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library_name: peft
license: other
base_model: mistralai/Ministral-8B-Instruct-2410
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: Ministral-8B-Instruct-2410-PsyCourse-fold8
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. -->
# Ministral-8B-Instruct-2410-PsyCourse-fold8
This model is a fine-tuned version of [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410) on the course-train-fold1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0316
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2582 | 0.0770 | 50 | 0.2416 |
| 0.0851 | 0.1539 | 100 | 0.0695 |
| 0.061 | 0.2309 | 150 | 0.0585 |
| 0.0577 | 0.3078 | 200 | 0.0543 |
| 0.0438 | 0.3848 | 250 | 0.0433 |
| 0.0407 | 0.4617 | 300 | 0.0464 |
| 0.0425 | 0.5387 | 350 | 0.0436 |
| 0.0485 | 0.6156 | 400 | 0.0431 |
| 0.0308 | 0.6926 | 450 | 0.0379 |
| 0.0281 | 0.7695 | 500 | 0.0374 |
| 0.0406 | 0.8465 | 550 | 0.0347 |
| 0.0335 | 0.9234 | 600 | 0.0349 |
| 0.0294 | 1.0004 | 650 | 0.0363 |
| 0.0334 | 1.0773 | 700 | 0.0368 |
| 0.0262 | 1.1543 | 750 | 0.0362 |
| 0.0278 | 1.2312 | 800 | 0.0343 |
| 0.0277 | 1.3082 | 850 | 0.0344 |
| 0.0201 | 1.3851 | 900 | 0.0337 |
| 0.0357 | 1.4621 | 950 | 0.0335 |
| 0.0314 | 1.5391 | 1000 | 0.0348 |
| 0.0312 | 1.6160 | 1050 | 0.0328 |
| 0.0342 | 1.6930 | 1100 | 0.0352 |
| 0.023 | 1.7699 | 1150 | 0.0324 |
| 0.021 | 1.8469 | 1200 | 0.0366 |
| 0.0276 | 1.9238 | 1250 | 0.0330 |
| 0.022 | 2.0008 | 1300 | 0.0320 |
| 0.017 | 2.0777 | 1350 | 0.0316 |
| 0.0208 | 2.1547 | 1400 | 0.0352 |
| 0.011 | 2.2316 | 1450 | 0.0366 |
| 0.0196 | 2.3086 | 1500 | 0.0345 |
| 0.0155 | 2.3855 | 1550 | 0.0382 |
| 0.012 | 2.4625 | 1600 | 0.0371 |
| 0.0199 | 2.5394 | 1650 | 0.0331 |
| 0.0209 | 2.6164 | 1700 | 0.0360 |
| 0.0228 | 2.6933 | 1750 | 0.0324 |
| 0.0192 | 2.7703 | 1800 | 0.0317 |
| 0.0204 | 2.8472 | 1850 | 0.0320 |
| 0.0165 | 2.9242 | 1900 | 0.0331 |
| 0.0225 | 3.0012 | 1950 | 0.0341 |
| 0.0083 | 3.0781 | 2000 | 0.0380 |
| 0.0119 | 3.1551 | 2050 | 0.0392 |
| 0.0069 | 3.2320 | 2100 | 0.0373 |
| 0.007 | 3.3090 | 2150 | 0.0396 |
| 0.0132 | 3.3859 | 2200 | 0.0377 |
| 0.0073 | 3.4629 | 2250 | 0.0368 |
| 0.0104 | 3.5398 | 2300 | 0.0389 |
| 0.005 | 3.6168 | 2350 | 0.0398 |
| 0.0117 | 3.6937 | 2400 | 0.0376 |
| 0.0066 | 3.7707 | 2450 | 0.0388 |
| 0.007 | 3.8476 | 2500 | 0.0385 |
| 0.0081 | 3.9246 | 2550 | 0.0408 |
| 0.0089 | 4.0015 | 2600 | 0.0399 |
| 0.0023 | 4.0785 | 2650 | 0.0415 |
| 0.0079 | 4.1554 | 2700 | 0.0443 |
| 0.0016 | 4.2324 | 2750 | 0.0446 |
| 0.0022 | 4.3093 | 2800 | 0.0454 |
| 0.0028 | 4.3863 | 2850 | 0.0465 |
| 0.004 | 4.4633 | 2900 | 0.0468 |
| 0.0024 | 4.5402 | 2950 | 0.0466 |
| 0.0031 | 4.6172 | 3000 | 0.0466 |
| 0.0043 | 4.6941 | 3050 | 0.0469 |
| 0.0035 | 4.7711 | 3100 | 0.0468 |
| 0.0032 | 4.8480 | 3150 | 0.0467 |
| 0.003 | 4.9250 | 3200 | 0.0468 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |