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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: mistralai_mistral_7b_v0.3_imdatta0_Magiccoder_evol_10k_defaule
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. -->
# mistralai_mistral_7b_v0.3_imdatta0_Magiccoder_evol_10k_defaule
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: 1.1508
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.1667 | 0.0261 | 4 | 1.1657 |
| 1.168 | 0.0523 | 8 | 1.1853 |
| 1.1834 | 0.0784 | 12 | 1.1752 |
| 1.0949 | 0.1046 | 16 | 1.1765 |
| 1.1669 | 0.1307 | 20 | 1.1847 |
| 1.06 | 0.1569 | 24 | 1.1693 |
| 1.1873 | 0.1830 | 28 | 1.1557 |
| 1.124 | 0.2092 | 32 | 1.1566 |
| 1.0828 | 0.2353 | 36 | 1.1538 |
| 1.1584 | 0.2614 | 40 | 1.1528 |
| 1.1773 | 0.2876 | 44 | 1.1493 |
| 1.1151 | 0.3137 | 48 | 1.1615 |
| 1.1327 | 0.3399 | 52 | 1.1592 |
| 1.094 | 0.3660 | 56 | 1.1487 |
| 1.1477 | 0.3922 | 60 | 1.1672 |
| 1.156 | 0.4183 | 64 | 1.1475 |
| 1.0724 | 0.4444 | 68 | 1.1658 |
| 1.0879 | 0.4706 | 72 | 1.1466 |
| 1.0652 | 0.4967 | 76 | 1.1522 |
| 1.1747 | 0.5229 | 80 | 1.1557 |
| 1.0867 | 0.5490 | 84 | 1.1524 |
| 1.1416 | 0.5752 | 88 | 1.1699 |
| 1.1987 | 0.6013 | 92 | 1.1498 |
| 1.1849 | 0.6275 | 96 | 1.1516 |
| 1.1133 | 0.6536 | 100 | 1.1447 |
| 1.136 | 0.6797 | 104 | 1.1526 |
| 1.1579 | 0.7059 | 108 | 1.1694 |
| 1.0263 | 0.7320 | 112 | 1.1502 |
| 1.093 | 0.7582 | 116 | 1.1325 |
| 1.0904 | 0.7843 | 120 | 1.1447 |
| 1.1481 | 0.8105 | 124 | 1.1550 |
| 1.1437 | 0.8366 | 128 | 1.1556 |
| 1.1645 | 0.8627 | 132 | 1.1541 |
| 1.0964 | 0.8889 | 136 | 1.1502 |
| 1.1825 | 0.9150 | 140 | 1.1487 |
| 1.0579 | 0.9412 | 144 | 1.1495 |
| 1.0728 | 0.9673 | 148 | 1.1504 |
| 1.2134 | 0.9935 | 152 | 1.1508 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |