qwen2.5-0.5b-expo-L2EXPO-W0-noES4-0.1

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: 179.2621
• Logps: -92.2613
• Logits: -1.4975
• Objective: 175.9752
• Dpo Loss: 0.6785
• Regularize: 0.3992
• Ranking Simple: 0.5280
• Ranking Idealized: 0.6025
• Ranking Idealized Expo: 0.5233
• Wo Beta: 16.5856

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: 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: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Logps	Logits	Objective	Dpo Loss	Regularize	Ranking Simple	Ranking Idealized	Ranking Idealized Expo	Wo Beta
182.5182	0.1417	50	182.5003	-90.8517	-1.4200	180.4893	0.6895	0.4093	0.5248	0.6025	0.5233	16.3100
159.305	0.2834	100	182.1522	-91.3531	-1.4622	180.5219	0.6860	0.4103	0.5311	0.6025	0.5233	16.3819
150.2379	0.4251	150	180.0575	-90.2469	-1.4576	177.1578	0.6806	0.4010	0.5331	0.6025	0.5233	16.6107
135.925	0.5668	200	179.9740	-91.1249	-1.4453	177.0413	0.6795	0.4006	0.5305	0.6025	0.5233	16.2687
130.7065	0.7085	250	181.5092	-91.6178	-1.5061	178.2784	0.6800	0.4049	0.5305	0.6025	0.5233	16.6407
109.74	0.8503	300	180.4924	-92.4236	-1.4760	178.1365	0.6815	0.4047	0.5305	0.6025	0.5233	16.4981
104.2663	0.9920	350	182.2591	-92.8005	-1.5066	178.8644	0.6808	0.4058	0.5290	0.6025	0.5233	16.5694
91.3585	1.1337	400	180.0295	-92.3854	-1.4789	177.7148	0.6800	0.4024	0.5280	0.6025	0.5233	16.5852
77.8925	1.2754	450	179.2441	-92.7062	-1.4746	175.8475	0.6792	0.3989	0.5274	0.6025	0.5233	16.5269
73.5844	1.4171	500	180.3643	-93.2695	-1.4849	176.2332	0.6786	0.3994	0.5305	0.6025	0.5233	16.5003
74.752	1.5588	550	181.3646	-92.8892	-1.4832	177.2267	0.6795	0.4020	0.5274	0.6025	0.5233	16.5546
66.606	1.7005	600	179.4953	-91.6158	-1.4675	176.2793	0.6789	0.3999	0.5311	0.6025	0.5233	16.6183
65.4503	1.8422	650	180.1248	-91.8974	-1.5046	176.5553	0.6790	0.4003	0.5285	0.6025	0.5233	16.5373
62.3615	1.9839	700	179.3857	-91.5875	-1.4984	176.0021	0.6784	0.3992	0.5300	0.6025	0.5233	16.5863
48.9708	2.1256	750	179.8103	-92.1933	-1.4919	176.7028	0.6794	0.4011	0.5274	0.6025	0.5233	16.5884
51.9463	2.2674	800	179.2178	-92.0065	-1.4993	175.7036	0.6782	0.3986	0.5290	0.6025	0.5233	16.5689
44.3463	2.4091	850	179.1735	-92.2372	-1.4918	175.7777	0.6783	0.3988	0.5285	0.6025	0.5233	16.5682
44.3015	2.5508	900	179.1590	-92.1898	-1.4983	175.8240	0.6784	0.3990	0.5280	0.6025	0.5233	16.5905
43.4164	2.6925	950	179.2801	-92.2046	-1.4967	176.0408	0.6785	0.3993	0.5274	0.6025	0.5233	16.5891
43.6009	2.8342	1000	179.2791	-92.2705	-1.4978	175.9963	0.6785	0.3992	0.5280	0.6025	0.5233	16.5880
47.7054	2.9759	1050	179.2622	-92.2613	-1.4975	175.9752	0.6785	0.3992	0.5280	0.6025	0.5233	16.5856

Framework versions

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