This model is a fine-tuned version of meta-llama/Meta-Llama-3.1-8B-Instruct on the simonycl/llama-3.1-metamath_subset_65k-annotate dataset. It achieves the following results on the evaluation set:

Loss: 0.5772
Rewards/chosen: -1.7008
Rewards/rejected: -2.0716
Rewards/accuracies: 0.6794
Rewards/margins: 0.3707
Logps/rejected: -481.0541
Logps/chosen: -432.9763
Logits/rejected: -2.4284
Logits/chosen: -2.5593

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-07
train_batch_size: 2
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 16
total_train_batch_size: 128
total_eval_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.347	0.7887	400	0.5772	-1.7008	-2.0716	0.6794	0.3707	-481.0541	-432.9763	-2.4284	-2.5593

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

simonycl/llama-3.1-8b-instruct-metamath-agg-judge

llama-3.1-8b-instruct-metamath-agg-judge

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

simonycl/
llama-3.1-8b-instruct-metamath-agg-judge