This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the YuchenLi01/MATH_Qwen2.5-1.5BInstruct_DPO_MoreUniqueResponseNoGTv2 dataset. It achieves the following results on the evaluation set:

Loss: 0.5373
Rewards/chosen: -0.4144
Rewards/rejected: -1.8998
Rewards/accuracies: 0.7340
Rewards/margins: 1.4854
Logps/rejected: -58.7794
Logps/chosen: -44.9520
Logits/rejected: -2.1590
Logits/chosen: -2.2995

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: 8
total_train_batch_size: 32
total_eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Logits/chosen	Logits/rejected	Logps/chosen	Logps/rejected	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.7291	0.0060	20	-2.2839	-2.1667	-44.5204	-56.8603	0.7265	0.4746	-0.0080	-0.0210	0.0130
0.6848	0.0120	40	-2.2871	-2.1693	-44.5138	-56.8637	0.7228	0.4960	-0.0014	-0.0110	0.0096
0.738	0.0180	60	-2.2893	-2.1717	-44.5182	-56.8755	0.7245	0.5040	-0.0058	-0.0037	-0.0021
0.8261	0.0240	80	-2.2897	-2.1718	-44.5406	-56.8701	0.7230	0.4840	-0.0283	-0.0315	0.0032
0.7049	0.0300	100	-2.2855	-2.1676	-44.5247	-56.9012	0.7206	0.4947	-0.0123	0.0156	-0.0279
0.7746	0.0360	120	-2.2848	-2.1670	-44.5321	-56.9030	0.7215	0.5013	-0.0197	0.0100	-0.0297
0.7032	0.0420	140	-2.2821	-2.1642	-44.5492	-56.9150	0.7168	0.5040	-0.0368	0.0049	-0.0417
0.6695	0.0480	160	-2.2801	-2.1619	-44.5614	-56.9801	0.7058	0.5495	-0.0490	0.0578	-0.1068
0.6458	0.0540	180	-2.2777	-2.1588	-44.5593	-56.9735	0.7072	0.5441	-0.0469	0.0532	-0.1001
0.7211	0.0600	200	-2.2810	-2.1612	-44.6140	-57.0562	0.6916	0.5762	-0.1017	0.0812	-0.1829
0.6591	0.0660	220	0.6895	-0.0710	-0.1987	0.5775	0.1277	-57.0783	-44.6086	-2.1531	-2.2732
0.6036	0.0720	240	0.6858	-0.0897	-0.2607	0.5762	0.1710	-57.1402	-44.6273	-2.1598	-2.2810
0.6814	0.0780	260	0.6696	-0.0913	-0.3278	0.6016	0.2365	-57.2073	-44.6288	-2.1537	-2.2759
0.6837	0.0840	280	0.6625	-0.1462	-0.3878	0.6230	0.2416	-57.2673	-44.6838	-2.1504	-2.2729
0.6117	0.0900	300	0.6541	-0.0738	-0.3823	0.6297	0.3085	-57.2618	-44.6114	-2.1533	-2.2769
0.669	0.0960	320	0.6442	0.0061	-0.3176	0.6377	0.3237	-57.1972	-44.5314	-2.1626	-2.2873
0.583	0.1019	340	0.6447	-0.0856	-0.4312	0.6404	0.3455	-57.3107	-44.6232	-2.1547	-2.2799
0.5654	0.1079	360	0.6353	-0.1420	-0.5727	0.6658	0.4307	-57.4522	-44.6796	-2.1527	-2.2794
