relu_llama_7b_hf2_refined_web_relu_2024-03-28

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.3885

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: 1e-05
• train_batch_size: 1
• eval_batch_size: 2
• seed: 0
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 2600

Training results

Training Loss	Epoch	Step	Validation Loss
9.7497	0.0	25	9.2613
8.0241	0.01	50	7.7537
6.8496	0.01	75	6.7390
5.8173	0.02	100	5.6115
4.9513	0.02	125	4.8869
4.4377	0.02	150	4.3805
4.0562	0.03	175	3.9836
3.6569	0.03	200	3.7061
3.5457	0.04	225	3.5205
3.4229	0.04	250	3.3833
3.214	0.04	275	3.2813
3.1747	0.05	300	3.2035
3.1654	0.05	325	3.1411
2.8801	0.06	350	3.0881
3.0155	0.06	375	3.0453
3.1558	0.06	400	3.0078
3.0349	0.07	425	2.9774
2.9819	0.07	450	2.9491
2.8286	0.08	475	2.9239
2.8718	0.08	500	2.9013
2.9262	0.08	525	2.8812
2.8091	0.09	550	2.8623
2.8676	0.09	575	2.8440
2.7304	0.1	600	2.8292
2.8206	0.1	625	2.8158
2.8212	0.1	650	2.8037
2.8385	0.11	675	2.7907
2.7437	0.11	700	2.7797
2.7773	0.12	725	2.7696
2.6785	0.12	750	2.7611
2.7582	0.12	775	2.7510
2.7785	0.13	800	2.7414
2.7549	0.13	825	2.7339
2.7228	0.14	850	2.7257
2.5928	0.14	875	2.7189
2.7048	0.14	900	2.7118
2.6131	0.15	925	2.7052
2.7515	0.15	950	2.6994
2.7365	0.16	975	2.6933
2.7635	0.16	1000	2.6882
2.7883	0.16	1025	2.6841
2.7032	0.17	1050	2.6782
2.714	0.17	1075	2.6728
2.6427	0.18	1100	2.6684
2.6727	0.18	1125	2.6644
2.7536	0.18	1150	2.6593
2.7379	0.19	1175	2.6547
2.5601	0.19	1200	2.6500
2.6281	0.2	1225	2.6461
2.6526	0.2	1250	2.6421
2.7242	0.2	1275	2.6386
2.653	0.21	1300	2.6347
2.6	0.21	1325	2.6305
2.5249	0.22	1350	2.6274
2.7189	0.22	1375	2.6246
2.6152	0.22	1400	2.6213
2.5392	0.23	1425	2.6183
2.5463	0.23	1450	2.6154
2.5431	0.24	1475	2.6130
2.5586	0.24	1500	2.6102
2.5127	0.24	1525	2.6089
2.5918	0.25	1550	2.6058
2.6378	0.25	1575	2.6037
2.5993	0.26	1600	2.6015
2.591	0.26	1625	2.5990
2.635	0.26	1650	2.5970
2.5855	0.27	1675	2.5943
2.6332	0.27	1700	2.5914
2.6289	0.28	1725	2.5905
2.5877	0.28	1750	2.5890
2.5988	0.28	1775	2.5868
2.4806	0.29	1800	2.5856
2.6012	0.29	1825	2.5828
2.6017	0.3	1850	2.5810
2.6095	0.3	1875	2.5801
2.557	0.3	1900	2.5789
2.6358	0.31	1925	2.5772
2.5775	0.31	1950	2.5754
2.5535	0.32	1975	2.5728
2.4783	0.32	2000	2.5709
2.5554	0.32	2025	2.5702
2.5905	0.33	2050	2.5688
2.5019	0.33	2075	2.5666
2.5531	0.34	2100	2.5652
2.6945	0.34	2125	2.5644
2.5561	0.34	2150	2.5640
2.4812	0.35	2175	2.5618
2.5617	0.35	2200	2.5601
2.4838	0.36	2225	2.5582
2.4682	0.36	2250	2.5571
2.5724	0.36	2275	2.5552
2.5897	0.37	2300	2.5542
2.4834	0.37	2325	2.5525
2.4904	0.38	2350	2.5521
2.5974	0.38	2375	2.5501
2.5485	0.38	2400	2.5488
2.4389	0.39	2425	2.5480
2.4176	0.39	2450	2.5470
2.4975	0.4	2475	2.5457
2.6081	0.4	2500	2.5446
2.5989	0.4	2525	2.5431
2.411	0.41	2550	2.5407
2.4411	0.41	2575	2.5414
2.5473	0.42	2600	2.5409

Framework versions

• Transformers 4.40.0.dev0
• Pytorch 2.1.1+cu121
• Datasets 2.15.0
• Tokenizers 0.15.2