qwen2.5-0.5b-expo-DPO-noES3-0.1

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: 0.7566
• Logps: -117.9870
• Logits: -1.9778
• Objective: 0.7537
• Dpo Loss: 0.7537
• Regularize: 0.7537
• Ranking Simple: 0.5595
• Wo Beta: 9.1042

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Logps	Logits	Objective	Dpo Loss	Regularize	Ranking Simple	Wo Beta
0.6316	0.1417	50	0.6807	-90.3282	-1.5879	0.6825	0.6825	0.6825	0.5342	7.8619
0.5922	0.2834	100	0.6793	-95.8152	-1.7964	0.6819	0.6819	0.6819	0.5487	7.7077
0.5002	0.4251	150	0.6815	-96.2024	-1.5951	0.6749	0.6749	0.6749	0.5497	7.4380
0.4735	0.5668	200	0.6951	-98.9176	-1.7564	0.6911	0.6911	0.6911	0.5569	7.5241
0.4626	0.7085	250	0.6976	-93.4775	-1.7986	0.6945	0.6945	0.6945	0.5580	7.9027
0.4214	0.8503	300	0.6931	-104.4337	-2.0138	0.6865	0.6865	0.6865	0.5616	7.5814
0.3652	0.9920	350	0.7074	-102.8306	-1.9094	0.6984	0.6984	0.6984	0.5559	7.8344
0.2206	1.1337	400	0.7347	-113.6048	-2.0909	0.7296	0.7296	0.7296	0.5502	8.6751
0.2202	1.2754	450	0.7463	-115.7782	-1.9911	0.7433	0.7433	0.7433	0.5512	8.9123
0.2366	1.4171	500	0.7444	-114.7710	-2.0464	0.7387	0.7387	0.7387	0.5518	8.8630
0.1989	1.5588	550	0.7553	-118.7775	-2.0168	0.7519	0.7519	0.7519	0.5595	8.9846
0.1952	1.7005	600	0.7544	-117.4880	-1.9707	0.7513	0.7513	0.7513	0.5595	9.0297
0.2252	1.8422	650	0.7560	-117.8008	-1.9748	0.7529	0.7529	0.7529	0.5585	9.0926
0.199	1.9839	700	0.7566	-117.9869	-1.9778	0.7537	0.7537	0.7537	0.5595	9.1042

Framework versions

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