0.568	0.1139	380	0.6292	0.0082	-0.4383	0.6618	0.4465	-57.3178	-44.5294	-2.1664	-2.2942
0.5304	0.1199	400	0.6213	0.0454	-0.4712	0.6725	0.5166	-57.3507	-44.4921	-2.1627	-2.2910
0.5035	0.1259	420	0.6208	0.0784	-0.4786	0.6885	0.5570	-57.3581	-44.4592	-2.1681	-2.2973
0.5953	0.1319	440	0.6156	0.0266	-0.5164	0.6791	0.5430	-57.3960	-44.5109	-2.1651	-2.2949
0.6721	0.1379	460	0.6141	-0.0717	-0.6730	0.6872	0.6013	-57.5526	-44.6093	-2.1553	-2.2855
0.6381	0.1439	480	0.6074	-0.0999	-0.7249	0.6725	0.6250	-57.6044	-44.6374	-2.1532	-2.2838
0.5607	0.1499	500	0.6092	-0.0985	-0.7370	0.6898	0.6385	-57.6165	-44.6361	-2.1585	-2.2899
0.6352	0.1559	520	0.6102	-0.0134	-0.7123	0.6872	0.6989	-57.5918	-44.5509	-2.1696	-2.3020
0.6601	0.1619	540	0.6091	-0.0986	-0.7803	0.6832	0.6817	-57.6598	-44.6362	-2.1609	-2.2935
0.5776	0.1679	560	0.6048	-0.1802	-0.8749	0.6885	0.6947	-57.7545	-44.7178	-2.1510	-2.2829
0.4806	0.1739	580	0.5989	-0.0128	-0.6958	0.6952	0.6831	-57.5754	-44.5503	-2.1652	-2.2977
0.6933	0.1799	600	0.5970	0.0327	-0.6716	0.7045	0.7043	-57.5511	-44.5048	-2.1671	-2.3001
0.5323	0.1859	620	0.5958	0.0160	-0.7278	0.6858	0.7438	-57.6073	-44.5215	-2.1676	-2.3019
0.5477	0.1919	640	0.5946	0.1690	-0.5606	0.7045	0.7296	-57.4402	-44.3685	-2.1765	-2.3109
0.6585	0.1979	660	0.5962	0.0598	-0.6520	0.6832	0.7117	-57.5315	-44.4778	-2.1682	-2.3029
0.5485	0.2039	680	0.5933	0.1119	-0.6337	0.7032	0.7456	-57.5132	-44.4257	-2.1761	-2.3110
0.5765	0.2099	700	0.6049	0.1645	-0.6431	0.7005	0.8076	-57.5226	-44.3731	-2.1780	-2.3138
0.4799	0.2159	720	0.6018	0.1444	-0.6378	0.6845	0.7822	-57.5174	-44.3932	-2.1812	-2.3170
0.5287	0.2219	740	0.5965	0.2839	-0.5356	0.6845	0.8195	-57.4152	-44.2537	-2.1909	-2.3270
0.4884	0.2279	760	0.5881	-0.0027	-0.8259	0.6858	0.8233	-57.7055	-44.5402	-2.1620	-2.2977
0.8514	0.2339	780	0.5866	-0.1486	-1.0444	0.7139	0.8958	-57.9239	-44.6861	-2.1426	-2.2779
0.6214	0.2399	800	0.5836	-0.2221	-1.1080	0.6952	0.8859	-57.9875	-44.7596	-2.1479	-2.2835
0.5172	0.2459	820	0.5869	-0.1074	-0.9923	0.7099	0.8850	-57.8718	-44.6449	-2.1582	-2.2938
0.4711	0.2519	840	0.5849	-0.2542	-1.1450	0.7099	0.8907	-58.0245	-44.7918	-2.1413	-2.2762
0.7469	0.2579	860	0.5852	-0.0856	-0.9371	0.7086	0.8515	-57.8167	-44.6231	-2.1657	-2.3014
0.469	0.2639	880	0.5760	-0.2586	-1.1437	0.7032	0.8851	-58.0232	-44.7962	-2.1416	-2.2761
0.5544	0.2699	900	0.5768	-0.5459	-1.4264	0.6965	0.8805	-58.3060	-45.0835	-2.1224	-2.2564
0.4741	0.2759	920	0.5786	-0.0623	-0.9692	0.7086	0.9070	-57.8488	-44.5998	-2.1644	-2.3000
0.5469	0.2819	940	0.5764	-0.1955	-1.0870	0.7072	0.8915	-57.9665	-44.7331	-2.1566	-2.2916
0.5406	0.2879	960	0.5679	-0.2866	-1.2454	0.7112	0.9588	-58.1249	-44.8242	-2.1399	-2.2741
0.6933	0.2939	980	0.5740	-0.2874	-1.2353	0.7166	0.9479	-58.1149	-44.8250	-2.1391	-2.2734
0.4695	0.2999	1000	0.5590	-0.2671	-1.2448	0.7179	0.9777	-58.1243	-44.8046	-2.1418	-2.2766
0.7882	0.3058	1020	0.5670	-0.0953	-1.0910	0.7112	0.9957	-57.9705	-44.6328	-2.1612	-2.2964
0.4208	0.3118	1040	0.5623	-0.1584	-1.1983	0.7059	1.0398	-58.0778	-44.6960	-2.1570	-2.2921
0.4874	0.3178	1060	0.5655	-0.2882	-1.3783	0.7193	1.0901	-58.2578	-44.8257	-2.1507	-2.2866
0.6674	0.3238	1080	0.5589	-0.3916	-1.5032	0.7193	1.1116	-58.3827	-44.9291	-2.1378	-2.2739
0.4281	0.3298	1100	0.5612	-0.4694	-1.5722	0.7072	1.1028	-58.4517	-45.0070	-2.1394	-2.2753
0.4614	0.3358	1120	0.5631	-0.3489	-1.4640	0.7126	1.1151	-58.3435	-44.8864	-2.1465	-2.2826
0.5335	0.3418	1140	0.5555	-0.3036	-1.4296	0.7126	1.1260	-58.3091	-44.8411	-2.1457	-2.2827
0.5651	0.3478	1160	0.5579	-0.2463	-1.4118	0.7179	1.1656	-58.2914	-44.7838	-2.1509	-2.2887
0.563	0.3538	1180	0.5580	-0.3329	-1.4682	0.7032	1.1352	-58.3477	-44.8705	-2.1379	-2.2746
0.6401	0.3598	1200	0.5601	-0.1982	-1.3582	0.7206	1.1600	-58.2377	-44.7357	-2.1506	-2.2875
0.406	0.3658	1220	0.5536	-0.1809	-1.3450	0.6992	1.1641	-58.2245	-44.7185	-2.1601	-2.2971
0.7775	0.3718	1240	0.5566	-0.2128	-1.4310	0.7179	1.2182	-58.3105	-44.7504	-2.1534	-2.2908
0.6181	0.3778	1260	0.5628	-0.5730	-1.7615	0.7139	1.1885	-58.6410	-45.1105	-2.1293	-2.2663
0.5988	0.3838	1280	0.5710	-0.5662	-1.7811	0.7166	1.2149	-58.6607	-45.1037	-2.1313	-2.2682
0.3884	0.3898	1300	0.5668	-0.2991	-1.4983	0.7152	1.1992	-58.3779	-44.8367	-2.1530	-2.2904
0.4156	0.3958	1320	0.5669	-0.5498	-1.7991	0.7286	1.2492	-58.6786	-45.0874	-2.1365	-2.2731
0.3103	0.4018	1340	0.5619	-0.4249	-1.6448	0.7112	1.2200	-58.5244	-44.9624	-2.1447	-2.2821
0.4455	0.4078	1360	0.5658	-0.4876	-1.7084	0.7072	1.2208	-58.5879	-45.0251	-2.1432	-2.2811
0.5354	0.4138	1380	0.5588	-0.4171	-1.6939	0.7233	1.2768	-58.5735	-44.9547	-2.1500	-2.2883
