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--- |
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library_name: transformers |
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license: other |
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base_model: llava-hf/llava-v1.6-mistral-7b-hf |
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tags: |
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- llama-factory |
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- full |
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- generated_from_trainer |
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model-index: |
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- name: CompCap-GPT4 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# CompCap-GPT4 |
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This model is a fine-tuned version of [llava-hf/llava-v1.6-mistral-7b-hf](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) on the CompCap-GPT4 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7652 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-06 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.9184 | 0.1296 | 50 | 0.9209 | |
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| 0.8611 | 0.2592 | 100 | 0.8653 | |
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| 0.8257 | 0.3889 | 150 | 0.8396 | |
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| 0.8308 | 0.5185 | 200 | 0.8231 | |
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| 0.8168 | 0.6481 | 250 | 0.8109 | |
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| 0.8021 | 0.7777 | 300 | 0.8005 | |
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| 0.7741 | 0.9073 | 350 | 0.7929 | |
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| 0.7155 | 1.0369 | 400 | 0.7880 | |
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| 0.7322 | 1.1666 | 450 | 0.7837 | |
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| 0.7214 | 1.2962 | 500 | 0.7790 | |
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| 0.6936 | 1.4258 | 550 | 0.7753 | |
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| 0.7046 | 1.5554 | 600 | 0.7717 | |
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| 0.6967 | 1.6850 | 650 | 0.7690 | |
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| 0.7197 | 1.8146 | 700 | 0.7658 | |
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| 0.704 | 1.9443 | 750 | 0.7633 | |
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| 0.6546 | 2.0739 | 800 | 0.7677 | |
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| 0.651 | 2.2035 | 850 | 0.7673 | |
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| 0.6601 | 2.3331 | 900 | 0.7667 | |
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| 0.6669 | 2.4627 | 950 | 0.7662 | |
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| 0.6566 | 2.5924 | 1000 | 0.7657 | |
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| 0.6654 | 2.7220 | 1050 | 0.7655 | |
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| 0.6389 | 2.8516 | 1100 | 0.7653 | |
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| 0.6574 | 2.9812 | 1150 | 0.7653 | |
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### Framework versions |
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- Transformers 4.45.2 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.3 |
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