---
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-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. -->

# Llama3-OpenBioLLM-8B-PsyCourse-fold3

This model is a fine-tuned version of [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) on the course-train-fold3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0350

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.4566        | 0.0753 | 50   | 0.3151          |
| 0.1039        | 0.1505 | 100  | 0.0934          |
| 0.0879        | 0.2258 | 150  | 0.0729          |
| 0.0684        | 0.3011 | 200  | 0.0644          |
| 0.0696        | 0.3763 | 250  | 0.0604          |
| 0.064         | 0.4516 | 300  | 0.0538          |
| 0.048         | 0.5269 | 350  | 0.0553          |
| 0.0568        | 0.6021 | 400  | 0.0504          |
| 0.0548        | 0.6774 | 450  | 0.0462          |
| 0.0387        | 0.7527 | 500  | 0.0457          |
| 0.0454        | 0.8279 | 550  | 0.0439          |
| 0.0343        | 0.9032 | 600  | 0.0421          |
| 0.0363        | 0.9785 | 650  | 0.0402          |
| 0.0272        | 1.0537 | 700  | 0.0415          |
| 0.0324        | 1.1290 | 750  | 0.0384          |
| 0.0394        | 1.2043 | 800  | 0.0429          |
| 0.0297        | 1.2795 | 850  | 0.0411          |
| 0.0423        | 1.3548 | 900  | 0.0396          |
| 0.0324        | 1.4300 | 950  | 0.0372          |
| 0.0311        | 1.5053 | 1000 | 0.0395          |
| 0.0309        | 1.5806 | 1050 | 0.0407          |
| 0.0233        | 1.6558 | 1100 | 0.0377          |
| 0.0455        | 1.7311 | 1150 | 0.0354          |
| 0.0329        | 1.8064 | 1200 | 0.0364          |
| 0.0352        | 1.8816 | 1250 | 0.0351          |
| 0.029         | 1.9569 | 1300 | 0.0350          |
| 0.0187        | 2.0322 | 1350 | 0.0363          |
| 0.025         | 2.1074 | 1400 | 0.0380          |
| 0.0209        | 2.1827 | 1450 | 0.0377          |
| 0.0224        | 2.2580 | 1500 | 0.0407          |
| 0.0261        | 2.3332 | 1550 | 0.0393          |
| 0.0156        | 2.4085 | 1600 | 0.0387          |
| 0.0173        | 2.4838 | 1650 | 0.0388          |
| 0.0228        | 2.5590 | 1700 | 0.0367          |
| 0.0328        | 2.6343 | 1750 | 0.0371          |
| 0.0264        | 2.7096 | 1800 | 0.0393          |
| 0.0235        | 2.7848 | 1850 | 0.0364          |
| 0.0204        | 2.8601 | 1900 | 0.0386          |
| 0.0207        | 2.9354 | 1950 | 0.0372          |
| 0.01          | 3.0106 | 2000 | 0.0390          |
| 0.0103        | 3.0859 | 2050 | 0.0431          |
| 0.0096        | 3.1612 | 2100 | 0.0418          |
| 0.012         | 3.2364 | 2150 | 0.0431          |
| 0.0098        | 3.3117 | 2200 | 0.0443          |
| 0.0104        | 3.3870 | 2250 | 0.0466          |
| 0.0061        | 3.4622 | 2300 | 0.0481          |
| 0.0051        | 3.5375 | 2350 | 0.0463          |
| 0.0085        | 3.6128 | 2400 | 0.0458          |
| 0.0093        | 3.6880 | 2450 | 0.0467          |
| 0.0103        | 3.7633 | 2500 | 0.0477          |
| 0.0066        | 3.8386 | 2550 | 0.0487          |
| 0.0101        | 3.9138 | 2600 | 0.0477          |
| 0.0104        | 3.9891 | 2650 | 0.0455          |
| 0.0073        | 4.0644 | 2700 | 0.0460          |
| 0.003         | 4.1396 | 2750 | 0.0478          |
| 0.0025        | 4.2149 | 2800 | 0.0498          |
| 0.0051        | 4.2901 | 2850 | 0.0506          |
| 0.0078        | 4.3654 | 2900 | 0.0516          |
| 0.0019        | 4.4407 | 2950 | 0.0518          |
| 0.0029        | 4.5159 | 3000 | 0.0521          |
| 0.0048        | 4.5912 | 3050 | 0.0521          |
| 0.0033        | 4.6665 | 3100 | 0.0525          |
| 0.002         | 4.7417 | 3150 | 0.0525          |
| 0.0017        | 4.8170 | 3200 | 0.0528          |
| 0.003         | 4.8923 | 3250 | 0.0529          |
| 0.0021        | 4.9675 | 3300 | 0.0526          |


### Framework versions

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