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---
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-fold6
  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-fold6

This model is a fine-tuned version of [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B) on the course-train-fold6 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0381

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.4971        | 0.0751 | 50   | 0.3237          |
| 0.1179        | 0.1502 | 100  | 0.0942          |
| 0.0891        | 0.2254 | 150  | 0.0797          |
| 0.0539        | 0.3005 | 200  | 0.0598          |
| 0.065         | 0.3756 | 250  | 0.0575          |
| 0.052         | 0.4507 | 300  | 0.0530          |
| 0.0535        | 0.5258 | 350  | 0.0545          |
| 0.0512        | 0.6009 | 400  | 0.0466          |
| 0.0597        | 0.6761 | 450  | 0.0495          |
| 0.0509        | 0.7512 | 500  | 0.0469          |
| 0.0624        | 0.8263 | 550  | 0.0431          |
| 0.035         | 0.9014 | 600  | 0.0458          |
| 0.0546        | 0.9765 | 650  | 0.0444          |
| 0.0404        | 1.0516 | 700  | 0.0443          |
| 0.0342        | 1.1268 | 750  | 0.0434          |
| 0.0298        | 1.2019 | 800  | 0.0428          |
| 0.0348        | 1.2770 | 850  | 0.0407          |
| 0.0291        | 1.3521 | 900  | 0.0397          |
| 0.0373        | 1.4272 | 950  | 0.0397          |
| 0.0289        | 1.5023 | 1000 | 0.0406          |
| 0.0339        | 1.5775 | 1050 | 0.0450          |
| 0.0244        | 1.6526 | 1100 | 0.0412          |
| 0.0288        | 1.7277 | 1150 | 0.0402          |
| 0.036         | 1.8028 | 1200 | 0.0395          |
| 0.0334        | 1.8779 | 1250 | 0.0392          |
| 0.0568        | 1.9531 | 1300 | 0.0428          |
| 0.0206        | 2.0282 | 1350 | 0.0409          |
| 0.0226        | 2.1033 | 1400 | 0.0408          |
| 0.0257        | 2.1784 | 1450 | 0.0407          |
| 0.0138        | 2.2535 | 1500 | 0.0414          |
| 0.0167        | 2.3286 | 1550 | 0.0409          |
| 0.0217        | 2.4038 | 1600 | 0.0388          |
| 0.0195        | 2.4789 | 1650 | 0.0427          |
| 0.0223        | 2.5540 | 1700 | 0.0437          |
| 0.0195        | 2.6291 | 1750 | 0.0428          |
| 0.0218        | 2.7042 | 1800 | 0.0409          |
| 0.0189        | 2.7793 | 1850 | 0.0401          |
| 0.0195        | 2.8545 | 1900 | 0.0381          |
| 0.0188        | 2.9296 | 1950 | 0.0399          |
| 0.0105        | 3.0047 | 2000 | 0.0412          |
| 0.0073        | 3.0798 | 2050 | 0.0443          |
| 0.0082        | 3.1549 | 2100 | 0.0466          |
| 0.0107        | 3.2300 | 2150 | 0.0490          |
| 0.0143        | 3.3052 | 2200 | 0.0447          |
| 0.005         | 3.3803 | 2250 | 0.0471          |
| 0.0079        | 3.4554 | 2300 | 0.0481          |
| 0.0085        | 3.5305 | 2350 | 0.0501          |
| 0.0073        | 3.6056 | 2400 | 0.0468          |
| 0.0034        | 3.6808 | 2450 | 0.0481          |
| 0.0053        | 3.7559 | 2500 | 0.0496          |
| 0.0052        | 3.8310 | 2550 | 0.0498          |
| 0.0117        | 3.9061 | 2600 | 0.0501          |
| 0.0082        | 3.9812 | 2650 | 0.0500          |
| 0.0054        | 4.0563 | 2700 | 0.0516          |
| 0.0019        | 4.1315 | 2750 | 0.0546          |
| 0.0048        | 4.2066 | 2800 | 0.0565          |
| 0.0026        | 4.2817 | 2850 | 0.0583          |
| 0.001         | 4.3568 | 2900 | 0.0608          |
| 0.0022        | 4.4319 | 2950 | 0.0609          |
| 0.0036        | 4.5070 | 3000 | 0.0615          |
| 0.0006        | 4.5822 | 3050 | 0.0621          |
| 0.0019        | 4.6573 | 3100 | 0.0624          |
| 0.0027        | 4.7324 | 3150 | 0.0628          |
| 0.0022        | 4.8075 | 3200 | 0.0629          |
| 0.002         | 4.8826 | 3250 | 0.0630          |
| 0.0063        | 4.9577 | 3300 | 0.0628          |


### Framework versions

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