This model is a fine-tuned version of martimfasantos/tinyllama-1.1b-sum-sft-full_old on the openai/summarize_from_feedback dataset. It achieves the following results on the evaluation set:

Loss: 0.6901
Rewards/chosen: -0.0088
Rewards/rejected: -0.0152
Rewards/accuracies: 0.5892
Rewards/margins: 0.0064
Logps/rejected: -64.7009
Logps/chosen: -59.5896
Logits/rejected: -3.1105
Logits/chosen: -3.1162

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: 2e-08
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
gradient_accumulation_steps: 2
total_train_batch_size: 16
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	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.6932	0.0172	100	0.6932	0.0001	0.0002	0.4947	-0.0000	-63.1650	-58.7014	-3.1574	-3.1631
0.6932	0.0345	200	0.6932	0.0000	0.0002	0.4840	-0.0001	-63.1618	-58.7076	-3.1573	-3.1630
0.6932	0.0517	300	0.6932	-0.0000	0.0001	0.4842	-0.0001	-63.1720	-58.7121	-3.1574	-3.1631
0.6933	0.0689	400	0.6932	-0.0000	0.0000	0.4856	-0.0000	-63.1788	-58.7153	-3.1577	-3.1633
0.693	0.0861	500	0.6932	0.0001	0.0002	0.4847	-0.0001	-63.1648	-58.7040	-3.1576	-3.1633
0.6931	0.1034	600	0.6931	0.0000	0.0000	0.4961	0.0000	-63.1795	-58.7070	-3.1572	-3.1629
0.6932	0.1206	700	0.6932	0.0000	0.0001	0.4912	-0.0001	-63.1701	-58.7077	-3.1574	-3.1631
0.693	0.1378	800	0.6932	0.0002	0.0002	0.4909	-0.0000	-63.1604	-58.6950	-3.1576	-3.1633
0.6934	0.1551	900	0.6932	0.0001	0.0001	0.5060	-0.0000	-63.1695	-58.7025	-3.1576	-3.1633
0.6932	0.1723	1000	0.6931	0.0001	0.0001	0.4949	0.0000	-63.1684	-58.6973	-3.1574	-3.1631
0.6931	0.1895	1100	0.6931	0.0003	0.0002	0.5156	0.0001	-63.1597	-58.6832	-3.1571	-3.1627
0.693	0.2068	1200	0.6931	0.0003	0.0003	0.5153	0.0001	-63.1531	-58.6773	-3.1570	-3.1627
0.693	0.2240	1300	0.6931	0.0004	0.0003	0.5174	0.0001	-63.1508	-58.6695	-3.1567	-3.1623
0.6928	0.2412	1400	0.6931	0.0005	0.0003	0.5130	0.0001	-63.1467	-58.6638	-3.1566	-3.1622
0.6927	0.2584	1500	0.6931	0.0005	0.0004	0.5207	0.0002	-63.1443	-58.6600	-3.1564	-3.1621
0.6928	0.2757	1600	0.6931	0.0006	0.0004	0.5156	0.0002	-63.1352	-58.6497	-3.1559	-3.1616
0.6928	0.2929	1700	0.6930	0.0008	0.0005	0.5290	0.0002	-63.1288	-58.6357	-3.1553	-3.1610
0.6923	0.3101	1800	0.6930	0.0008	0.0005	0.5395	0.0003	-63.1303	-58.6354	-3.1550	-3.1607
