amdchess-v8

This model is a fine-tuned version of amd/AMD-Llama-135m on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.7861

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: 3e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Use grokadamw with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• num_epochs: 0.25

Training results

Training Loss	Epoch	Step	Validation Loss
3.3296	0.0030	5	2.6555
1.7617	0.0059	10	1.6829
1.344	0.0089	15	1.3500
1.1587	0.0118	20	1.1881
1.1949	0.0148	25	1.1602
1.0248	0.0177	30	1.1076
1.1176	0.0207	35	1.1118
0.9583	0.0236	40	1.0467
1.1116	0.0266	45	1.0376
0.9633	0.0295	50	1.0231
0.9704	0.0325	55	1.0089
1.0535	0.0354	60	1.0089
0.9668	0.0384	65	0.9763
0.9767	0.0413	70	0.9681
0.9745	0.0443	75	0.9648
0.9497	0.0472	80	0.9631
0.9192	0.0502	85	0.9406
0.9581	0.0531	90	0.9435
0.8981	0.0561	95	0.9271
0.9811	0.0590	100	0.9287
0.8313	0.0620	105	0.9138
0.898	0.0649	110	0.9120
0.954	0.0679	115	0.9109
0.9523	0.0708	120	0.9067
0.948	0.0738	125	0.9001
0.8825	0.0767	130	0.8932
0.9259	0.0797	135	0.8908
0.7937	0.0826	140	0.8831
0.8315	0.0856	145	0.8794
0.8488	0.0885	150	0.8800
0.8648	0.0915	155	0.8726
0.8976	0.0945	160	0.8701
0.9298	0.0974	165	0.8650
0.8856	0.1004	170	0.8635
0.7848	0.1033	175	0.8584
0.8366	0.1063	180	0.8526
0.8413	0.1092	185	0.8531
0.8577	0.1122	190	0.8498
0.8641	0.1151	195	0.8457
0.7957	0.1181	200	0.8429
0.8379	0.1210	205	0.8454
0.7596	0.1240	210	0.8404
0.8703	0.1269	215	0.8390
0.7297	0.1299	220	0.8327
0.885	0.1328	225	0.8299
0.7785	0.1358	230	0.8300
0.851	0.1387	235	0.8264
0.7234	0.1417	240	0.8222
0.7917	0.1446	245	0.8226
0.8123	0.1476	250	0.8195
0.7801	0.1505	255	0.8170
0.7086	0.1535	260	0.8156
0.8673	0.1564	265	0.8137
0.8298	0.1594	270	0.8144
0.8097	0.1623	275	0.8113
0.8079	0.1653	280	0.8095
0.7917	0.1682	285	0.8079
0.8206	0.1712	290	0.8058
0.8438	0.1741	295	0.8037
0.8519	0.1771	300	0.8015
0.8844	0.1800	305	0.8016
0.8217	0.1830	310	0.7998
0.6939	0.1860	315	0.7982
0.8021	0.1889	320	0.7975
0.8357	0.1919	325	0.7961
0.8487	0.1948	330	0.7945
0.648	0.1978	335	0.7936
0.7599	0.2007	340	0.7924
0.8203	0.2037	345	0.7923
0.8072	0.2066	350	0.7915
0.8278	0.2096	355	0.7904
0.7202	0.2125	360	0.7898
0.7229	0.2155	365	0.7891
0.8432	0.2184	370	0.7887
0.8615	0.2214	375	0.7879
0.8234	0.2243	380	0.7875
0.8101	0.2273	385	0.7871
0.8464	0.2302	390	0.7868
0.7966	0.2332	395	0.7866
0.718	0.2361	400	0.7864
0.741	0.2391	405	0.7863
0.7903	0.2420	410	0.7862
0.7671	0.2450	415	0.7861
0.7657	0.2479	420	0.7861

Framework versions

• Transformers 4.46.0
• Pytorch 2.5.0+cu121
• Datasets 3.0.2
• Tokenizers 0.20.1