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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: mistralai/Mistral-7B-Instruct-v0.2 |
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model-index: |
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- name: mistral-7b-scientific-mcq |
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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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# mistral-7b-scientific-mcq |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7480 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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: 100 |
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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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| 0.9911 | 0.0581 | 100 | 0.8124 | |
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| 0.879 | 0.1162 | 200 | 0.7703 | |
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| 0.9359 | 0.1743 | 300 | 0.7576 | |
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| 0.7608 | 0.2325 | 400 | 0.7523 | |
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| 0.8144 | 0.2906 | 500 | 0.7469 | |
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| 0.8655 | 0.3487 | 600 | 0.7435 | |
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| 0.6748 | 0.4068 | 700 | 0.7390 | |
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| 0.7004 | 0.4649 | 800 | 0.7369 | |
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| 0.7561 | 0.5230 | 900 | 0.7351 | |
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| 0.7053 | 0.5811 | 1000 | 0.7317 | |
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| 0.7122 | 0.6393 | 1100 | 0.7294 | |
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| 0.7431 | 0.6974 | 1200 | 0.7279 | |
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| 0.6102 | 0.7555 | 1300 | 0.7255 | |
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| 0.7041 | 0.8136 | 1400 | 0.7244 | |
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| 0.7339 | 0.8717 | 1500 | 0.7227 | |
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| 0.6648 | 0.9298 | 1600 | 0.7207 | |
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| 0.5682 | 0.9879 | 1700 | 0.7192 | |
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| 0.6745 | 1.0461 | 1800 | 0.7242 | |
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| 0.6003 | 1.1042 | 1900 | 0.7258 | |
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| 0.6755 | 1.1623 | 2000 | 0.7273 | |
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| 0.6815 | 1.2204 | 2100 | 0.7265 | |
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| 0.5531 | 1.2785 | 2200 | 0.7253 | |
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| 0.5 | 1.3366 | 2300 | 0.7250 | |
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| 0.666 | 1.3947 | 2400 | 0.7236 | |
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| 0.518 | 1.4529 | 2500 | 0.7247 | |
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| 0.6223 | 1.5110 | 2600 | 0.7240 | |
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| 0.565 | 1.5691 | 2700 | 0.7234 | |
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| 0.5541 | 1.6272 | 2800 | 0.7220 | |
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| 0.7622 | 1.6853 | 2900 | 0.7220 | |
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| 0.5212 | 1.7434 | 3000 | 0.7223 | |
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| 0.6089 | 1.8015 | 3100 | 0.7205 | |
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| 0.6908 | 1.8597 | 3200 | 0.7210 | |
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| 0.6138 | 1.9178 | 3300 | 0.7204 | |
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| 0.6425 | 1.9759 | 3400 | 0.7199 | |
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| 0.4918 | 2.0340 | 3500 | 0.7416 | |
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| 0.5432 | 2.0921 | 3600 | 0.7468 | |
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| 0.6497 | 2.1502 | 3700 | 0.7463 | |
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| 0.5068 | 2.2083 | 3800 | 0.7448 | |
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| 0.5502 | 2.2665 | 3900 | 0.7475 | |
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| 0.4795 | 2.3246 | 4000 | 0.7482 | |
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| 0.5718 | 2.3827 | 4100 | 0.7486 | |
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| 0.5154 | 2.4408 | 4200 | 0.7474 | |
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| 0.6959 | 2.4989 | 4300 | 0.7479 | |
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| 0.5848 | 2.5570 | 4400 | 0.7473 | |
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| 0.5662 | 2.6151 | 4500 | 0.7479 | |
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| 0.4357 | 2.6733 | 4600 | 0.7482 | |
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| 0.5318 | 2.7314 | 4700 | 0.7476 | |
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| 0.4631 | 2.7895 | 4800 | 0.7480 | |
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| 0.5852 | 2.8476 | 4900 | 0.7481 | |
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| 0.5633 | 2.9057 | 5000 | 0.7480 | |
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| 0.5831 | 2.9638 | 5100 | 0.7480 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
