This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the envbench_weave_2500 dataset. It achieves the following results on the evaluation set:

Loss: 0.3857
Accuracy: 0.8937
Num Input Tokens Seen: 93186816

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: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 4
total_train_batch_size: 64
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 0.1
num_epochs: 5.0

Training results

Framework versions

Transformers 4.52.4
Pytorch 2.6.0a0+df5bbc09d1.nv24.12
Datasets 3.6.0
Tokenizers 0.21.1

waleko/Llama-3.1-8B-SFT-envbench_weave_2500

Llama-3.1-8B-SFT-envbench_weave_2500

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

waleko/
Llama-3.1-8B-SFT-envbench_weave_2500