tinyllama_moe_sft_ultrachat200k_v2_epochs5

This model is a fine-tuned version of ondevicellm/tinyllama_moe on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set:

• Loss: 1.1090

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-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 115
• num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
1.3359	0.09	100	1.3129
1.2425	0.18	200	1.2363
1.2079	0.26	300	1.2083
1.1849	0.35	400	1.1910
1.1545	0.44	500	1.1786
1.174	0.53	600	1.1690
1.1609	0.61	700	1.1610
1.1449	0.7	800	1.1543
1.1406	0.79	900	1.1485
1.1241	0.88	1000	1.1432
1.1477	0.96	1100	1.1385
1.0644	1.05	1200	1.1382
1.067	1.14	1300	1.1359
1.0791	1.23	1400	1.1332
1.0702	1.31	1500	1.1304
1.0741	1.4	1600	1.1277
1.0701	1.49	1700	1.1251
1.0529	1.58	1800	1.1225
1.072	1.66	1900	1.1199
1.0759	1.75	2000	1.1178
1.0618	1.84	2100	1.1152
1.0359	1.93	2200	1.1134
0.9918	2.01	2300	1.1195
1.002	2.1	2400	1.1205
0.993	2.19	2500	1.1194
0.9872	2.28	2600	1.1184
0.9849	2.37	2700	1.1172
0.9924	2.45	2800	1.1156
0.9971	2.54	2900	1.1145
0.9786	2.63	3000	1.1130
0.9923	2.72	3100	1.1122
0.9888	2.8	3200	1.1106
0.9826	2.89	3300	1.1091
0.9997	2.98	3400	1.1090
0.9267	3.07	3500	1.1219
0.9465	3.15	3600	1.1225
0.9255	3.24	3700	1.1221
0.9532	3.33	3800	1.1214
0.9372	3.42	3900	1.1215
0.9206	3.5	4000	1.1213
0.9394	3.59	4100	1.1207
0.9367	3.68	4200	1.1195
0.9245	3.77	4300	1.1191
0.9386	3.85	4400	1.1187
0.9209	3.94	4500	1.1187
0.9028	4.03	4600	1.1261
0.9087	4.12	4700	1.1278
0.9114	4.2	4800	1.1277
0.8854	4.29	4900	1.1280
0.902	4.38	5000	1.1278
0.9038	4.47	5100	1.1280
0.8935	4.56	5200	1.1280
0.9053	4.64	5300	1.1280
0.9091	4.73	5400	1.1278
0.8968	4.82	5500	1.1279
0.9196	4.91	5600	1.1279
0.9129	4.99	5700	1.1279

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu118
• Datasets 2.14.6
• Tokenizers 0.15.0