|
--- |
|
base_model: mistralai/Mistral-7B-Instruct-v0.3 |
|
library_name: peft |
|
license: apache-2.0 |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: pgd_mistral_8bits_lr9e-05_alpha32_rk4_do0.2_wd2.0e-02 |
|
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. --> |
|
|
|
# pgd_mistral_8bits_lr9e-05_alpha32_rk4_do0.2_wd2.0e-02 |
|
|
|
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co./mistralai/Mistral-7B-Instruct-v0.3) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.8425 |
|
|
|
## 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: 9e-05 |
|
- train_batch_size: 3 |
|
- eval_batch_size: 3 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 12 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 20 |
|
- num_epochs: 10 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:----:|:---------------:| |
|
| 2.6427 | 0.9778 | 22 | 1.6811 | |
|
| 1.0715 | 2.0 | 45 | 0.9352 | |
|
| 0.8713 | 2.9778 | 67 | 0.8724 | |
|
| 0.7894 | 4.0 | 90 | 0.8532 | |
|
| 0.8064 | 4.9778 | 112 | 0.8457 | |
|
| 0.7582 | 6.0 | 135 | 0.8422 | |
|
| 0.7828 | 6.9778 | 157 | 0.8422 | |
|
| 0.7403 | 8.0 | 180 | 0.8403 | |
|
| 0.7665 | 8.9778 | 202 | 0.8410 | |
|
| 0.7214 | 9.7778 | 220 | 0.8425 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.12.0 |
|
- Transformers 4.42.4 |
|
- Pytorch 2.4.0+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |