---
library_name: peft
license: llama3
base_model: aaditya/Llama3-OpenBioLLM-8B
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: Llama3-OpenBioLLM-8B-PsyCourse-fold9
  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. -->

# Llama3-OpenBioLLM-8B-PsyCourse-fold9

This model is a fine-tuned version of [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) on the course-train-fold9 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0367

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.5046        | 0.0768 | 50   | 0.3063          |
| 0.1059        | 0.1535 | 100  | 0.0842          |
| 0.0783        | 0.2303 | 150  | 0.0695          |
| 0.0635        | 0.3070 | 200  | 0.0595          |
| 0.075         | 0.3838 | 250  | 0.0530          |
| 0.065         | 0.4606 | 300  | 0.0491          |
| 0.0474        | 0.5373 | 350  | 0.0478          |
| 0.0461        | 0.6141 | 400  | 0.0493          |
| 0.0533        | 0.6908 | 450  | 0.0540          |
| 0.048         | 0.7676 | 500  | 0.0457          |
| 0.0694        | 0.8444 | 550  | 0.0475          |
| 0.0396        | 0.9211 | 600  | 0.0416          |
| 0.0412        | 0.9979 | 650  | 0.0386          |
| 0.0339        | 1.0746 | 700  | 0.0457          |
| 0.0357        | 1.1514 | 750  | 0.0434          |
| 0.0336        | 1.2282 | 800  | 0.0408          |
| 0.0342        | 1.3049 | 850  | 0.0414          |
| 0.0307        | 1.3817 | 900  | 0.0407          |
| 0.0312        | 1.4585 | 950  | 0.0379          |
| 0.0314        | 1.5352 | 1000 | 0.0392          |
| 0.0229        | 1.6120 | 1050 | 0.0367          |
| 0.0337        | 1.6887 | 1100 | 0.0372          |
| 0.028         | 1.7655 | 1150 | 0.0379          |
| 0.0191        | 1.8423 | 1200 | 0.0388          |
| 0.0348        | 1.9190 | 1250 | 0.0411          |
| 0.0469        | 1.9958 | 1300 | 0.0399          |
| 0.0193        | 2.0725 | 1350 | 0.0412          |
| 0.0168        | 2.1493 | 1400 | 0.0416          |
| 0.019         | 2.2261 | 1450 | 0.0390          |
| 0.0268        | 2.3028 | 1500 | 0.0390          |
| 0.0221        | 2.3796 | 1550 | 0.0412          |
| 0.0264        | 2.4563 | 1600 | 0.0408          |
| 0.0248        | 2.5331 | 1650 | 0.0390          |
| 0.018         | 2.6099 | 1700 | 0.0397          |
| 0.0148        | 2.6866 | 1750 | 0.0406          |
| 0.0228        | 2.7634 | 1800 | 0.0416          |
| 0.0216        | 2.8401 | 1850 | 0.0392          |
| 0.021         | 2.9169 | 1900 | 0.0396          |
| 0.016         | 2.9937 | 1950 | 0.0393          |
| 0.0055        | 3.0704 | 2000 | 0.0446          |
| 0.0128        | 3.1472 | 2050 | 0.0464          |
| 0.0105        | 3.2239 | 2100 | 0.0466          |
| 0.009         | 3.3007 | 2150 | 0.0450          |
| 0.0087        | 3.3775 | 2200 | 0.0487          |
| 0.0102        | 3.4542 | 2250 | 0.0473          |
| 0.007         | 3.5310 | 2300 | 0.0486          |
| 0.0113        | 3.6078 | 2350 | 0.0490          |
| 0.0066        | 3.6845 | 2400 | 0.0522          |
| 0.0064        | 3.7613 | 2450 | 0.0510          |
| 0.0095        | 3.8380 | 2500 | 0.0514          |
| 0.0089        | 3.9148 | 2550 | 0.0521          |
| 0.0065        | 3.9916 | 2600 | 0.0524          |
| 0.0034        | 4.0683 | 2650 | 0.0540          |
| 0.0032        | 4.1451 | 2700 | 0.0563          |
| 0.0026        | 4.2218 | 2750 | 0.0564          |
| 0.0024        | 4.2986 | 2800 | 0.0586          |
| 0.0021        | 4.3754 | 2850 | 0.0595          |
| 0.0043        | 4.4521 | 2900 | 0.0604          |
| 0.0019        | 4.5289 | 2950 | 0.0607          |
| 0.0011        | 4.6056 | 3000 | 0.0610          |
| 0.0018        | 4.6824 | 3050 | 0.0617          |
| 0.0051        | 4.7592 | 3100 | 0.0614          |
| 0.0032        | 4.8359 | 3150 | 0.0617          |
| 0.001         | 4.9127 | 3200 | 0.0617          |
| 0.0029        | 4.9894 | 3250 | 0.0618          |


### Framework versions

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