fabrizziomcl

nanoballena-cpt-es-dryrun

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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: 0.0006
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.03
training_steps: 50

Training results

Framework versions

Transformers 5.12.0
Pytorch 2.12.0+cu130
Datasets 5.0.0
Tokenizers 0.22.2

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Model Details

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fabrizziomcl

Model Tree

Base

HuggingFaceTB/nanowhale-100m-base

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: 0.0006
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 0.03
training_steps: 50

Training results

Framework versions

Transformers 5.12.0
Pytorch 2.12.0+cu130
Datasets 5.0.0
Tokenizers 0.22.2