---
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-fold7
  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-fold7

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-fold7 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0324

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.6247        | 0.0764 | 50   | 0.4018          |
| 0.1081        | 0.1528 | 100  | 0.0778          |
| 0.0687        | 0.2292 | 150  | 0.0614          |
| 0.0565        | 0.3056 | 200  | 0.0577          |
| 0.0612        | 0.3820 | 250  | 0.0510          |
| 0.0426        | 0.4584 | 300  | 0.0490          |
| 0.0657        | 0.5348 | 350  | 0.0489          |
| 0.0423        | 0.6112 | 400  | 0.0453          |
| 0.056         | 0.6875 | 450  | 0.0438          |
| 0.0393        | 0.7639 | 500  | 0.0396          |
| 0.0321        | 0.8403 | 550  | 0.0387          |
| 0.0493        | 0.9167 | 600  | 0.0400          |
| 0.0578        | 0.9931 | 650  | 0.0393          |
| 0.0371        | 1.0695 | 700  | 0.0376          |
| 0.0228        | 1.1459 | 750  | 0.0364          |
| 0.0301        | 1.2223 | 800  | 0.0388          |
| 0.0383        | 1.2987 | 850  | 0.0371          |
| 0.0403        | 1.3751 | 900  | 0.0364          |
| 0.0446        | 1.4515 | 950  | 0.0381          |
| 0.0282        | 1.5279 | 1000 | 0.0352          |
| 0.0303        | 1.6043 | 1050 | 0.0363          |
| 0.0287        | 1.6807 | 1100 | 0.0400          |
| 0.0562        | 1.7571 | 1150 | 0.0355          |
| 0.0354        | 1.8335 | 1200 | 0.0350          |
| 0.0379        | 1.9099 | 1250 | 0.0377          |
| 0.0272        | 1.9862 | 1300 | 0.0353          |
| 0.0232        | 2.0626 | 1350 | 0.0356          |
| 0.0203        | 2.1390 | 1400 | 0.0356          |
| 0.0156        | 2.2154 | 1450 | 0.0388          |
| 0.0207        | 2.2918 | 1500 | 0.0344          |
| 0.0202        | 2.3682 | 1550 | 0.0345          |
| 0.0196        | 2.4446 | 1600 | 0.0345          |
| 0.0244        | 2.5210 | 1650 | 0.0345          |
| 0.0199        | 2.5974 | 1700 | 0.0355          |
| 0.0221        | 2.6738 | 1750 | 0.0338          |
| 0.0271        | 2.7502 | 1800 | 0.0324          |
| 0.0265        | 2.8266 | 1850 | 0.0324          |
| 0.0214        | 2.9030 | 1900 | 0.0343          |
| 0.0214        | 2.9794 | 1950 | 0.0354          |
| 0.0159        | 3.0558 | 2000 | 0.0351          |
| 0.0147        | 3.1322 | 2050 | 0.0364          |
| 0.0125        | 3.2086 | 2100 | 0.0382          |
| 0.0135        | 3.2850 | 2150 | 0.0398          |
| 0.0138        | 3.3613 | 2200 | 0.0406          |
| 0.0143        | 3.4377 | 2250 | 0.0450          |
| 0.0082        | 3.5141 | 2300 | 0.0436          |
| 0.0177        | 3.5905 | 2350 | 0.0426          |
| 0.0119        | 3.6669 | 2400 | 0.0396          |
| 0.0061        | 3.7433 | 2450 | 0.0407          |
| 0.0101        | 3.8197 | 2500 | 0.0403          |
| 0.0105        | 3.8961 | 2550 | 0.0398          |
| 0.0087        | 3.9725 | 2600 | 0.0390          |
| 0.0058        | 4.0489 | 2650 | 0.0419          |
| 0.0065        | 4.1253 | 2700 | 0.0461          |
| 0.0072        | 4.2017 | 2750 | 0.0491          |
| 0.0018        | 4.2781 | 2800 | 0.0508          |
| 0.0053        | 4.3545 | 2850 | 0.0508          |
| 0.0024        | 4.4309 | 2900 | 0.0524          |
| 0.0042        | 4.5073 | 2950 | 0.0534          |
| 0.0056        | 4.5837 | 3000 | 0.0535          |
| 0.0023        | 4.6600 | 3050 | 0.0541          |
| 0.0028        | 4.7364 | 3100 | 0.0541          |
| 0.0063        | 4.8128 | 3150 | 0.0538          |
| 0.0034        | 4.8892 | 3200 | 0.0534          |
| 0.0077        | 4.9656 | 3250 | 0.0536          |


### Framework versions

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