This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the argilla-warehouse/magpie-ultra-v1.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.5953

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: 1e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 4
total_train_batch_size: 128
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss
0.6117	0.9998	1458	0.6077
0.5287	1.9997	2916	0.5882
0.4775	2.9995	4374	0.5953

Framework versions

Transformers 4.45.0
Pytorch 2.4.1+cu121
Datasets 3.0.1
Tokenizers 0.20.0

argilla-warehouse/Llama-3.1-8B-MagPie-Ultra-500k

Llama-3.1-8B-MagPie-Ultra-500k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

argilla-warehouse/
Llama-3.1-8B-MagPie-Ultra-500k