qwen2.5-0.5b-expo-L2EXPO-ES-10

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

• Loss: 38.5263
• Logps: -75.2715
• Logits: -0.8236
• Objective: 37.4366
• Dpo Loss: 18.9116
• Regularize: 37.4366
• Ranking Simple: 0.5295
• Ranking Idealized: 0.5212
• Ranking Idealized Expo: 0.5212
• Wo Beta: 14.6816

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
4.4544	0.1417	50	3.0769	-1.4000	-90.0695	6.1132	6.2354	0.5212	0.5212	0.5243	6.2354	16.0705
17.3779	0.2834	100	7.9374	-1.3238	-85.5257	16.1760	16.0037	0.5212	0.5212	0.5259	16.0037	15.7780
28.1478	0.4251	150	14.7239	-1.0824	-82.4808	28.7309	28.1308	0.5212	0.5212	0.5228	28.1308	15.4096
35.2522	0.5668	200	18.9116	-0.8236	-75.2715	38.5263	37.4366	0.5212	0.5212	0.5295	37.4366	14.6816
37.8556	0.7085	250	22.7495	-0.6024	-76.2798	44.8164	44.5795	0.5212	0.5212	0.5223	44.5795	14.3182
36.0351	0.8503	300	22.1457	-0.7057	-79.1833	44.3831	43.8777	0.5212	0.5212	0.5254	43.8777	14.2675
32.9882	0.9920	350	23.0098	-0.6345	-80.3166	46.6946	45.5953	0.5212	0.5212	0.5248	45.5953	14.1690
30.7247	1.1337	400	48.3805	-82.4111	-0.4810	48.0656	24.6183	48.0656	0.5166	0.5212	0.5212	14.1059
29.6491	1.2754	450	48.5237	-81.5285	-0.5861	48.8411	24.9495	48.8411	0.5243	0.5212	0.5212	14.4793
28.3933	1.4171	500	47.8150	-79.8843	-0.5585	47.9210	24.8156	47.9210	0.5212	0.5212	0.5212	14.3458
26.3026	1.5588	550	48.0081	-79.5567	-0.5594	48.2215	24.4583	48.2215	0.5228	0.5212	0.5212	14.1587
25.1162	1.7005	600	49.4271	-79.4245	-0.4875	49.7428	25.2219	49.7428	0.5259	0.5212	0.5212	14.1923

Framework versions

• Transformers 4.42.0
• Pytorch 2.3.0+cu121
• Datasets 3.2.0
• Tokenizers 0.19.1