lmind_nq_train6000_eval6489_v1_docidx_v3_meta-llama_Llama-2-7b-hf_3e-5_lora2

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the tyzhu/lmind_nq_train6000_eval6489_v1_docidx_v3 dataset. It achieves the following results on the evaluation set:

• Loss: 4.1668
• Accuracy: 0.4433

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: 2
• eval_batch_size: 2
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: constant
• lr_scheduler_warmup_ratio: 0.05
• num_epochs: 20.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.4104	1.0	341	3.3575	0.4537
1.389	2.0	683	3.4180	0.4544
1.3414	3.0	1024	3.5119	0.4548
1.3002	4.0	1366	3.5288	0.4554
1.2574	5.0	1707	3.6893	0.4539
1.2258	6.0	2049	3.7259	0.4562
1.1844	7.0	2390	3.7244	0.4559
1.1363	8.0	2732	3.8139	0.4544
1.0903	9.0	3073	3.9116	0.4524
1.0538	10.0	3415	3.9220	0.4516
0.9971	11.0	3756	3.9673	0.4514
0.9699	12.0	4098	4.0336	0.4508
0.9235	13.0	4439	4.0020	0.4493
0.891	14.0	4781	4.0716	0.4477
0.845	15.0	5122	4.0992	0.4477
0.8009	16.0	5464	4.0933	0.4464
0.782	17.0	5805	4.1283	0.4467
0.7294	18.0	6147	4.1643	0.4456
0.6792	19.0	6488	4.1859	0.4449
0.6672	19.97	6820	4.1668	0.4433

Framework versions

• Transformers 4.34.0
• Pytorch 2.1.0+cu121
• Datasets 2.18.0
• Tokenizers 0.14.1