0.3858	0.4198	1400	0.5590	-0.5162	-1.8287	0.7313	1.3126	-58.7083	-45.0537	-2.1433	-2.2812
0.6049	0.4258	1420	0.5614	-0.4740	-1.8276	0.7447	1.3536	-58.7071	-45.0115	-2.1476	-2.2861
0.671	0.4318	1440	0.5574	-0.3845	-1.7527	0.7340	1.3682	-58.6322	-44.9221	-2.1634	-2.3029
0.6687	0.4378	1460	0.5607	-0.5440	-1.9107	0.7273	1.3667	-58.7903	-45.0816	-2.1524	-2.2911
0.435	0.4438	1480	0.5621	-0.6818	-2.0400	0.7179	1.3582	-58.9195	-45.2194	-2.1355	-2.2733
0.5685	0.4498	1500	0.5610	-0.5662	-1.9505	0.7299	1.3844	-58.8301	-45.1038	-2.1460	-2.2842
0.5898	0.4558	1520	0.5606	-0.3553	-1.7632	0.7420	1.4078	-58.6427	-44.8929	-2.1691	-2.3083
0.6662	0.4618	1540	0.5601	-0.3791	-1.8558	0.7366	1.4766	-58.7353	-44.9167	-2.1732	-2.3135
0.3823	0.4678	1560	0.5607	-0.4463	-1.8739	0.7340	1.4275	-58.7534	-44.9839	-2.1631	-2.3028
0.5838	0.4738	1580	0.5610	-0.2651	-1.6597	0.7326	1.3946	-58.5393	-44.8027	-2.1765	-2.3163
0.4693	0.4798	1600	0.5639	-0.3246	-1.7396	0.7299	1.4150	-58.6191	-44.8622	-2.1756	-2.3155
0.3254	0.4858	1620	0.5579	-0.4645	-1.8930	0.7487	1.4284	-58.7725	-45.0021	-2.1551	-2.2944
0.4764	0.4918	1640	0.5548	-0.3863	-1.8092	0.7353	1.4229	-58.6888	-44.9238	-2.1629	-2.3022
0.6371	0.4978	1660	0.5555	-0.3064	-1.6736	0.7166	1.3672	-58.5532	-44.8440	-2.1718	-2.3108
0.3995	0.5037	1680	0.5532	-0.3046	-1.7258	0.7433	1.4212	-58.6053	-44.8422	-2.1654	-2.3048
0.4857	0.5097	1700	0.5560	-0.4484	-1.8684	0.7353	1.4200	-58.7479	-44.9860	-2.1534	-2.2926
0.4943	0.5157	1720	0.5549	-0.5572	-1.9759	0.7246	1.4187	-58.8554	-45.0948	-2.1474	-2.2867
0.3548	0.5217	1740	0.5583	-0.5077	-1.9194	0.7219	1.4117	-58.7989	-45.0453	-2.1547	-2.2944
0.5436	0.5277	1760	0.5538	-0.3911	-1.8133	0.7206	1.4221	-58.6928	-44.9287	-2.1615	-2.3009
0.5254	0.5337	1780	0.5597	-0.4025	-1.7827	0.7152	1.3801	-58.6622	-44.9401	-2.1638	-2.3031
0.6327	0.5397	1800	0.5570	-0.3423	-1.7646	0.7233	1.4223	-58.6441	-44.8798	-2.1680	-2.3082
0.5497	0.5457	1820	0.5607	-0.2746	-1.6845	0.7219	1.4099	-58.5640	-44.8121	-2.1764	-2.3166
0.4722	0.5517	1840	0.5608	-0.2190	-1.6021	0.7286	1.3831	-58.4817	-44.7565	-2.1820	-2.3217
0.4388	0.5577	1860	0.5511	-0.2535	-1.6785	0.7286	1.4250	-58.5581	-44.7911	-2.1735	-2.3130
0.5964	0.5637	1880	0.5502	-0.3695	-1.7796	0.7126	1.4100	-58.6591	-44.9071	-2.1653	-2.3045
0.3551	0.5697	1900	0.5487	-0.4635	-1.8912	0.7286	1.4276	-58.7707	-45.0011	-2.1522	-2.2909
0.6526	0.5757	1920	0.5508	-0.4139	-1.8344	0.7246	1.4205	-58.7139	-44.9515	-2.1552	-2.2942
0.7552	0.5817	1940	0.5512	-0.4846	-1.8929	0.7286	1.4083	-58.7724	-45.0222	-2.1504	-2.2893
0.6127	0.5877	1960	0.5535	-0.4042	-1.8043	0.7299	1.4001	-58.6838	-44.9417	-2.1602	-2.2994
0.5269	0.5937	1980	0.5530	-0.3085	-1.7349	0.7433	1.4264	-58.6145	-44.8460	-2.1715	-2.3116
0.4157	0.5997	2000	0.5528	-0.2603	-1.6849	0.7193	1.4246	-58.5644	-44.7978	-2.1742	-2.3139
0.4349	0.6057	2020	0.5532	-0.3441	-1.7591	0.7259	1.4149	-58.6386	-44.8817	-2.1638	-2.3032
0.4809	0.6117	2040	0.5532	-0.4741	-1.8853	0.7273	1.4112	-58.7648	-45.0116	-2.1597	-2.2988
0.5702	0.6177	2060	0.5557	-0.5300	-1.9318	0.7206	1.4018	-58.8114	-45.0676	-2.1556	-2.2944
0.3962	0.6237	2080	0.5534	-0.4741	-1.9026	0.7326	1.4284	-58.7821	-45.0117	-2.1597	-2.2986
0.5157	0.6297	2100	0.5514	-0.4082	-1.8621	0.7219	1.4539	-58.7416	-44.9457	-2.1663	-2.3057
0.6062	0.6357	2120	0.5489	-0.4892	-1.9210	0.7259	1.4318	-58.8005	-45.0267	-2.1580	-2.2971
0.5805	0.6417	2140	0.5506	-0.5743	-2.0036	0.7313	1.4293	-58.8832	-45.1119	-2.1521	-2.2912
0.4468	0.6477	2160	0.5480	-0.4742	-1.9282	0.7380	1.4539	-58.8077	-45.0118	-2.1569	-2.2961
0.4592	0.6537	2180	0.5470	-0.4579	-1.9294	0.7380	1.4716	-58.8090	-44.9954	-2.1610	-2.3007
0.4995	0.6597	2200	0.5537	-0.4685	-1.9381	0.7233	1.4695	-58.8176	-45.0061	-2.1621	-2.3016
0.4452	0.6657	2220	0.5499	-0.3967	-1.8687	0.7286	1.4720	-58.7483	-44.9342	-2.1665	-2.3063
0.4684	0.6717	2240	0.5472	-0.2935	-1.7729	0.7286	1.4794	-58.6524	-44.8310	-2.1763	-2.3166
0.4355	0.6777	2260	0.5512	-0.3032	-1.7494	0.7273	1.4462	-58.6289	-44.8407	-2.1728	-2.3128
0.4648	0.6837	2280	0.5438	-0.3819	-1.8558	0.7340	1.4739	-58.7353	-44.9195	-2.1650	-2.3048
0.4073	0.6897	2300	0.5416	-0.4445	-1.9273	0.7353	1.4829	-58.8069	-44.9820	-2.1590	-2.2986
0.3451	0.6957	2320	0.5500	-0.4966	-1.9387	0.7273	1.4421	-58.8182	-45.0342	-2.1546	-2.2942
0.5987	0.7016	2340	0.5495	-0.3648	-1.8353	0.7233	1.4705	-58.7149	-44.9023	-2.1697	-2.3098
0.5222	0.7076	2360	0.5439	-0.3629	-1.8259	0.7286	1.4630	-58.7054	-44.9005	-2.1665	-2.3063
0.3971	0.7136	2380	0.5496	-0.2985	-1.7990	0.7393	1.5005	-58.6786	-44.8360	-2.1752	-2.3154
0.5132	0.7196	2400	0.5450	-0.3154	-1.7928	0.7433	1.4774	-58.6723	-44.8530	-2.1745	-2.3148
