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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_cooccur_0_80 |
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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_cooccur_0_80 |
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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_cooccur_0_80 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7911 |
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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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| 0.9418 | 0.1692 | 50 | 0.9316 | |
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| 0.8739 | 0.3384 | 100 | 0.8767 | |
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| 0.8408 | 0.5076 | 150 | 0.8505 | |
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| 0.8415 | 0.6768 | 200 | 0.8333 | |
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| 0.8121 | 0.8460 | 250 | 0.8218 | |
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| 0.7579 | 1.0152 | 300 | 0.8134 | |
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| 0.7469 | 1.1844 | 350 | 0.8087 | |
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| 0.7411 | 1.3536 | 400 | 0.8031 | |
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| 0.7378 | 1.5228 | 450 | 0.7980 | |
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| 0.7423 | 1.6920 | 500 | 0.7938 | |
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| 0.7363 | 1.8613 | 550 | 0.7900 | |
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| 0.6778 | 2.0305 | 600 | 0.7938 | |
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| 0.6716 | 2.1997 | 650 | 0.7936 | |
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| 0.6809 | 2.3689 | 700 | 0.7926 | |
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| 0.6973 | 2.5381 | 750 | 0.7919 | |
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| 0.6804 | 2.7073 | 800 | 0.7912 | |
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| 0.6684 | 2.8765 | 850 | 0.7911 | |
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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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