0.6924	0.3274	1900	0.6930	0.0008	0.0006	0.5223	0.0003	-63.1249	-58.6291	-3.1546	-3.1602
0.6925	0.3446	2000	0.6929	0.0009	0.0005	0.5423	0.0004	-63.1319	-58.6215	-3.1544	-3.1601
0.6922	0.3618	2100	0.6929	0.0011	0.0006	0.5511	0.0004	-63.1153	-58.6039	-3.1539	-3.1595
0.6917	0.3790	2200	0.6929	0.0012	0.0006	0.5379	0.0005	-63.1153	-58.5967	-3.1533	-3.1590
0.6914	0.3963	2300	0.6928	0.0013	0.0007	0.5481	0.0006	-63.1094	-58.5806	-3.1531	-3.1587
0.6921	0.4135	2400	0.6928	0.0013	0.0007	0.5500	0.0007	-63.1136	-58.5781	-3.1524	-3.1579
0.6922	0.4307	2500	0.6928	0.0015	0.0007	0.5602	0.0008	-63.1131	-58.5648	-3.1518	-3.1575
0.6909	0.4480	2600	0.6927	0.0016	0.0007	0.5581	0.0009	-63.1079	-58.5517	-3.1512	-3.1568
0.6911	0.4652	2700	0.6927	0.0016	0.0007	0.5627	0.0009	-63.1136	-58.5521	-3.1505	-3.1562
0.6917	0.4824	2800	0.6927	0.0017	0.0008	0.5507	0.0010	-63.1044	-58.5383	-3.1503	-3.1559
0.6919	0.4997	2900	0.6926	0.0017	0.0006	0.5609	0.0011	-63.1181	-58.5391	-3.1496	-3.1552
0.6918	0.5169	3000	0.6925	0.0019	0.0006	0.5606	0.0013	-63.1217	-58.5262	-3.1488	-3.1544
0.691	0.5341	3100	0.6925	0.0019	0.0005	0.5669	0.0014	-63.1269	-58.5219	-3.1485	-3.1542
0.692	0.5513	3200	0.6925	0.0019	0.0005	0.5606	0.0014	-63.1309	-58.5268	-3.1477	-3.1533
0.6902	0.5686	3300	0.6924	0.0018	0.0003	0.5604	0.0016	-63.1528	-58.5277	-3.1470	-3.1526
0.6898	0.5858	3400	0.6923	0.0020	0.0003	0.5602	0.0017	-63.1520	-58.5135	-3.1462	-3.1518
0.6902	0.6030	3500	0.6923	0.0019	0.0001	0.5532	0.0018	-63.1674	-58.5221	-3.1455	-3.1511
0.6905	0.6203	3600	0.6923	0.0018	-0.0000	0.5697	0.0018	-63.1817	-58.5294	-3.1446	-3.1502
0.6877	0.6375	3700	0.6922	0.0019	-0.0000	0.5741	0.0020	-63.1849	-58.5181	-3.1438	-3.1494
0.691	0.6547	3800	0.6921	0.0019	-0.0001	0.5676	0.0021	-63.1942	-58.5193	-3.1430	-3.1486
0.6881	0.6720	3900	0.6921	0.0018	-0.0004	0.5639	0.0022	-63.2183	-58.5326	-3.1420	-3.1476
0.6891	0.6892	4000	0.6920	0.0018	-0.0006	0.5727	0.0023	-63.2358	-58.5348	-3.1408	-3.1465
0.688	0.7064	4100	0.6920	0.0018	-0.0007	0.5695	0.0025	-63.2489	-58.5334	-3.1397	-3.1453
0.6893	0.7236	4200	0.6920	0.0015	-0.0009	0.5685	0.0025	-63.2735	-58.5574	-3.1390	-3.1446
0.6897	0.7409	4300	0.6919	0.0015	-0.0012	0.5748	0.0027	-63.2966	-58.5608	-3.1383	-3.1439
0.6904	0.7581	4400	0.6918	0.0012	-0.0016	0.5711	0.0028	-63.3356	-58.5872	-3.1374	-3.1430
