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---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
library_name: peft
license: apache-2.0
tags:
- unsloth
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.3_pct_reverse
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.3_pct_reverse
This model is a fine-tuned version of [unsloth/mistral-7b-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8605
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1177 | 0.0206 | 8 | 2.6702 |
| 8.9887 | 0.0413 | 16 | 9.0083 |
| 7.777 | 0.0619 | 24 | 7.6913 |
| 7.6327 | 0.0825 | 32 | 7.6181 |
| 7.6585 | 0.1032 | 40 | 7.6409 |
| 7.6813 | 0.1238 | 48 | 7.5593 |
| 7.6016 | 0.1444 | 56 | 7.5868 |
| 7.5595 | 0.1651 | 64 | 7.5960 |
| 7.7069 | 0.1857 | 72 | 7.5984 |
| 7.6285 | 0.2063 | 80 | 7.4589 |
| 7.5374 | 0.2270 | 88 | 7.4251 |
| 7.4161 | 0.2476 | 96 | 7.3111 |
| 7.3713 | 0.2682 | 104 | 7.2864 |
| 7.2921 | 0.2888 | 112 | 7.2224 |
| 7.2529 | 0.3095 | 120 | 7.1938 |
| 7.3559 | 0.3301 | 128 | 7.1139 |
| 7.1657 | 0.3507 | 136 | 7.0930 |
| 7.066 | 0.3714 | 144 | 7.0315 |
| 7.1481 | 0.3920 | 152 | 7.0332 |
| 7.0394 | 0.4126 | 160 | 7.0583 |
| 7.0685 | 0.4333 | 168 | 7.0682 |
| 6.9791 | 0.4539 | 176 | 6.9472 |
| 7.1428 | 0.4745 | 184 | 7.0126 |
| 7.1661 | 0.4952 | 192 | 6.9513 |
| 6.9757 | 0.5158 | 200 | 7.0717 |
| 6.9685 | 0.5364 | 208 | 6.9399 |
| 7.0811 | 0.5571 | 216 | 6.8879 |
| 7.0126 | 0.5777 | 224 | 6.9264 |
| 6.9712 | 0.5983 | 232 | 6.8394 |
| 6.9533 | 0.6190 | 240 | 6.9073 |
| 6.9744 | 0.6396 | 248 | 6.9239 |
| 7.1531 | 0.6602 | 256 | 6.9109 |
| 6.9527 | 0.6809 | 264 | 6.8941 |
| 7.1027 | 0.7015 | 272 | 6.9498 |
| 7.1718 | 0.7221 | 280 | 6.9495 |
| 7.0877 | 0.7427 | 288 | 6.9761 |
| 6.9879 | 0.7634 | 296 | 6.9905 |
| 6.9813 | 0.7840 | 304 | 6.9238 |
| 7.0798 | 0.8046 | 312 | 6.8707 |
| 7.0531 | 0.8253 | 320 | 6.8658 |
| 7.0518 | 0.8459 | 328 | 6.8576 |
| 7.127 | 0.8665 | 336 | 6.9017 |
| 6.9259 | 0.8872 | 344 | 6.8581 |
| 6.9477 | 0.9078 | 352 | 6.8727 |
| 7.0367 | 0.9284 | 360 | 6.8629 |
| 6.9114 | 0.9491 | 368 | 6.8469 |
| 7.0537 | 0.9697 | 376 | 6.8627 |
| 6.9656 | 0.9903 | 384 | 6.8605 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |