---
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- generator
library_name: peft
license: llama3
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Meta-Llama-3-8B_AviationQA-cosine
  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. -->

# Meta-Llama-3-8B_AviationQA-cosine

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 generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6061

## 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: 3
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7872        | 0.0590 | 50   | 0.7652          |
| 0.7373        | 0.1181 | 100  | 0.7328          |
| 0.7242        | 0.1771 | 150  | 0.7182          |
| 0.7143        | 0.2361 | 200  | 0.7107          |
| 0.73          | 0.2952 | 250  | 0.7046          |
| 0.7159        | 0.3542 | 300  | 0.6973          |
| 0.7211        | 0.4132 | 350  | 0.6921          |
| 0.7096        | 0.4723 | 400  | 0.6873          |
| 0.6845        | 0.5313 | 450  | 0.6824          |
| 0.7251        | 0.5903 | 500  | 0.6783          |
| 0.6685        | 0.6494 | 550  | 0.6720          |
| 0.697         | 0.7084 | 600  | 0.6667          |
| 0.7006        | 0.7674 | 650  | 0.6639          |
| 0.6952        | 0.8264 | 700  | 0.6618          |
| 0.6649        | 0.8855 | 750  | 0.6596          |
| 0.6877        | 0.9445 | 800  | 0.6553          |
| 0.6673        | 1.0035 | 850  | 0.6531          |
| 0.6611        | 1.0626 | 900  | 0.6487          |
| 0.6971        | 1.1216 | 950  | 0.6452          |
| 0.6652        | 1.1806 | 1000 | 0.6423          |
| 0.645         | 1.2397 | 1050 | 0.6397          |
| 0.6494        | 1.2987 | 1100 | 0.6388          |
| 0.6623        | 1.3577 | 1150 | 0.6359          |
| 0.6552        | 1.4168 | 1200 | 0.6334          |
| 0.6465        | 1.4758 | 1250 | 0.6297          |
| 0.6495        | 1.5348 | 1300 | 0.6285          |
| 0.6521        | 1.5939 | 1350 | 0.6272          |
| 0.6505        | 1.6529 | 1400 | 0.6261          |
| 0.6773        | 1.7119 | 1450 | 0.6238          |
| 0.6487        | 1.7710 | 1500 | 0.6225          |
| 0.639         | 1.8300 | 1550 | 0.6208          |
| 0.6465        | 1.8890 | 1600 | 0.6194          |
| 0.6528        | 1.9481 | 1650 | 0.6182          |
| 0.6265        | 2.0071 | 1700 | 0.6164          |
| 0.6161        | 2.0661 | 1750 | 0.6137          |
| 0.6236        | 2.1251 | 1800 | 0.6118          |
| 0.6371        | 2.1842 | 1850 | 0.6111          |
| 0.6294        | 2.2432 | 1900 | 0.6093          |
| 0.6257        | 2.3022 | 1950 | 0.6087          |
| 0.6204        | 2.3613 | 2000 | 0.6081          |
| 0.6133        | 2.4203 | 2050 | 0.6073          |
| 0.6108        | 2.4793 | 2100 | 0.6068          |
| 0.622         | 2.5384 | 2150 | 0.6066          |
| 0.6233        | 2.5974 | 2200 | 0.6064          |
| 0.6183        | 2.6564 | 2250 | 0.6063          |
| 0.6237        | 2.7155 | 2300 | 0.6062          |
| 0.6388        | 2.7745 | 2350 | 0.6062          |
| 0.6236        | 2.8335 | 2400 | 0.6062          |
| 0.6236        | 2.8926 | 2450 | 0.6062          |
| 0.6205        | 2.9516 | 2500 | 0.6061          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1