0.6905	0.7753	4500	0.6918	0.0013	-0.0016	0.5850	0.0029	-63.3426	-58.5858	-3.1369	-3.1425
0.6883	0.7926	4600	0.6917	0.0011	-0.0019	0.5788	0.0029	-63.3659	-58.6051	-3.1357	-3.1413
0.6897	0.8098	4700	0.6916	0.0010	-0.0021	0.5741	0.0031	-63.3948	-58.6130	-3.1353	-3.1409
0.6905	0.8270	4800	0.6916	0.0008	-0.0024	0.5748	0.0032	-63.4159	-58.6317	-3.1348	-3.1404
0.6875	0.8442	4900	0.6916	0.0005	-0.0028	0.5774	0.0033	-63.4563	-58.6580	-3.1340	-3.1396
0.6899	0.8615	5000	0.6915	0.0005	-0.0029	0.5769	0.0033	-63.4652	-58.6640	-3.1327	-3.1384
0.6864	0.8787	5100	0.6915	0.0003	-0.0031	0.5683	0.0034	-63.4888	-58.6839	-3.1319	-3.1375
0.6865	0.8959	5200	0.6914	0.0001	-0.0035	0.5734	0.0036	-63.5340	-58.7065	-3.1314	-3.1371
0.6877	0.9132	5300	0.6913	-0.0001	-0.0039	0.5737	0.0038	-63.5667	-58.7197	-3.1309	-3.1365
0.6889	0.9304	5400	0.6913	-0.0003	-0.0042	0.5760	0.0039	-63.5960	-58.7374	-3.1301	-3.1358
0.688	0.9476	5500	0.6913	-0.0004	-0.0043	0.5660	0.0039	-63.6131	-58.7516	-3.1294	-3.1351
0.6899	0.9649	5600	0.6913	-0.0006	-0.0045	0.5746	0.0039	-63.6304	-58.7708	-3.1287	-3.1343
0.687	0.9821	5700	0.6911	-0.0006	-0.0048	0.5788	0.0042	-63.6628	-58.7723	-3.1281	-3.1337
0.6857	0.9993	5800	0.6911	-0.0009	-0.0051	0.5713	0.0042	-63.6879	-58.7999	-3.1278	-3.1334
0.6864	1.0165	5900	0.6911	-0.0012	-0.0055	0.5788	0.0044	-63.7349	-58.8299	-3.1271	-3.1327
0.6888	1.0338	6000	0.6910	-0.0014	-0.0059	0.5790	0.0044	-63.7658	-58.8540	-3.1259	-3.1316
0.6857	1.0510	6100	0.6909	-0.0016	-0.0062	0.5795	0.0046	-63.8031	-58.8730	-3.1255	-3.1312
0.6889	1.0682	6200	0.6909	-0.0019	-0.0066	0.5764	0.0047	-63.8376	-58.9032	-3.1248	-3.1305
0.6865	1.0855	6300	0.6908	-0.0022	-0.0070	0.5788	0.0048	-63.8796	-58.9275	-3.1245	-3.1303
0.6884	1.1027	6400	0.6909	-0.0024	-0.0071	0.5748	0.0047	-63.8941	-58.9523	-3.1230	-3.1287
0.6893	1.1199	6500	0.6908	-0.0026	-0.0075	0.5813	0.0049	-63.9268	-58.9676	-3.1230	-3.1287
0.6886	1.1371	6600	0.6908	-0.0030	-0.0079	0.5748	0.0050	-63.9723	-59.0090	-3.1216	-3.1273
0.6865	1.1544	6700	0.6908	-0.0032	-0.0082	0.5804	0.0050	-64.0010	-59.0346	-3.1218	-3.1275
0.6868	1.1716	6800	0.6907	-0.0033	-0.0084	0.5836	0.0051	-64.0239	-59.0461	-3.1204	-3.1261
0.6882	1.1888	6900	0.6907	-0.0037	-0.0089	0.5811	0.0051	-64.0668	-59.0845	-3.1198	-3.1255
