---
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
- name: LLaMA3_AAID_mixed_train
  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_AAID_mixed_train

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the AAID_mixed dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002

## 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: 0.0003
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4761        | 0.2023 | 200  | 0.4195          |
| 0.3788        | 0.4047 | 400  | 0.3681          |
| 0.3038        | 0.6070 | 600  | 0.2551          |
| 0.1763        | 0.8093 | 800  | 0.1140          |
| 0.0719        | 1.0116 | 1000 | 0.0454          |
| 0.0265        | 1.2140 | 1200 | 0.0216          |
| 0.0137        | 1.4163 | 1400 | 0.0099          |
| 0.007         | 1.6186 | 1600 | 0.0052          |
| 0.0036        | 1.8209 | 1800 | 0.0023          |
| 0.0015        | 2.0233 | 2000 | 0.0010          |
| 0.0006        | 2.2256 | 2200 | 0.0005          |
| 0.0004        | 2.4279 | 2400 | 0.0003          |
| 0.0002        | 2.6302 | 2600 | 0.0002          |
| 0.0002        | 2.8326 | 2800 | 0.0002          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1