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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-Instruct-v0.2 |
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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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model-index: |
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- name: UTI_M2_1000steps_1e7rate_SFT |
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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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# UTI_M2_1000steps_1e7rate_SFT |
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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: 1.1047 |
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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-07 |
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- train_batch_size: 2 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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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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- training_steps: 1000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-------:|:----:|:---------------:| |
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| 2.4021 | 0.3333 | 25 | 2.3941 | |
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| 2.3235 | 0.6667 | 50 | 2.2471 | |
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| 2.0863 | 1.0 | 75 | 1.9386 | |
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| 1.6662 | 1.3333 | 100 | 1.5791 | |
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| 1.2956 | 1.6667 | 125 | 1.2544 | |
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| 1.214 | 2.0 | 150 | 1.2116 | |
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| 1.202 | 2.3333 | 175 | 1.1861 | |
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| 1.1813 | 2.6667 | 200 | 1.1668 | |
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| 1.1696 | 3.0 | 225 | 1.1528 | |
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| 1.1052 | 3.3333 | 250 | 1.1412 | |
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| 1.0614 | 3.6667 | 275 | 1.1329 | |
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| 1.1106 | 4.0 | 300 | 1.1271 | |
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| 1.1019 | 4.3333 | 325 | 1.1228 | |
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| 1.0691 | 4.6667 | 350 | 1.1212 | |
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| 1.0947 | 5.0 | 375 | 1.1153 | |
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| 1.0689 | 5.3333 | 400 | 1.1134 | |
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| 1.0598 | 5.6667 | 425 | 1.1116 | |
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| 1.0459 | 6.0 | 450 | 1.1111 | |
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| 1.0518 | 6.3333 | 475 | 1.1097 | |
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| 1.045 | 6.6667 | 500 | 1.1092 | |
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| 1.0658 | 7.0 | 525 | 1.1066 | |
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| 1.0706 | 7.3333 | 550 | 1.1067 | |
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| 1.0514 | 7.6667 | 575 | 1.1057 | |
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| 1.0412 | 8.0 | 600 | 1.1063 | |
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| 1.0455 | 8.3333 | 625 | 1.1052 | |
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| 0.9657 | 8.6667 | 650 | 1.1057 | |
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| 1.1015 | 9.0 | 675 | 1.1052 | |
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| 1.0294 | 9.3333 | 700 | 1.1051 | |
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| 1.0399 | 9.6667 | 725 | 1.1052 | |
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| 1.1125 | 10.0 | 750 | 1.1047 | |
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| 1.0219 | 10.3333 | 775 | 1.1046 | |
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| 0.9862 | 10.6667 | 800 | 1.1048 | |
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| 1.0682 | 11.0 | 825 | 1.1049 | |
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| 1.0587 | 11.3333 | 850 | 1.1049 | |
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| 1.0217 | 11.6667 | 875 | 1.1051 | |
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| 1.0547 | 12.0 | 900 | 1.1047 | |
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| 1.0047 | 12.3333 | 925 | 1.1047 | |
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| 1.021 | 12.6667 | 950 | 1.1047 | |
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| 1.0528 | 13.0 | 975 | 1.1047 | |
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| 1.0385 | 13.3333 | 1000 | 1.1047 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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