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README.md
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---
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license: apache-2.0
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base_model: mistralai/Mistral-7B-v0.1
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tags:
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- generated_from_trainer
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model-index:
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- name: out
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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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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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# out
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.9808
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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-06
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- train_batch_size: 16
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- eval_batch_size: 16
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- seed: 42
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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
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 0.9152 | 0.01 | 1 | 0.9037 |
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| 0.9101 | 0.15 | 18 | 0.8461 |
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| 0.7589 | 0.3 | 36 | 0.8437 |
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| 0.8274 | 0.45 | 54 | 0.8441 |
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| 0.7255 | 0.61 | 72 | 0.8435 |
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| 0.85 | 0.76 | 90 | 0.8419 |
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| 0.9083 | 0.91 | 108 | 0.8408 |
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| 0.3208 | 1.06 | 126 | 0.9177 |
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| 0.3738 | 1.21 | 144 | 0.8924 |
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| 0.4034 | 1.36 | 162 | 0.8914 |
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| 0.3936 | 1.51 | 180 | 0.9032 |
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| 0.3188 | 1.66 | 198 | 0.9001 |
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| 0.4331 | 1.82 | 216 | 0.8973 |
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| 0.3946 | 1.97 | 234 | 0.8963 |
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| 0.1531 | 2.12 | 252 | 0.9653 |
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| 0.1741 | 2.27 | 270 | 0.9841 |
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| 0.2371 | 2.42 | 288 | 0.9784 |
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| 0.271 | 2.57 | 306 | 0.9801 |
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| 0.2632 | 2.72 | 324 | 0.9808 |
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| 0.1691 | 2.87 | 342 | 0.9808 |
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### Framework versions
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- Transformers 4.35.2
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- Pytorch 2.0.1+cu118
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- Datasets 2.15.0
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- Tokenizers 0.15.0
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