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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-fold2
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-fold2
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-fold2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0340
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.5122 | 0.0775 | 50 | 0.4118 |
| 0.0913 | 0.1550 | 100 | 0.0831 |
| 0.0638 | 0.2326 | 150 | 0.0662 |
| 0.0589 | 0.3101 | 200 | 0.0558 |
| 0.0628 | 0.3876 | 250 | 0.0528 |
| 0.0482 | 0.4651 | 300 | 0.0484 |
| 0.0429 | 0.5426 | 350 | 0.0443 |
| 0.0526 | 0.6202 | 400 | 0.0432 |
| 0.0446 | 0.6977 | 450 | 0.0391 |
| 0.0503 | 0.7752 | 500 | 0.0377 |
| 0.048 | 0.8527 | 550 | 0.0386 |
| 0.0636 | 0.9302 | 600 | 0.0439 |
| 0.0333 | 1.0078 | 650 | 0.0361 |
| 0.0319 | 1.0853 | 700 | 0.0385 |
| 0.033 | 1.1628 | 750 | 0.0357 |
| 0.0242 | 1.2403 | 800 | 0.0371 |
| 0.0363 | 1.3178 | 850 | 0.0343 |
| 0.0419 | 1.3953 | 900 | 0.0360 |
| 0.0444 | 1.4729 | 950 | 0.0349 |
| 0.0297 | 1.5504 | 1000 | 0.0362 |
| 0.0348 | 1.6279 | 1050 | 0.0348 |
| 0.0271 | 1.7054 | 1100 | 0.0354 |
| 0.0362 | 1.7829 | 1150 | 0.0366 |
| 0.034 | 1.8605 | 1200 | 0.0344 |
| 0.039 | 1.9380 | 1250 | 0.0344 |
| 0.0248 | 2.0155 | 1300 | 0.0340 |
| 0.0209 | 2.0930 | 1350 | 0.0369 |
| 0.0211 | 2.1705 | 1400 | 0.0352 |
| 0.0178 | 2.2481 | 1450 | 0.0379 |
| 0.026 | 2.3256 | 1500 | 0.0355 |
| 0.0166 | 2.4031 | 1550 | 0.0375 |
| 0.0237 | 2.4806 | 1600 | 0.0355 |
| 0.037 | 2.5581 | 1650 | 0.0347 |
| 0.0161 | 2.6357 | 1700 | 0.0387 |
| 0.0174 | 2.7132 | 1750 | 0.0383 |
| 0.0222 | 2.7907 | 1800 | 0.0363 |
| 0.0217 | 2.8682 | 1850 | 0.0376 |
| 0.0198 | 2.9457 | 1900 | 0.0362 |
| 0.0105 | 3.0233 | 1950 | 0.0388 |
| 0.0097 | 3.1008 | 2000 | 0.0440 |
| 0.008 | 3.1783 | 2050 | 0.0482 |
| 0.0114 | 3.2558 | 2100 | 0.0435 |
| 0.0112 | 3.3333 | 2150 | 0.0401 |
| 0.0079 | 3.4109 | 2200 | 0.0448 |
| 0.0133 | 3.4884 | 2250 | 0.0483 |
| 0.0128 | 3.5659 | 2300 | 0.0471 |
| 0.012 | 3.6434 | 2350 | 0.0467 |
| 0.0143 | 3.7209 | 2400 | 0.0464 |
| 0.0063 | 3.7984 | 2450 | 0.0487 |
| 0.0098 | 3.8760 | 2500 | 0.0470 |
| 0.0088 | 3.9535 | 2550 | 0.0461 |
| 0.0057 | 4.0310 | 2600 | 0.0467 |
| 0.006 | 4.1085 | 2650 | 0.0506 |
| 0.0058 | 4.1860 | 2700 | 0.0565 |
| 0.0057 | 4.2636 | 2750 | 0.0592 |
| 0.0043 | 4.3411 | 2800 | 0.0595 |
| 0.0043 | 4.4186 | 2850 | 0.0612 |
| 0.0055 | 4.4961 | 2900 | 0.0617 |
| 0.0029 | 4.5736 | 2950 | 0.0608 |
| 0.0029 | 4.6512 | 3000 | 0.0615 |
| 0.0039 | 4.7287 | 3050 | 0.0620 |
| 0.0034 | 4.8062 | 3100 | 0.0620 |
| 0.0061 | 4.8837 | 3150 | 0.0618 |
| 0.0045 | 4.9612 | 3200 | 0.0619 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |