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---
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-fold4
  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-fold4

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-fold4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0344

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.5681        | 0.0763 | 50   | 0.3814          |
| 0.122         | 0.1527 | 100  | 0.0796          |
| 0.0541        | 0.2290 | 150  | 0.0592          |
| 0.0502        | 0.3053 | 200  | 0.0522          |
| 0.0666        | 0.3816 | 250  | 0.0539          |
| 0.046         | 0.4580 | 300  | 0.0493          |
| 0.0458        | 0.5343 | 350  | 0.0527          |
| 0.0448        | 0.6106 | 400  | 0.0488          |
| 0.0567        | 0.6870 | 450  | 0.0462          |
| 0.0358        | 0.7633 | 500  | 0.0410          |
| 0.0445        | 0.8396 | 550  | 0.0407          |
| 0.0462        | 0.9159 | 600  | 0.0407          |
| 0.0363        | 0.9923 | 650  | 0.0410          |
| 0.0343        | 1.0686 | 700  | 0.0370          |
| 0.0413        | 1.1449 | 750  | 0.0378          |
| 0.0322        | 1.2213 | 800  | 0.0398          |
| 0.0342        | 1.2976 | 850  | 0.0385          |
| 0.0337        | 1.3739 | 900  | 0.0436          |
| 0.0295        | 1.4502 | 950  | 0.0373          |
| 0.0267        | 1.5266 | 1000 | 0.0386          |
| 0.0287        | 1.6029 | 1050 | 0.0380          |
| 0.0504        | 1.6792 | 1100 | 0.0388          |
| 0.0317        | 1.7556 | 1150 | 0.0391          |
| 0.0448        | 1.8319 | 1200 | 0.0366          |
| 0.0278        | 1.9082 | 1250 | 0.0362          |
| 0.0347        | 1.9845 | 1300 | 0.0344          |
| 0.0201        | 2.0609 | 1350 | 0.0355          |
| 0.0238        | 2.1372 | 1400 | 0.0357          |
| 0.0299        | 2.2135 | 1450 | 0.0371          |
| 0.0155        | 2.2899 | 1500 | 0.0384          |
| 0.0157        | 2.3662 | 1550 | 0.0391          |
| 0.0222        | 2.4425 | 1600 | 0.0370          |
| 0.0245        | 2.5188 | 1650 | 0.0360          |
| 0.0206        | 2.5952 | 1700 | 0.0376          |
| 0.0198        | 2.6715 | 1750 | 0.0363          |
| 0.0209        | 2.7478 | 1800 | 0.0370          |
| 0.026         | 2.8242 | 1850 | 0.0362          |
| 0.0197        | 2.9005 | 1900 | 0.0358          |
| 0.0291        | 2.9768 | 1950 | 0.0355          |
| 0.0091        | 3.0531 | 2000 | 0.0416          |
| 0.0132        | 3.1295 | 2050 | 0.0421          |
| 0.0115        | 3.2058 | 2100 | 0.0443          |
| 0.0131        | 3.2821 | 2150 | 0.0459          |
| 0.0132        | 3.3585 | 2200 | 0.0409          |
| 0.0077        | 3.4348 | 2250 | 0.0445          |
| 0.0156        | 3.5111 | 2300 | 0.0444          |
| 0.0125        | 3.5874 | 2350 | 0.0480          |
| 0.0089        | 3.6638 | 2400 | 0.0499          |
| 0.0125        | 3.7401 | 2450 | 0.0467          |
| 0.0115        | 3.8164 | 2500 | 0.0447          |
| 0.0062        | 3.8928 | 2550 | 0.0449          |
| 0.0112        | 3.9691 | 2600 | 0.0462          |
| 0.005         | 4.0454 | 2650 | 0.0465          |
| 0.0065        | 4.1217 | 2700 | 0.0502          |
| 0.0021        | 4.1981 | 2750 | 0.0543          |
| 0.0033        | 4.2744 | 2800 | 0.0556          |
| 0.0068        | 4.3507 | 2850 | 0.0572          |
| 0.0015        | 4.4271 | 2900 | 0.0599          |
| 0.0036        | 4.5034 | 2950 | 0.0602          |
| 0.0027        | 4.5797 | 3000 | 0.0615          |
| 0.0013        | 4.6560 | 3050 | 0.0615          |
| 0.0056        | 4.7324 | 3100 | 0.0618          |
| 0.0028        | 4.8087 | 3150 | 0.0618          |
| 0.0044        | 4.8850 | 3200 | 0.0620          |
| 0.0061        | 4.9614 | 3250 | 0.0622          |


### Framework versions

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