations

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Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 16
eval_batch_size: 16
seed: 324
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_warmup_steps: 13
training_steps: 250

Training results

Framework versions

Transformers 5.4.0
Pytorch 2.8.0+cu128
Datasets 3.2.0
Tokenizers 0.22.1

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Model Details

Model Provider

qing-yao

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Base

EleutherAI/pythia-160m

Fine-tuned

this model

Input Modalities

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Output Modalities

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README

License: apache-2.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.001
train_batch_size: 16
eval_batch_size: 16
seed: 324
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine_with_min_lr
lr_scheduler_warmup_steps: 13
training_steps: 250

Training results

Framework versions

Transformers 5.4.0
Pytorch 2.8.0+cu128
Datasets 3.2.0
Tokenizers 0.22.1