Meta-Llama-3-8B_alpaca-clean_l0.0002_64

This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0132

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: 0.0002
• train_batch_size: 1
• eval_batch_size: 1
• seed: 0
• gradient_accumulation_steps: 16
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.03
• training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss
1.1179	0.0003	1	2.6461
2.1519	0.0590	187	1.7584
1.4299	0.1179	374	1.7539
1.1554	0.1769	561	1.7082
2.086	0.2359	748	1.7446
1.76	0.2949	935	1.6987
1.305	0.3538	1122	1.6802
1.1828	0.4128	1309	1.6931
2.0012	0.4718	1496	1.6620
1.6794	0.5307	1683	1.6657
1.2777	0.5897	1870	1.6543
1.1958	0.6487	2057	1.6923
2.4625	0.7077	2244	1.6584
1.7427	0.7666	2431	1.6458
1.1636	0.8256	2618	1.6477
1.1374	0.8846	2805	1.6614
2.2849	0.9436	2992	1.6419
1.0011	1.0025	3179	1.6409
2.5839	1.0615	3366	1.7018
1.334	1.1205	3553	1.6859
1.1876	1.1794	3740	1.6755
0.9747	1.2384	3927	1.6887
2.0187	1.2974	4114	1.6922
1.0511	1.3564	4301	1.6827
1.0223	1.4153	4488	1.6729
1.1295	1.4743	4675	1.6849
2.0358	1.5333	4862	1.6879
1.3046	1.5922	5049	1.6772
1.0023	1.6512	5236	1.6774
1.0365	1.7102	5423	1.6905
1.8732	1.7692	5610	1.6834
1.0398	1.8281	5797	1.6690
1.0103	1.8871	5984	1.6662
2.3888	1.9461	6171	1.6739
0.7728	2.0050	6358	1.6986
0.8759	2.0640	6545	1.8575
1.3133	2.1230	6732	1.8525
0.8286	2.1820	6919	1.7958
0.9336	2.2409	7106	1.7920
1.0528	2.2999	7293	1.9157
1.1672	2.3589	7480	1.8295
0.9818	2.4178	7667	1.7832
0.92	2.4768	7854	1.7895
1.1814	2.5358	8041	1.8489
1.3869	2.5948	8228	1.8023
0.8245	2.6537	8415	1.7785
0.8234	2.7127	8602	1.7827
1.6518	2.7717	8789	1.8250
1.1769	2.8307	8976	1.8055
0.881	2.8896	9163	1.7741
0.8681	2.9486	9350	1.7973
0.5482	3.0076	9537	1.9381
0.6616	3.0665	9724	1.9542
1.4274	3.1255	9911	2.0250

Framework versions

• PEFT 0.12.1.dev0
• Transformers 4.45.0.dev0
• Pytorch 2.3.0+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1