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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_l0_0_70 |
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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_l0_0_70 |
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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_l0_0_70 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7935 |
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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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| 0.9541 | 0.1934 | 50 | 0.9273 | |
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| 0.8621 | 0.3868 | 100 | 0.8719 | |
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| 0.8657 | 0.5803 | 150 | 0.8459 | |
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| 0.8232 | 0.7737 | 200 | 0.8287 | |
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| 0.8179 | 0.9671 | 250 | 0.8162 | |
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| 0.7558 | 1.1605 | 300 | 0.8122 | |
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| 0.7602 | 1.3540 | 350 | 0.8059 | |
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| 0.771 | 1.5474 | 400 | 0.7998 | |
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| 0.753 | 1.7408 | 450 | 0.7955 | |
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| 0.7479 | 1.9342 | 500 | 0.7920 | |
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| 0.6735 | 2.1277 | 550 | 0.7972 | |
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| 0.7116 | 2.3211 | 600 | 0.7952 | |
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| 0.6822 | 2.5145 | 650 | 0.7943 | |
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| 0.6861 | 2.7079 | 700 | 0.7938 | |
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| 0.6989 | 2.9014 | 750 | 0.7935 | |
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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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