|
--- |
|
base_model: unsloth/mistral-7b-v0.3 |
|
library_name: peft |
|
license: apache-2.0 |
|
tags: |
|
- unsloth |
|
- generated_from_trainer |
|
model-index: |
|
- name: Mistral-7B-v0.3_pct_ortho_r16 |
|
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_ortho_r16 |
|
|
|
This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 2.0091 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 2 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 32 |
|
- 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 | |
|
|:-------------:|:------:|:----:|:---------------:| |
|
| 1.9566 | 0.0206 | 8 | 2.0118 | |
|
| 2.0191 | 0.0413 | 16 | 1.9983 | |
|
| 2.0779 | 0.0619 | 24 | 2.0212 | |
|
| 2.0339 | 0.0825 | 32 | 2.0205 | |
|
| 2.0429 | 0.1032 | 40 | 2.0132 | |
|
| 2.0601 | 0.1238 | 48 | 2.0219 | |
|
| 2.041 | 0.1445 | 56 | 2.0171 | |
|
| 2.0602 | 0.1651 | 64 | 2.0230 | |
|
| 2.0341 | 0.1857 | 72 | 2.0311 | |
|
| 2.0378 | 0.2064 | 80 | 2.0319 | |
|
| 2.0961 | 0.2270 | 88 | 2.0402 | |
|
| 2.106 | 0.2476 | 96 | 2.0208 | |
|
| 2.1219 | 0.2683 | 104 | 2.0328 | |
|
| 2.0569 | 0.2889 | 112 | 2.0528 | |
|
| 2.1062 | 0.3096 | 120 | 2.0355 | |
|
| 2.0522 | 0.3302 | 128 | 2.0365 | |
|
| 2.0631 | 0.3508 | 136 | 2.0300 | |
|
| 2.1052 | 0.3715 | 144 | 2.0409 | |
|
| 2.0875 | 0.3921 | 152 | 2.0454 | |
|
| 2.0854 | 0.4127 | 160 | 2.0273 | |
|
| 2.0533 | 0.4334 | 168 | 2.0529 | |
|
| 2.1096 | 0.4540 | 176 | 2.0373 | |
|
| 2.0288 | 0.4746 | 184 | 2.0289 | |
|
| 2.1344 | 0.4953 | 192 | 2.0375 | |
|
| 2.0952 | 0.5159 | 200 | 2.0445 | |
|
| 2.0613 | 0.5366 | 208 | 2.0374 | |
|
| 2.0441 | 0.5572 | 216 | 2.0225 | |
|
| 2.0493 | 0.5778 | 224 | 2.0380 | |
|
| 2.0568 | 0.5985 | 232 | 2.0219 | |
|
| 2.0477 | 0.6191 | 240 | 2.0261 | |
|
| 2.1065 | 0.6397 | 248 | 2.0310 | |
|
| 2.0245 | 0.6604 | 256 | 2.0208 | |
|
| 2.1013 | 0.6810 | 264 | 2.0270 | |
|
| 2.0356 | 0.7017 | 272 | 2.0205 | |
|
| 2.0815 | 0.7223 | 280 | 2.0117 | |
|
| 2.0898 | 0.7429 | 288 | 2.0175 | |
|
| 2.0529 | 0.7636 | 296 | 2.0171 | |
|
| 2.0281 | 0.7842 | 304 | 2.0134 | |
|
| 2.0473 | 0.8048 | 312 | 2.0150 | |
|
| 2.0315 | 0.8255 | 320 | 2.0088 | |
|
| 2.0215 | 0.8461 | 328 | 2.0071 | |
|
| 2.0003 | 0.8667 | 336 | 2.0093 | |
|
| 2.0561 | 0.8874 | 344 | 2.0136 | |
|
| 2.0407 | 0.9080 | 352 | 2.0132 | |
|
| 2.0257 | 0.9287 | 360 | 2.0105 | |
|
| 2.0294 | 0.9493 | 368 | 2.0090 | |
|
| 2.0321 | 0.9699 | 376 | 2.0089 | |
|
| 2.0516 | 0.9906 | 384 | 2.0091 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.12.0 |
|
- Transformers 4.44.0 |
|
- Pytorch 2.4.0+cu121 |
|
- Datasets 2.20.0 |
|
- Tokenizers 0.19.1 |
