qwen2.5-0.5b-expo-L2EXPO-25-4

This model is a fine-tuned version of hZzy/qwen2.5-0.5b-sft3-25-2 on the hZzy/train_pairwise_all_new4 dataset. It achieves the following results on the evaluation set:

• Loss: 0.4787
• Objective: 0.4685
• Reward Accuracy: 0.6174
• Logp Accuracy: 0.6186
• Log Diff Policy: 86.7020
• Chosen Logps: -546.2890
• Rejected Logps: -632.9909
• Chosen Rewards: -0.4588
• Rejected Rewards: -0.5452
• Logits: -5.2345

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: 1e-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: constant_with_warmup
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Objective	Reward Accuracy	Logp Accuracy	Log Diff Policy	Chosen Logps	Rejected Logps	Chosen Rewards	Rejected Rewards	Logits
0.5032	0.1577	50	0.5111	0.5045	0.5475	0.5291	1.4376	-98.0006	-99.4382	-0.0105	-0.0116	-1.3005
0.5088	0.3154	100	0.5084	0.5013	0.5671	0.5570	5.7542	-157.0740	-162.8282	-0.0696	-0.0750	-1.6634
0.5169	0.4731	150	0.5000	0.4915	0.5895	0.5794	18.4406	-238.2272	-256.6678	-0.1508	-0.1689	-2.4207
0.4721	0.6307	200	0.4920	0.4808	0.5984	0.5984	35.5526	-324.8310	-360.3837	-0.2374	-0.2726	-3.1314
0.4783	0.7884	250	0.4854	0.4740	0.6079	0.6091	48.2612	-382.3873	-430.6486	-0.2949	-0.3428	-3.6091
0.4459	0.9461	300	0.4825	0.4709	0.6208	0.6141	56.4166	-438.2454	-494.6620	-0.3508	-0.4069	-4.3226
0.4457	1.1038	350	0.4803	0.4696	0.6247	0.6219	65.3183	-452.0122	-517.3304	-0.3645	-0.4295	-4.4589
0.4549	1.2615	400	0.4795	0.4683	0.6253	0.6202	71.3522	-470.1124	-541.4646	-0.3826	-0.4537	-4.7243
0.4227	1.4192	450	0.4778	0.4663	0.6270	0.6258	72.8114	-452.3585	-525.1699	-0.3649	-0.4374	-4.7772
0.4436	1.5769	500	0.4794	0.4674	0.6219	0.6158	79.5543	-541.1447	-620.6989	-0.4537	-0.5329	-5.1940
0.4133	1.7346	550	0.4770	0.4654	0.6214	0.6202	79.3433	-482.3657	-561.7089	-0.3949	-0.4739	-5.1004
0.414	1.8922	600	0.4772	0.4664	0.6292	0.6264	82.1284	-524.0090	-606.1374	-0.4365	-0.5183	-5.3084

Framework versions

• Transformers 4.42.0
• Pytorch 2.6.0+cu124
• Datasets 3.2.0
• Tokenizers 0.19.1