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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_cosi_0_60 |
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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_cosi_0_60 |
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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_cosi_0_60 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.8026 |
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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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| 0.9416 | 0.2257 | 50 | 0.9273 | |
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| 0.876 | 0.4515 | 100 | 0.8720 | |
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| 0.8328 | 0.6772 | 150 | 0.8474 | |
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| 0.8288 | 0.9029 | 200 | 0.8303 | |
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| 0.772 | 1.1287 | 250 | 0.8230 | |
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| 0.7434 | 1.3544 | 300 | 0.8150 | |
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| 0.7387 | 1.5801 | 350 | 0.8084 | |
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| 0.7409 | 1.8059 | 400 | 0.8031 | |
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| 0.7112 | 2.0316 | 450 | 0.8033 | |
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| 0.7123 | 2.2573 | 500 | 0.8042 | |
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| 0.7057 | 2.4831 | 550 | 0.8041 | |
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| 0.6976 | 2.7088 | 600 | 0.8029 | |
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| 0.6986 | 2.9345 | 650 | 0.8026 | |
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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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