This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the openmathinstruct2_cot_20k_train dataset. It achieves the following results on the evaluation set:

Loss: 0.7634

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: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 16
total_eval_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss
0.8177	0.4808	500	0.7823
0.7708	0.9615	1000	0.7572
0.5513	1.4423	1500	0.7693
0.5059	1.9231	2000	0.7637

Framework versions

Transformers 4.45.2
Pytorch 2.3.0+cu121
Datasets 2.21.0
Tokenizers 0.20.1

Lorraine013/openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2

openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Lorraine013/
openmathinstruct2-llama-3.1-8B-Instruct-lr5-ep2