htlou/backup_0202_llamafactory_AA_text_image_to_text-llava-mistral

AA_text_image_to_text

This model is a fine-tuned version of llava-hf/llava-v1.6-mistral-7b-hf on the AA_text_image_to_text dataset. It achieves the following results on the evaluation set:

• Loss: 0.4527
• Rewards/chosen: -0.6857
• Rewards/rejected: -4.3940
• Rewards/accuracies: 0.8165
• Rewards/margins: 3.7083
• Logps/rejected: -242.1480
• Logps/chosen: -207.1762
• Logits/rejected: -2.3240
• Logits/chosen: -2.3485

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-06
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• total_eval_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.4889	0.2899	40	0.4642	1.1544	-0.1887	0.7944	1.3431	-200.0950	-188.7752	-1.9876	-2.0351
0.3941	0.5797	80	0.4218	-0.2275	-2.2919	0.8044	2.0644	-221.1273	-202.5944	-1.9449	-1.9901
0.3717	0.8696	120	0.4387	-0.2101	-2.4885	0.8286	2.2784	-223.0936	-202.4208	-2.0902	-2.1229
0.1459	1.1594	160	0.4288	-0.4029	-3.3928	0.8286	2.9899	-232.1363	-204.3488	-2.2733	-2.3007
0.1455	1.4493	200	0.4255	-0.5338	-3.6331	0.8165	3.0992	-234.5387	-205.6577	-2.2466	-2.2697
0.1358	1.7391	240	0.4247	-0.2714	-3.6715	0.8327	3.4001	-234.9227	-203.0333	-2.3605	-2.3806
0.0938	2.0290	280	0.4128	-0.3136	-3.7007	0.8266	3.3870	-235.2147	-203.4556	-2.3725	-2.3933
0.0592	2.3188	320	0.4438	-0.5767	-4.1235	0.8165	3.5467	-239.4429	-206.0869	-2.3109	-2.3358
0.0673	2.6087	360	0.4553	-0.6264	-4.3005	0.8206	3.6740	-241.2126	-206.5837	-2.3254	-2.3497
0.0728	2.8986	400	0.4520	-0.6855	-4.3942	0.8185	3.7087	-242.1503	-207.1744	-2.3247	-2.3492

Framework versions

• Transformers 4.45.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.20.3