0.5456	0.7256	2420	0.5473	-0.3452	-1.8211	0.7233	1.4759	-58.7006	-44.8827	-2.1671	-2.3072
0.5893	0.7316	2440	0.5428	-0.3562	-1.8190	0.7313	1.4629	-58.6986	-44.8937	-2.1665	-2.3065
0.26	0.7376	2460	0.5399	-0.3181	-1.7823	0.7313	1.4642	-58.6619	-44.8557	-2.1654	-2.3055
0.5268	0.7436	2480	0.5446	-0.3627	-1.8521	0.7433	1.4894	-58.7316	-44.9002	-2.1640	-2.3040
0.6663	0.7496	2500	0.5453	-0.3867	-1.8816	0.7406	1.4949	-58.7612	-44.9243	-2.1618	-2.3016
0.4173	0.7556	2520	0.5372	-0.4807	-1.9412	0.7420	1.4605	-58.8207	-45.0183	-2.1517	-2.2912
0.5192	0.7616	2540	0.5412	-0.4859	-1.9418	0.7420	1.4559	-58.8214	-45.0235	-2.1509	-2.2905
0.4405	0.7676	2560	0.5442	-0.4695	-1.9568	0.7393	1.4874	-58.8364	-45.0070	-2.1509	-2.2905
0.5601	0.7736	2580	0.5391	-0.4597	-1.9504	0.7313	1.4907	-58.8300	-44.9973	-2.1567	-2.2967
0.2472	0.7796	2600	0.5425	-0.4773	-1.9279	0.7326	1.4507	-58.8075	-45.0148	-2.1519	-2.2914
0.578	0.7856	2620	0.5437	-0.4561	-1.9401	0.7447	1.4840	-58.8196	-44.9937	-2.1571	-2.2973
0.3509	0.7916	2640	0.5433	-0.4294	-1.9287	0.7286	1.4993	-58.8082	-44.9669	-2.1646	-2.3048
0.4184	0.7976	2660	0.5428	-0.4130	-1.9064	0.7513	1.4934	-58.7860	-44.9505	-2.1624	-2.3026
0.3575	0.8036	2680	0.5459	-0.4280	-1.9096	0.7326	1.4816	-58.7891	-44.9655	-2.1605	-2.3007
0.6414	0.8096	2700	0.5444	-0.3947	-1.8890	0.7246	1.4943	-58.7686	-44.9323	-2.1667	-2.3072
0.3501	0.8156	2720	0.5424	-0.3892	-1.8607	0.7259	1.4716	-58.7403	-44.9267	-2.1668	-2.3072
0.3903	0.8216	2740	0.5425	-0.4224	-1.8859	0.7340	1.4635	-58.7655	-44.9600	-2.1631	-2.3033
0.4217	0.8276	2760	0.5426	-0.4113	-1.8610	0.7166	1.4497	-58.7405	-44.9489	-2.1673	-2.3077
0.6227	0.8336	2780	0.5423	-0.4355	-1.8973	0.7299	1.4618	-58.7769	-44.9731	-2.1640	-2.3042
0.4151	0.8396	2800	0.5404	-0.4068	-1.8552	0.7286	1.4484	-58.7348	-44.9444	-2.1627	-2.3027
0.3177	0.8456	2820	0.5401	-0.4114	-1.9078	0.7380	1.4964	-58.7873	-44.9489	-2.1597	-2.2997
0.4506	0.8516	2840	0.5412	-0.4089	-1.9111	0.7447	1.5022	-58.7906	-44.9465	-2.1568	-2.2969
0.4208	0.8576	2860	0.5413	-0.4089	-1.8687	0.7406	1.4598	-58.7482	-44.9464	-2.1592	-2.2990
0.5825	0.8636	2880	0.5417	-0.3859	-1.8753	0.7313	1.4894	-58.7548	-44.9235	-2.1578	-2.2978
0.3224	0.8696	2900	0.5401	-0.3852	-1.8855	0.7473	1.5003	-58.7650	-44.9228	-2.1586	-2.2988
0.4393	0.8756	2920	0.5388	-0.4133	-1.8711	0.7299	1.4578	-58.7506	-44.9508	-2.1594	-2.2997
0.4102	0.8816	2940	0.5418	-0.4315	-1.9297	0.7513	1.4982	-58.8093	-44.9691	-2.1566	-2.2966
0.3763	0.8876	2960	0.5374	-0.4262	-1.9210	0.7380	1.4948	-58.8006	-44.9638	-2.1581	-2.2981
0.3902	0.8936	2980	0.5403	-0.3949	-1.9049	0.7447	1.5100	-58.7845	-44.9324	-2.1653	-2.3058
0.447	0.8996	3000	0.5365	-0.3871	-1.9045	0.7380	1.5173	-58.7840	-44.9247	-2.1636	-2.3041
0.5848	0.9055	3020	0.5386	-0.3979	-1.9016	0.7299	1.5037	-58.7812	-44.9355	-2.1626	-2.3031
0.3782	0.9115	3040	0.5349	-0.4300	-1.9187	0.7460	1.4886	-58.7982	-44.9676	-2.1615	-2.3020
0.3947	0.9175	3060	0.5403	-0.4178	-1.9074	0.7366	1.4896	-58.7870	-44.9554	-2.1630	-2.3038
0.3656	0.9235	3080	0.5383	-0.4113	-1.9006	0.7406	1.4893	-58.7802	-44.9489	-2.1575	-2.2976
0.5638	0.9295	3100	0.5373	-0.4196	-1.9184	0.7313	1.4987	-58.7979	-44.9572	-2.1559	-2.2959
0.3273	0.9355	3120	0.5391	-0.4388	-1.9081	0.7366	1.4693	-58.7877	-44.9764	-2.1561	-2.2962
0.3505	0.9415	3140	0.5380	-0.4132	-1.8992	0.75	1.4860	-58.7788	-44.9508	-2.1574	-2.2977
0.5039	0.9475	3160	0.5349	-0.4262	-1.9045	0.7406	1.4783	-58.7840	-44.9637	-2.1567	-2.2970
0.4245	0.9535	3180	0.5397	-0.4281	-1.9163	0.7246	1.4882	-58.7958	-44.9657	-2.1556	-2.2960
0.5275	0.9595	3200	0.5382	-0.4241	-1.9178	0.7406	1.4937	-58.7974	-44.9617	-2.1563	-2.2965
0.2625	0.9655	3220	0.5348	-0.4192	-1.9261	0.7340	1.5069	-58.8056	-44.9568	-2.1620	-2.3027
0.4075	0.9715	3240	0.5372	-0.4057	-1.9098	0.7166	1.5041	-58.7893	-44.9432	-2.1577	-2.2981
0.4119	0.9775	3260	0.5370	-0.4353	-1.9074	0.7393	1.4721	-58.7869	-44.9728	-2.1602	-2.3006
0.4395	0.9835	3280	0.5404	-0.4379	-1.9290	0.7206	1.4911	-58.8086	-44.9754	-2.1616	-2.3020
0.5961	0.9895	3300	0.5379	-0.4077	-1.9297	0.7513	1.5220	-58.8092	-44.9453	-2.1590	-2.2991
0.6383	0.9955	3320	0.5397	-0.4208	-1.9264	0.7326	1.5055	-58.8059	-44.9584	-2.1588	-2.2993

Framework versions

Transformers 4.45.2
Pytorch 2.5.1+cu121
Datasets 3.5.0
Tokenizers 0.20.3

YuchenLi01/generatedMoreUniqueResponseNoGTv2_Qwen2.5-1.5BInstruct_dpo_ebs32_lr5e-07_beta1.0_42

generatedMoreUniqueResponseNoGTv2_Qwen2.5-1.5BInstruct_dpo_ebs32_lr5e-07_beta1.0_42

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

YuchenLi01/
generatedMoreUniqueResponseNoGTv2_Qwen2.5-1.5BInstruct_dpo_ebs32_lr5e-07_beta1.0_42