This model is a fine-tuned version of hZzy/qwen2.5-0.5b-sft-news-IFT on the hZzy/train_pairwise dataset. It achieves the following results on the evaluation set:

Loss: 12497.9082
Logps: -80.8773
Logits: -1.3459
Objective: 12906.1104
Dpo Loss: 12906.1104
Regularize: 12906.1104
Ranking Simple: 0.5258
Ranking Idealized: 0.5093
Ranking Idealized Expo: 0.5093

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-06
train_batch_size: 4
eval_batch_size: 4
seed: 42
distributed_type: multi-GPU
num_devices: 6
gradient_accumulation_steps: 12
total_train_batch_size: 288
total_eval_batch_size: 24
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	Logps	Logits	Objective	Dpo Loss	Regularize	Ranking Simple	Ranking Idealized	Ranking Idealized Expo
10470.1625	0.2834	50	11322.9775	-84.9886	-1.4586	11815.3389	11815.3389	11815.3389	0.5165	0.5093	0.5093
8558.2836	0.5668	100	12877.2754	-82.3385	-1.3720	13248.5166	13248.5166	13248.5166	0.5176	0.5093	0.5093
7521.95	0.8503	150	12664.2070	-80.5751	-1.3556	12926.7227	12926.7227	12926.7227	0.5227	0.5093	0.5093

Framework versions

Transformers 4.42.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

hZzy/qwen2.5-0.5b-expo-DPO-EXPERIMENT-1W-5e6

qwen2.5-0.5b-expo-DPO-EXPERIMENT-1W

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hZzy/
qwen2.5-0.5b-expo-DPO-EXPERIMENT-1W-5e6