This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the cpt_jazz_pretrain dataset. It achieves the following results on the evaluation set:

Loss: 1.5595

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: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 32
total_train_batch_size: 256
total_eval_batch_size: 8
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
lr_scheduler_warmup_ratio: 0.03
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss
1.5828	0.6461	200	1.5752

Framework versions

Transformers 4.57.1
Pytorch 2.9.1+cu128
Datasets 4.0.0
Tokenizers 0.22.1

ramzanniaz331/Llama-3.1-8B-full-pretrain-8192

full_pretrain

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ramzanniaz331/
Llama-3.1-8B-full-pretrain-8192