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: 0.6894
Logps: -99.3821
Logits: -1.7611
Objective: 0.6902
Dpo Loss: 0.6902
Regularize: 0.6902
Ranking Simple: 0.5305
Ranking Idealized: 0.8732
Ranking Idealized Expo: 0.5321
Wo Beta: 9.3575

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: 3
gradient_accumulation_steps: 12
total_train_batch_size: 144
total_eval_batch_size: 12
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5

Training results

Training Loss	Epoch	Step	Dpo Loss	Logits	Logps	Validation Loss	Objective	Ranking Idealized	Ranking Idealized Expo	Ranking Simple	Regularize	Wo Beta
0.6907	0.1417	50	0.6902	-1.7611	-99.3821	0.6894	0.6902	0.8732	0.5321	0.5305	0.6902	9.3575
0.6701	0.2834	100	0.6896	-1.8467	-153.5716	0.6837	0.6896	0.8732	0.5321	0.5518	0.6896	14.1741
0.637	0.4251	150	0.6765	-2.4614	-198.9269	0.6723	0.6765	0.8732	0.5321	0.5823	0.6765	17.2098
0.5833	0.5668	200	0.6780	-3.3478	-256.0312	0.6729	0.6780	0.8732	0.5321	0.5797	0.6780	21.7109
0.5439	0.7085	250	0.6858	-3.6269	-257.3546	0.6781	0.6858	0.8732	0.5321	0.5683	0.6858	22.9139
0.5077	0.8503	300	0.6685	-4.6100	-319.3935	0.6640	0.6685	0.8732	0.5321	0.5828	0.6685	23.6506
0.4786	0.9920	350	0.6867	-368.7014	-4.9192	0.6897	0.6897	0.6897	0.5751	0.8732	0.5321	27.7113
0.3619	1.1337	400	0.6961	-392.7018	-5.5801	0.6990	0.6990	0.6990	0.5849	0.8732	0.5321	32.0730
0.3679	1.2754	450	0.6843	-349.2029	-5.2450	0.6953	0.6953	0.6953	0.5885	0.8732	0.5321	31.3199
0.3662	1.4171	500	0.6858	-350.7137	-5.8233	0.6903	0.6903	0.6903	0.5890	0.8732	0.5321	30.2726
0.3485	1.5588	550	0.6901	-349.5348	-5.2492	0.6972	0.6972	0.6972	0.5719	0.8732	0.5321	31.1765

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-ES-0.01

qwen2.5-0.5b-expo-DPO-ES-0.01

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hZzy/
qwen2.5-0.5b-expo-DPO-ES-0.01