This model is a fine-tuned version of google/gemma-2-9b-it on the jz666/gemma2-ultrafeedback-ppl-split dataset. It achieves the following results on the evaluation set:

Loss: 0.4532
Rewards/chosen: -2.7523
Rewards/rejected: -3.7825
Rewards/accuracies: 0.7705
Rewards/margins: 1.0302
Logps/rejected: -562.3438
Logps/chosen: -463.9622
Logits/rejected: -19.6359
Logits/chosen: -19.6258

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
0.486	0.8724	400	0.4543	-2.7276	-3.7515	0.7705	1.0239	-559.2502	-461.4991	-19.5816	-19.5679

Framework versions

Transformers 4.44.2
Pytorch 2.7.0+cu128
Datasets 2.18.0
Tokenizers 0.19.1

jz666/dpo-grad-acc-16-train_filtered_full

dpo-grad-acc-16-train_filtered_full

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jz666/
dpo-grad-acc-16-train_filtered_full