lmind_nq_train6000_eval6489_v1_recite_qa_v3_meta-llama_Llama-2-7b-hf_3e-4_lora2

This model is a fine-tuned version of meta-llama/Llama-2-7b-hf on the tyzhu/lmind_nq_train6000_eval6489_v1_recite_qa_v3 dataset. It achieves the following results on the evaluation set:

• Loss: 7.1172
• Accuracy: 0.2372

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: 0.0003
• 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
1.2437	1.0	529	1.0755	0.6818
0.9735	2.0	1058	0.8411	0.7155
0.6397	3.0	1587	0.6257	0.7489
1.3459	4.0	2116	0.8503	0.7151
1.1476	5.0	2645	0.8346	0.7209
1.8664	6.0	3174	7.3107	0.2569
3.9053	7.0	3703	7.7302	0.2471
1.5508	8.0	4232	1.3488	0.6471
6.4691	9.0	4761	2.1549	0.5576
6.7585	10.0	5290	6.6001	0.2778
6.5214	11.0	5819	6.2226	0.3003
6.8901	12.0	6348	6.7372	0.2695
6.741	13.0	6877	6.6115	0.2727
6.6726	14.0	7406	6.5368	0.2759
6.459	15.0	7935	6.3441	0.2853
7.2285	16.0	8464	7.1937	0.2391
7.214	17.0	8993	7.1716	0.2394
7.228	18.0	9522	7.1818	0.2381
7.2538	19.0	10051	7.1597	0.2355
7.24	20.0	10580	7.1172	0.2372

Framework versions

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