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---
library_name: transformers
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Na_M2_1000steps_1e6rate_SFT
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. -->
# Na_M2_1000steps_1e6rate_SFT
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.
It achieves the following results on the evaluation set:
- Loss: 0.1550
## 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: 1e-06
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.349 | 0.2667 | 50 | 0.3425 |
| 0.2797 | 0.5333 | 100 | 0.2671 |
| 0.1882 | 0.8 | 150 | 0.1831 |
| 0.1593 | 1.0667 | 200 | 0.1748 |
| 0.1609 | 1.3333 | 250 | 0.1586 |
| 0.1544 | 1.6 | 300 | 0.1575 |
| 0.1558 | 1.8667 | 350 | 0.1564 |
| 0.1524 | 2.1333 | 400 | 0.1574 |
| 0.1527 | 2.4 | 450 | 0.1568 |
| 0.1564 | 2.6667 | 500 | 0.1554 |
| 0.1523 | 2.9333 | 550 | 0.1563 |
| 0.1519 | 3.2 | 600 | 0.1558 |
| 0.1532 | 3.4667 | 650 | 0.1555 |
| 0.1508 | 3.7333 | 700 | 0.1549 |
| 0.1518 | 4.0 | 750 | 0.1550 |
| 0.1507 | 4.2667 | 800 | 0.1551 |
| 0.1501 | 4.5333 | 850 | 0.1550 |
| 0.1477 | 4.8 | 900 | 0.1549 |
| 0.1464 | 5.0667 | 950 | 0.1550 |
| 0.1488 | 5.3333 | 1000 | 0.1550 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
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