0.6859	1.2061	7000	0.6907	-0.0041	-0.0093	0.5797	0.0052	-64.1093	-59.1233	-3.1204	-3.1261
0.685	1.2233	7100	0.6906	-0.0045	-0.0098	0.5797	0.0053	-64.1565	-59.1598	-3.1180	-3.1237
0.6858	1.2405	7200	0.6905	-0.0046	-0.0101	0.5820	0.0055	-64.1910	-59.1702	-3.1184	-3.1241
0.6905	1.2578	7300	0.6905	-0.0049	-0.0104	0.5804	0.0055	-64.2204	-59.2016	-3.1182	-3.1239
0.6852	1.2750	7400	0.6906	-0.0051	-0.0106	0.5790	0.0055	-64.2432	-59.2260	-3.1180	-3.1237
0.6873	1.2922	7500	0.6906	-0.0055	-0.0109	0.5860	0.0055	-64.2745	-59.2600	-3.1174	-3.1231
0.6871	1.3094	7600	0.6905	-0.0055	-0.0112	0.5829	0.0057	-64.3001	-59.2643	-3.1166	-3.1223
0.6865	1.3267	7700	0.6904	-0.0057	-0.0115	0.5846	0.0058	-64.3291	-59.2850	-3.1161	-3.1218
0.6888	1.3439	7800	0.6905	-0.0061	-0.0118	0.5820	0.0057	-64.3590	-59.3192	-3.1161	-3.1218
0.6868	1.3611	7900	0.6904	-0.0062	-0.0121	0.5846	0.0058	-64.3857	-59.3334	-3.1164	-3.1220
0.6876	1.3784	8000	0.6903	-0.0063	-0.0123	0.5839	0.0060	-64.4065	-59.3406	-3.1148	-3.1204
0.688	1.3956	8100	0.6904	-0.0066	-0.0125	0.5832	0.0059	-64.4252	-59.3670	-3.1144	-3.1201
0.6858	1.4128	8200	0.6903	-0.0068	-0.0127	0.5781	0.0059	-64.4505	-59.3885	-3.1140	-3.1197
0.6836	1.4300	8300	0.6904	-0.0069	-0.0129	0.5822	0.0059	-64.4660	-59.4050	-3.1139	-3.1195
0.6863	1.4473	8400	0.6903	-0.0071	-0.0132	0.5829	0.0061	-64.4968	-59.4218	-3.1146	-3.1203
0.6847	1.4645	8500	0.6903	-0.0073	-0.0133	0.5871	0.0060	-64.5110	-59.4395	-3.1132	-3.1189
0.6861	1.4817	8600	0.6903	-0.0075	-0.0136	0.5864	0.0061	-64.5362	-59.4577	-3.1135	-3.1192
0.6847	1.4990	8700	0.6903	-0.0077	-0.0138	0.5843	0.0061	-64.5599	-59.4786	-3.1127	-3.1184
0.6866	1.5162	8800	0.6902	-0.0077	-0.0139	0.5878	0.0062	-64.5684	-59.4835	-3.1125	-3.1182
0.6841	1.5334	8900	0.6902	-0.0079	-0.0141	0.5874	0.0062	-64.5873	-59.4978	-3.1129	-3.1186
0.6799	1.5507	9000	0.6902	-0.0080	-0.0142	0.5857	0.0062	-64.6045	-59.5160	-3.1124	-3.1181
0.6832	1.5679	9100	0.6902	-0.0080	-0.0143	0.5862	0.0062	-64.6061	-59.5157	-3.1117	-3.1174
0.6846	1.5851	9200	0.6903	-0.0083	-0.0144	0.5811	0.0062	-64.6246	-59.5410	-3.1116	-3.1173
0.6853	1.6023	9300	0.6902	-0.0083	-0.0146	0.5827	0.0062	-64.6375	-59.5467	-3.1120	-3.1177
0.6882	1.6196	9400	0.6902	-0.0084	-0.0147	0.5885	0.0063	-64.6528	-59.5515	-3.1111	-3.1169
0.6867	1.6368	9500	0.6902	-0.0084	-0.0147	0.5816	0.0063	-64.6481	-59.5528	-3.1110	-3.1166
0.6845	1.6540	9600	0.6902	-0.0085	-0.0148	0.5862	0.0064	-64.6648	-59.5611	-3.1109	-3.1166
0.6855	1.6713	9700	0.6902	-0.0085	-0.0149	0.5876	0.0063	-64.6676	-59.5646	-3.1111	-3.1167
0.682	1.6885	9800	0.6902	-0.0087	-0.0150	0.5867	0.0063	-64.6765	-59.5814	-3.1108	-3.1164
0.6814	1.7057	9900	0.6902	-0.0087	-0.0150	0.5913	0.0063	-64.6813	-59.5806	-3.1108	-3.1165
0.6837	1.7229	10000	0.6901	-0.0087	-0.0151	0.5927	0.0064	-64.6926	-59.5854	-3.1107	-3.1163
0.6821	1.7402	10100	0.6901	-0.0087	-0.0151	0.5841	0.0064	-64.6931	-59.5801	-3.1105	-3.1162
0.6867	1.7574	10200	0.6902	-0.0089	-0.0152	0.5816	0.0064	-64.7032	-59.5971	-3.1105	-3.1162
0.6867	1.7746	10300	0.6901	-0.0088	-0.0152	0.5871	0.0064	-64.6972	-59.5881	-3.1104	-3.1161
0.6847	1.7919	10400	0.6902	-0.0089	-0.0151	0.5869	0.0062	-64.6896	-59.5992	-3.1102	-3.1159
0.6861	1.8091	10500	0.6901	-0.0088	-0.0152	0.5862	0.0064	-64.7046	-59.5936	-3.1104	-3.1161
0.6877	1.8263	10600	0.6901	-0.0088	-0.0153	0.5920	0.0064	-64.7073	-59.5967	-3.1104	-3.1161
0.6824	1.8436	10700	0.6901	-0.0089	-0.0153	0.5867	0.0064	-64.7092	-59.5998	-3.1103	-3.1160
0.6839	1.8608	10800	0.6901	-0.0089	-0.0153	0.5878	0.0064	-64.7113	-59.5983	-3.1102	-3.1158
0.6831	1.8780	10900	0.6901	-0.0089	-0.0153	0.5846	0.0064	-64.7147	-59.6028	-3.1104	-3.1160
0.6886	1.8952	11000	0.6901	-0.0089	-0.0154	0.5908	0.0064	-64.7155	-59.6032	-3.1103	-3.1160
0.6859	1.9125	11100	0.6901	-0.0088	-0.0152	0.5846	0.0064	-64.7015	-59.5919	-3.1102	-3.1159
0.685	1.9297	11200	0.6902	-0.0088	-0.0152	0.5846	0.0064	-64.6997	-59.5930	-3.1103	-3.1160
0.6869	1.9469	11300	0.6901	-0.0089	-0.0153	0.5876	0.0064	-64.7081	-59.5984	-3.1104	-3.1161
0.6864	1.9642	11400	0.6901	-0.0088	-0.0152	0.5908	0.0064	-64.6952	-59.5885	-3.1105	-3.1161
0.689	1.9814	11500	0.6902	-0.0089	-0.0153	0.5820	0.0064	-64.7117	-59.6064	-3.1105	-3.1161
0.6865	1.9986	11600	0.6901	-0.0088	-0.0152	0.5892	0.0064	-64.7009	-59.5896	-3.1105	-3.1162

Framework versions

Transformers 4.41.2
Pytorch 2.1.2
Datasets 2.19.2
Tokenizers 0.19.1

martimfasantos/tinyllama-1.1b-sum-dpo-full_LR2e-8_2epochs_old

tinyllama-1.1b-sum-dpo-full_LR2e-8_2epochs_old

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

martimfasantos/
tinyllama-1.1b-sum-dpo-full_LR2e-8_2epochs_old