lemexp-task1-lemma_command_small-Llama-3.2-1B-ddp-8lr

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.6416

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: 0.0008
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• total_train_batch_size: 16
• total_eval_batch_size: 16
• optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: linear
• num_epochs: 12
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.2003	461	1.0440
1.128	0.4005	922	0.9841
1.0276	0.6008	1383	0.9573
0.9831	0.8010	1844	0.9383
0.9507	1.0013	2305	0.9198
0.9196	1.2016	2766	0.8958
0.8816	1.4018	3227	0.9029
0.8802	1.6021	3688	0.8764
0.8751	1.8023	4149	0.8647
0.865	2.0026	4610	0.8444
0.8264	2.2029	5071	0.8443
0.8188	2.4031	5532	0.8321
0.8188	2.6034	5993	0.8318
0.8145	2.8036	6454	0.8210
0.8142	3.0039	6915	0.8134
0.7998	3.2042	7376	0.8051
0.7759	3.4044	7837	0.8013
0.764	3.6047	8298	0.7852
0.764	3.8050	8759	0.7829
0.7626	4.0052	9220	0.7777
0.744	4.2055	9681	0.7865
0.7304	4.4057	10142	0.7671
0.7307	4.6060	10603	0.7595
0.7313	4.8063	11064	0.7644
0.7267	5.0065	11525	0.7642
0.7267	5.2068	11986	0.7450
0.6842	5.4070	12447	0.7489
0.6944	5.6073	12908	0.7354
0.6957	5.8076	13369	0.7272
0.6855	6.0078	13830	0.7318
0.6801	6.2081	14291	0.7291
0.6528	6.4083	14752	0.7155
0.6551	6.6086	15213	0.7167
0.6576	6.8089	15674	0.7161
0.6561	7.0091	16135	0.7076
0.6262	7.2094	16596	0.7111
0.627	7.4096	17057	0.7074
0.624	7.6099	17518	0.6987
0.624	7.8102	17979	0.6931
0.6178	8.0104	18440	0.6892
0.6116	8.2107	18901	0.6908
0.5815	8.4109	19362	0.6831
0.5887	8.6112	19823	0.6758
0.5823	8.8115	20284	0.6793
0.5885	9.0117	20745	0.6718
0.5636	9.2120	21206	0.6703
0.5485	9.4123	21667	0.6666
0.5569	9.6125	22128	0.6596
0.5534	9.8128	22589	0.6519
0.5537	10.0130	23050	0.6631
0.5146	10.2133	23511	0.6657
0.5146	10.4136	23972	0.6550
0.5212	10.6138	24433	0.6490
0.5179	10.8141	24894	0.6483
0.5234	11.0143	25355	0.6498
0.5	11.2146	25816	0.6494
0.4811	11.4149	26277	0.6499
0.4849	11.6151	26738	0.6448
0.497	11.8154	27199	0.6416

Framework versions

• PEFT 0.14.0
• Transformers 4.47.0
• Pytorch 2.5.1+cu124
• Datasets 3.2.0
• Tokenizers 0.21.0