This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on the trl-lib/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.6491
Rewards/chosen: -0.1814
Rewards/rejected: -0.2255
Rewards/accuracies: 0.5625
Rewards/margins: 0.0441
Logps/rejected: -419.1795
Logps/chosen: -335.9990
Logits/rejected: -1.1373
Logits/chosen: -1.0280

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: 3e-07
train_batch_size: 2
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 32
gradient_accumulation_steps: 8
total_train_batch_size: 512
total_eval_batch_size: 128
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
0.6411	0.8239	100	0.6494	-0.1752	-0.2195	0.5625	0.0443	-418.5782	-335.3811	-1.1582	-1.0463

Framework versions

Transformers 4.45.1
Pytorch 2.4.0+cu121
Datasets 3.0.0
Tokenizers 0.20.0

hxssgaa/llama-3-8b-dpo-full

llama-3-8b-dpo-full

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hxssgaa/
llama-3-8b-dpo-full