qwen2.5-0.5b-expo-L1EXPO-25-1

This model is a fine-tuned version of hZzy/qwen2.5-0.5b-sft3-25-1 on the hZzy/train_pairwise_all_new4 dataset. It achieves the following results on the evaluation set:

• Loss: 0.4894
• Objective: 0.4676
• Reward Accuracy: 0.6113
• Logp Accuracy: 0.6029
• Log Diff Policy: 40.0274
• Chosen Logps: -340.5497
• Rejected Logps: -380.5771
• Chosen Rewards: -0.2485
• Rejected Rewards: -0.2884
• Logits: -3.0576

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: 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: cosine
• 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
0.5053	0.1577	50	0.5071	0.4885	0.5341	0.5207	0.7811	-92.9844	-93.7655	-0.0009	-0.0016	-1.3904
0.502	0.3154	100	0.5061	0.4875	0.5660	0.5308	1.8720	-97.0731	-98.9451	-0.0050	-0.0067	-1.6907
0.5055	0.4731	150	0.5039	0.4851	0.5850	0.5515	4.6375	-127.4373	-132.0748	-0.0354	-0.0399	-1.9857
0.493	0.6307	200	0.4997	0.4801	0.5845	0.5749	12.2021	-205.9091	-218.1112	-0.1138	-0.1259	-2.3415
0.503	0.7884	250	0.4960	0.4758	0.5850	0.5710	18.8664	-231.5545	-250.4210	-0.1395	-0.1582	-2.6015
0.4819	0.9461	300	0.4926	0.4721	0.6018	0.5889	28.5350	-287.0227	-315.5577	-0.1950	-0.2233	-2.8512
0.4996	1.1038	350	0.4917	0.4701	0.6018	0.5861	34.2180	-325.4102	-359.6282	-0.2334	-0.2674	-2.9696
0.4858	1.2615	400	0.4903	0.4684	0.6023	0.5962	36.4981	-309.6466	-346.1447	-0.2176	-0.2539	-2.9164
0.4633	1.4192	450	0.4898	0.4680	0.6057	0.5962	38.4418	-334.3559	-372.7977	-0.2423	-0.2806	-3.0070
0.4827	1.5769	500	0.4898	0.4682	0.6085	0.5951	38.9854	-337.3905	-376.3759	-0.2453	-0.2842	-3.0316
0.4618	1.7346	550	0.4895	0.4677	0.6107	0.6029	39.8381	-339.5474	-379.3856	-0.2475	-0.2872	-3.0556
0.473	1.8922	600	0.4894	0.4676	0.6119	0.6018	40.0601	-340.9121	-380.9722	-0.2489	-0.2888	-3.0572

Framework versions

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