---
library_name: peft
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: Llama-3.1-8B-Instruct-PsyCourse-fold3
  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. -->

# Llama-3.1-8B-Instruct-PsyCourse-fold3

This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the course-train-fold3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0352

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.5742        | 0.0753 | 50   | 0.4037          |
| 0.0981        | 0.1505 | 100  | 0.0890          |
| 0.0775        | 0.2258 | 150  | 0.0663          |
| 0.075         | 0.3011 | 200  | 0.0574          |
| 0.0587        | 0.3763 | 250  | 0.0533          |
| 0.0617        | 0.4516 | 300  | 0.0547          |
| 0.0431        | 0.5269 | 350  | 0.0519          |
| 0.0573        | 0.6021 | 400  | 0.0479          |
| 0.0504        | 0.6774 | 450  | 0.0438          |
| 0.0341        | 0.7527 | 500  | 0.0428          |
| 0.0448        | 0.8279 | 550  | 0.0440          |
| 0.0373        | 0.9032 | 600  | 0.0414          |
| 0.0369        | 0.9785 | 650  | 0.0414          |
| 0.0266        | 1.0537 | 700  | 0.0422          |
| 0.0337        | 1.1290 | 750  | 0.0380          |
| 0.0379        | 1.2043 | 800  | 0.0424          |
| 0.0297        | 1.2795 | 850  | 0.0413          |
| 0.0417        | 1.3548 | 900  | 0.0389          |
| 0.0342        | 1.4300 | 950  | 0.0393          |
| 0.033         | 1.5053 | 1000 | 0.0387          |
| 0.0304        | 1.5806 | 1050 | 0.0412          |
| 0.0225        | 1.6558 | 1100 | 0.0380          |
| 0.0406        | 1.7311 | 1150 | 0.0359          |
| 0.0314        | 1.8064 | 1200 | 0.0378          |
| 0.0345        | 1.8816 | 1250 | 0.0352          |
| 0.0314        | 1.9569 | 1300 | 0.0352          |
| 0.0232        | 2.0322 | 1350 | 0.0370          |
| 0.0298        | 2.1074 | 1400 | 0.0358          |
| 0.0224        | 2.1827 | 1450 | 0.0376          |
| 0.0251        | 2.2580 | 1500 | 0.0403          |
| 0.0303        | 2.3332 | 1550 | 0.0377          |
| 0.0174        | 2.4085 | 1600 | 0.0399          |
| 0.02          | 2.4838 | 1650 | 0.0393          |
| 0.0239        | 2.5590 | 1700 | 0.0386          |
| 0.0377        | 2.6343 | 1750 | 0.0377          |
| 0.0266        | 2.7096 | 1800 | 0.0373          |
| 0.0229        | 2.7848 | 1850 | 0.0356          |
| 0.0257        | 2.8601 | 1900 | 0.0409          |
| 0.021         | 2.9354 | 1950 | 0.0365          |
| 0.0137        | 3.0106 | 2000 | 0.0382          |
| 0.0119        | 3.0859 | 2050 | 0.0439          |
| 0.0116        | 3.1612 | 2100 | 0.0427          |
| 0.0131        | 3.2364 | 2150 | 0.0435          |
| 0.0132        | 3.3117 | 2200 | 0.0436          |
| 0.0095        | 3.3870 | 2250 | 0.0448          |
| 0.0101        | 3.4622 | 2300 | 0.0486          |
| 0.0068        | 3.5375 | 2350 | 0.0472          |
| 0.0133        | 3.6128 | 2400 | 0.0447          |
| 0.0155        | 3.6880 | 2450 | 0.0423          |
| 0.0118        | 3.7633 | 2500 | 0.0446          |
| 0.0104        | 3.8386 | 2550 | 0.0464          |
| 0.0149        | 3.9138 | 2600 | 0.0434          |
| 0.0126        | 3.9891 | 2650 | 0.0439          |
| 0.0066        | 4.0644 | 2700 | 0.0464          |
| 0.0048        | 4.1396 | 2750 | 0.0502          |
| 0.0052        | 4.2149 | 2800 | 0.0543          |
| 0.0051        | 4.2901 | 2850 | 0.0537          |
| 0.0102        | 4.3654 | 2900 | 0.0547          |
| 0.0052        | 4.4407 | 2950 | 0.0546          |
| 0.0029        | 4.5159 | 3000 | 0.0548          |
| 0.0085        | 4.5912 | 3050 | 0.0552          |
| 0.0049        | 4.6665 | 3100 | 0.0551          |
| 0.0054        | 4.7417 | 3150 | 0.0553          |
| 0.0035        | 4.8170 | 3200 | 0.0553          |
| 0.0041        | 4.8923 | 3250 | 0.0554          |
| 0.0045        | 4.9675 | 3300 | 0.0553          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3